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  • Computational Methods, Genomics  (140)
  • Biotechnology & Synthetic Biology  (41)
  • Oxford University Press  (181)
  • 1
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    A low-latency, big database system and browser for storage, querying and visualization of 3D genomic data (2015)
    Oxford University Press
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    Publication Date: 2015-09-19
    Description: Recent releases of genome three-dimensional (3D) structures have the potential to transform our understanding of genomes. Nonetheless, the storage technology and visualization tools need to evolve to offer to the scientific community fast and convenient access to these data. We introduce simultaneously a database system to store and query 3D genomic data ( 3DBG ), and a 3D genome browser to visualize and explore 3D genome structures ( 3DGB ). We benchmark 3DBG against state-of-the-art systems and demonstrate that it is faster than previous solutions, and importantly gracefully scales with the size of data. We also illustrate the usefulness of our 3D genome Web browser to explore human genome structures. The 3D genome browser is available at http://3dgb.cs.mcgill.ca/ .
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 2
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    Analysis of strand-specific RNA-seq data using machine learning reveals the structures of transcription units in Clostridium thermocellum (2015)
    Oxford University Press
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    Publication Date: 2015-05-29
    Description: Identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-specific RNA-seq (ssRNA-seq) datasets collected under two experimental conditions to derive the genomic TU organization of Clostridium thermocellum using a machine-learning approach. Our method accurately predicted the genomic boundaries of individual TUs based on two sets of parameters measuring the RNA-seq expression patterns across the genome: expression-level continuity and variance. A total of 2590 distinct TUs are predicted based on the four RNA-seq datasets. Among the predicted TUs, 44% have multiple genes. We assessed our prediction method on an independent set of RNA-seq data with longer reads. The evaluation confirmed the high quality of the predicted TUs. Functional enrichment analyses on a selected subset of the predicted TUs revealed interesting biology. To demonstrate the generality of the prediction method, we have also applied the method to RNA-seq data collected on Escherichia coli and achieved high prediction accuracies. The TU prediction program named SeqTU is publicly available at https://code.google.com/p/seqtu/ . We expect that the predicted TUs can serve as the baseline information for studying transcriptional and post-transcriptional regulation in C. thermocellum and other bacteria.
    Keywords: Computational Methods, Genomics
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  • 3
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    A thesaurus of genetic variation for interrogation of repetitive genomic regions (2015)
    Oxford University Press
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    Publication Date: 2015-05-29
    Description: Detecting genetic variation is one of the main applications of high-throughput sequencing, but is still challenging wherever aligning short reads poses ambiguities. Current state-of-the-art variant calling approaches avoid such regions, arguing that it is necessary to sacrifice detection sensitivity to limit false discovery. We developed a method that links candidate variant positions within repetitive genomic regions into clusters. The technique relies on a resource, a thesaurus of genetic variation, that enumerates genomic regions with similar sequence. The resource is computationally intensive to generate, but once compiled can be applied efficiently to annotate and prioritize variants in repetitive regions. We show that thesaurus annotation can reduce the rate of false variant calls due to mappability by up to three orders of magnitude. We apply the technique to whole genome datasets and establish that called variants in low mappability regions annotated using the thesaurus can be experimentally validated. We then extend the analysis to a large panel of exomes to show that the annotation technique opens possibilities to study variation in hereto hidden and under-studied parts of the genome.
    Keywords: Computational Methods, Genomics
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    Topics: Biology
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  • 4
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    A new molecular signature method for prediction of driver cancer pathways from transcriptional data (2016)
    Oxford University Press
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    Publication Date: 2016-06-21
    Description: Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways.
    Keywords: Computational Methods, Genomics
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  • 5
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    DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences (2016)
    Oxford University Press
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    Publication Date: 2016-06-21
    Description: Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory ‘grammar’ to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ .
    Keywords: Computational Methods, Genomics
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  • 6
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    Phylogeny-aware identification and correction of taxonomically mislabeled sequences (2016)
    Oxford University Press
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    Publication Date: 2016-06-21
    Description: Molecular sequences in public databases are mostly annotated by the submitting authors without further validation. This procedure can generate erroneous taxonomic sequence labels. Mislabeled sequences are hard to identify, and they can induce downstream errors because new sequences are typically annotated using existing ones. Furthermore, taxonomic mislabelings in reference sequence databases can bias metagenetic studies which rely on the taxonomy. Despite significant efforts to improve the quality of taxonomic annotations, the curation rate is low because of the labor-intensive manual curation process. Here, we present SATIVA, a phylogeny-aware method to automatically identify taxonomically mislabeled sequences (‘mislabels’) using statistical models of evolution. We use the Evolutionary Placement Algorithm (EPA) to detect and score sequences whose taxonomic annotation is not supported by the underlying phylogenetic signal, and automatically propose a corrected taxonomic classification for those. Using simulated data, we show that our method attains high accuracy for identification (96.9% sensitivity/91.7% precision) as well as correction (94.9% sensitivity/89.9% precision) of mislabels. Furthermore, an analysis of four widely used microbial 16S reference databases (Greengenes, LTP, RDP and SILVA) indicates that they currently contain between 0.2% and 2.5% mislabels. Finally, we use SATIVA to perform an in-depth evaluation of alternative taxonomies for Cyanobacteria. SATIVA is freely available at https://github.com/amkozlov/sativa .
    Keywords: Computational Methods, Genomics
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  • 7
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    iGEMS: an integrated model for identification of alternative exon usage events (2016)
    Oxford University Press
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    Publication Date: 2016-06-21
    Description: DNA microarrays and RNAseq are complementary methods for studying RNA molecules. Current computational methods to determine alternative exon usage (AEU) using such data require impractical visual inspection and still yield high false-positive rates. Integrated Gene and Exon Model of Splicing (iGEMS) adapts a gene-level residuals model with a gene size adjusted false discovery rate and exon-level analysis to circumvent these limitations. iGEMS was applied to two new DNA microarray datasets, including the high coverage Human Transcriptome Arrays 2.0 and performance was validated using RT-qPCR. First, AEU was studied in adipocytes treated with ( n = 9) or without ( n = 8) the anti-diabetes drug, rosiglitazone. iGEMS identified 555 genes with AEU, and robust verification by RT-qPCR (~90%). Second, in a three-way human tissue comparison (muscle, adipose and blood, n = 41) iGEMS identified 4421 genes with at least one AEU event, with excellent RT-qPCR verification (95%, n = 22). Importantly, iGEMS identified a variety of AEU events, including 3'UTR extension, as well as exon inclusion/exclusion impacting on protein kinase and extracellular matrix domains. In conclusion, iGEMS is a robust method for identification of AEU while the variety of exon usage between human tissues is 5–10 times more prevalent than reported by the Genotype-Tissue Expression consortium using RNA sequencing.
    Keywords: Computational Methods, Genomics
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  • 8
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    TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data (2016)
    Oxford University Press
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    Publication Date: 2016-05-06
    Description: The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers. However, mining the TCGA data presents several bioinformatics challenges, such as data retrieval and integration with clinical data and other molecular data types (e.g. RNA and DNA methylation). We developed an R/Bioconductor package called TCGAbiolinks to address these challenges and offer bioinformatics solutions by using a guided workflow to allow users to query, download and perform integrative analyses of TCGA data. We combined methods from computer science and statistics into the pipeline and incorporated methodologies developed in previous TCGA marker studies and in our own group. Using four different TCGA tumor types (Kidney, Brain, Breast and Colon) as examples, we provide case studies to illustrate examples of reproducibility, integrative analysis and utilization of different Bioconductor packages to advance and accelerate novel discoveries.
    Keywords: Computational Methods, Genomics
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  • 9
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    Robust detection of alternative splicing in a population of single cells (2016)
    Oxford University Press
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    Publication Date: 2016-05-06
    Description: Single cell RNA-seq experiments provide valuable insight into cellular heterogeneity but suffer from low coverage, 3' bias and technical noise. These unique properties of single cell RNA-seq data make study of alternative splicing difficult, and thus most single cell studies have restricted analysis of transcriptome variation to the gene level. To address these limitations, we developed SingleSplice, which uses a statistical model to detect genes whose isoform usage shows biological variation significantly exceeding technical noise in a population of single cells. Importantly, SingleSplice is tailored to the unique demands of single cell analysis, detecting isoform usage differences without attempting to infer expression levels for full-length transcripts. Using data from spike-in transcripts, we found that our approach detects variation in isoform usage among single cells with high sensitivity and specificity. We also applied SingleSplice to data from mouse embryonic stem cells and discovered a set of genes that show significant biological variation in isoform usage across the set of cells. A subset of these isoform differences are linked to cell cycle stage, suggesting a novel connection between alternative splicing and the cell cycle.
    Keywords: Computational Methods, Genomics
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  • 10
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    Endophytic fungi associated with Sudanese medicinal plants show cytotoxic and antibiotic potential (2016)
    Oxford University Press
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    Publication Date: 2016-05-12
    Description: In this study, we isolated 15 endophytic fungi from five Sudanese medicinal plants. Each fungal endophytic strain was identified by sequencing of internal transcribed spacer (ITS) regions of rDNA. Ethyl acetate extracts were prepared from each endophyte cultivated in vitro and tested for their respective antibacterial activities and antiproliferative activities against human cancer cells. Antibacterial screening was carried out against two bacterial strains: Gram-negative Escherichia coli and Gram-positive methicillin-resistant Staphylococcus aureus , by the broth dilution method. Cell viability was evaluated by the MTT procedure after exposure of MCF7 breast cancer cells and HT29 or HCT116 human colon adenocarcinoma cells to each endophytic extract. Of interest, Byssochlamys spectabilis isolated from Euphorbia prostata showed cytotoxicity (IC 50 = 1.51 ± 0.2 μg mL –1 ) against MCF7 cells, but had a low effect against HT29 or HCT116 cells (IC 50 〉 20 μg mL –1 ). Cladosporium cladosporioides 2, isolated from Vernonia amygdalina leaves, showed antiproliferative activities against MCF7 cells (IC 50 = 10.5 ± 1.5 μg mL –1 ) only. On the other hand, B. spectabilis and Alternaria sp. extract had antibacterial activities against the S. aureus strain. The findings of this work revealed that endophytic fungi associated with medicinal plants from Sudan could be considered as an attractive source of new therapeutic compounds.
    Keywords: Biotechnology & Synthetic Biology
    Print ISSN: 0378-1097
    Electronic ISSN: 1574-6968
    Topics: Biology
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  • 11
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    CARGO: effective format-free compressed storage of genomic information (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: The recent super-exponential growth in the amount of sequencing data generated worldwide has put techniques for compressed storage into the focus. Most available solutions, however, are strictly tied to specific bioinformatics formats, sometimes inheriting from them suboptimal design choices; this hinders flexible and effective data sharing. Here, we present CARGO (Compressed ARchiving for GenOmics), a high-level framework to automatically generate software systems optimized for the compressed storage of arbitrary types of large genomic data collections. Straightforward applications of our approach to FASTQ and SAM archives require a few lines of code, produce solutions that match and sometimes outperform specialized format-tailored compressors and scale well to multi-TB datasets. All CARGO software components can be freely downloaded for academic and non-commercial use from http://bio-cargo.sourceforge.net .
    Keywords: Computational Methods, Genomics
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  • 12
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    InPhaDel: integrative shotgun and proximity-ligation sequencing to phase deletions with single nucleotide polymorphisms (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: Phasing of single nucleotide (SNV), and structural variations into chromosome-wide haplotypes in humans has been challenging, and required either trio sequencing or restricting phasing to population-based haplotypes. Selvaraj et al . demonstrated single individual SNV phasing is possible with proximity ligated (HiC) sequencing. Here, we demonstrate HiC can phase structural variants into phased scaffolds of SNVs. Since HiC data is noisy, and SV calling is challenging, we applied a range of supervised classification techniques, including Support Vector Machines and Random Forest, to phase deletions. Our approach was demonstrated on deletion calls and phasings on the NA12878 human genome. We used three NA12878 chromosomes and simulated chromosomes to train model parameters. The remaining NA12878 chromosomes withheld from training were used to evaluate phasing accuracy. Random Forest had the highest accuracy and correctly phased 86% of the deletions with allele-specific read evidence. Allele-specific read evidence was found for 76% of the deletions. HiC provides significant read evidence for accurately phasing 33% of the deletions. Also, eight of eight top ranked deletions phased by only HiC were validated using long range polymerase chain reaction and Sanger. Thus, deletions from a single individual can be accurately phased using a combination of shotgun and proximity ligation sequencing. InPhaDel software is available at: http://l337x911.github.io/inphadel/.
    Keywords: Computational Methods, Genomics
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  • 13
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    Redundans: an assembly pipeline for highly heterozygous genomes (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: Many genomes display high levels of heterozygosity (i.e. presence of different alleles at the same loci in homologous chromosomes), being those of hybrid organisms an extreme such case. The assembly of highly heterozygous genomes from short sequencing reads is a challenging task because it is difficult to accurately recover the different haplotypes. When confronted with highly heterozygous genomes, the standard assembly process tends to collapse homozygous regions and reports heterozygous regions in alternative contigs. The boundaries between homozygous and heterozygous regions result in multiple assembly paths that are hard to resolve, which leads to highly fragmented assemblies with a total size larger than expected. This, in turn, causes numerous problems in downstream analyses such as fragmented gene models, wrong gene copy number, or broken synteny. To circumvent these caveats we have developed a pipeline that specifically deals with the assembly of heterozygous genomes by introducing a step to recognise and selectively remove alternative heterozygous contigs. We tested our pipeline on simulated and naturally-occurring heterozygous genomes and compared its accuracy to other existing tools. Our method is freely available at https://github.com/Gabaldonlab/redundans .
    Keywords: Computational Methods, Genomics
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  • 14
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    Graph-based modeling of tandem repeats improves global multiple sequence alignment (2013)
    Oxford University Press
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    Publication Date: 2013-09-26
    Description: Tandem repeats (TRs) are often present in proteins with crucial functions, responsible for resistance, pathogenicity and associated with infectious or neurodegenerative diseases. This motivates numerous studies of TRs and their evolution, requiring accurate multiple sequence alignment. TRs may be lost or inserted at any position of a TR region by replication slippage or recombination, but current methods assume fixed unit boundaries, and yet are of high complexity. We present a new global graph-based alignment method that does not restrict TR unit indels by unit boundaries. TR indels are modeled separately and penalized using the phylogeny-aware alignment algorithm. This ensures enhanced accuracy of reconstructed alignments, disentangling TRs and measuring indel events and rates in a biologically meaningful way. Our method detects not only duplication events but also all changes in TR regions owing to recombination, strand slippage and other events inserting or deleting TR units. We evaluate our method by simulation incorporating TR evolution, by either sampling TRs from a profile hidden Markov model or by mimicking strand slippage with duplications. The new method is illustrated on a family of type III effectors, a pathogenicity determinant in agriculturally important bacteria Ralstonia solanacearum. We show that TR indel rate variation contributes to the diversification of this protein family.
    Keywords: Computational Methods, Genomics
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  • 15
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    Meander: visually exploring the structural variome using space-filling curves (2013)
    Oxford University Press
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    Publication Date: 2013-06-08
    Description: The introduction of next generation sequencing methods in genome studies has made it possible to shift research from a gene-centric approach to a genome wide view. Although methods and tools to detect single nucleotide polymorphisms are becoming more mature, methods to identify and visualize structural variation (SV) are still in their infancy. Most genome browsers can only compare a given sequence to a reference genome; therefore, direct comparison of multiple individuals still remains a challenge. Therefore, the implementation of efficient approaches to explore and visualize SVs and directly compare two or more individuals is desirable. In this article, we present a visualization approach that uses space-filling Hilbert curves to explore SVs based on both read-depth and pair-end information. An interactive open-source Java application, called Meander , implements the proposed methodology, and its functionality is demonstrated using two cases. With Meander , users can explore variations at different levels of resolution and simultaneously compare up to four different individuals against a common reference. The application was developed using Java version 1.6 and Processing.org and can be run on any platform. It can be found at http://homes.esat.kuleuven.be/~bioiuser/meander .
    Keywords: Computational Methods, Genomics
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  • 16
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    Following the footprints of polymorphic inversions on SNP data: from detection to association tests (2015)
    Oxford University Press
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    Publication Date: 2015-05-03
    Description: Inversion polymorphisms have important phenotypic and evolutionary consequences in humans. Two different methodologies have been used to infer inversions from SNP dense data, enabling the use of large cohorts for their study. One approach relies on the differences in linkage disequilibrium across breakpoints; the other one captures the internal haplotype groups that tag the inversion status of chromosomes. In this article, we assessed the convergence of the two methods in the detection of 20 human inversions that have been reported in the literature. The methods converged in four inversions including inv-8p23, for which we studied its association with low-BMI in American children. Using a novel haplotype tagging method with control on inversion ancestry, we computed the frequency of inv-8p23 in two American cohorts and observed inversion haplotype admixture. Accounting for haplotype ancestry, we found that the European inverted allele in children carries a recessive risk of underweight, validated in an independent Spanish cohort (combined: OR= 2.00, P = 0.001). While the footprints of inversions on SNP data are complex, we show that systematic analyses, such as convergence of different methods and controlling for ancestry, can reveal the contribution of inversions to the ancestral composition of populations and to the heritability of human disease.
    Keywords: Computational Methods, Genomics
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    Reconstructing genome-scale metabolic models with merlin (2015)
    Oxford University Press
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    Publication Date: 2015-05-03
    Description: The Metabolic Models Reconstruction Using Genome-Scale Information ( merlin ) tool is a user-friendly Java application that aids the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced. It performs the major steps of the reconstruction process, including the functional genomic annotation of the whole genome and subsequent construction of the portfolio of reactions. Moreover, merlin includes tools for the identification and annotation of genes encoding transport proteins, generating the transport reactions for those carriers. It also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome and thus the localisation of the metabolites involved in the reactions promoted by such enzymes. The gene-proteins-reactions (GPR) associations are automatically generated and included in the model. Finally, merlin expedites the transition from genomic data to draft metabolic models reconstructions exported in the SBML standard format, allowing the user to have a preliminary view of the biochemical network, which can be manually curated within the environment provided by merlin .
    Keywords: Computational Methods, Genomics
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    Inferential modeling of 3D chromatin structure (2015)
    Oxford University Press
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    Publication Date: 2015-05-03
    Description: For eukaryotic cells, the biological processes involving regulatory DNA elements play an important role in cell cycle. Understanding 3D spatial arrangements of chromosomes and revealing long-range chromatin interactions are critical to decipher these biological processes. In recent years, chromosome conformation capture (3C) related techniques have been developed to measure the interaction frequencies between long-range genome loci, which have provided a great opportunity to decode the 3D organization of the genome. In this paper, we develop a new Bayesian framework to derive the 3D architecture of a chromosome from 3C-based data. By modeling each chromosome as a polymer chain, we define the conformational energy based on our current knowledge on polymer physics and use it as prior information in the Bayesian framework. We also propose an expectation-maximization (EM) based algorithm to estimate the unknown parameters of the Bayesian model and infer an ensemble of chromatin structures based on interaction frequency data. We have validated our Bayesian inference approach through cross-validation and verified the computed chromatin conformations using the geometric constraints derived from fluorescence in situ hybridization (FISH) experiments. We have further confirmed the inferred chromatin structures using the known genetic interactions derived from other studies in the literature. Our test results have indicated that our Bayesian framework can compute an accurate ensemble of 3D chromatin conformations that best interpret the distance constraints derived from 3C-based data and also agree with other sources of geometric constraints derived from experimental evidence in the previous studies. The source code of our approach can be found in https://github.com/wangsy11/InfMod3DGen .
    Keywords: Computational Methods, Genomics
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    Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism (2015)
    Oxford University Press
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    Publication Date: 2015-05-03
    Description: Characterization of cell type specific regulatory networks and elements is a major challenge in genomics, and emerging strategies frequently employ high-throughput genome-wide assays of transcription factor (TF) to DNA binding, histone modifications or chromatin state. However, these experiments remain too difficult/expensive for many laboratories to apply comprehensively to their system of interest. Here, we explore the potential of elucidating regulatory systems in varied cell types using computational techniques that rely on only data of gene expression, low-resolution chromatin accessibility, and TF–DNA binding specificities (‘motifs’). We show that static computational motif scans overlaid with chromatin accessibility data reasonably approximate experimentally measured TF–DNA binding. We demonstrate that predicted binding profiles and expression patterns of hundreds of TFs are sufficient to identify major regulators of ~200 spatiotemporal expression domains in the Drosophila embryo. We are then able to learn reliable statistical models of enhancer activity for over 70 expression domains and apply those models to annotate domain specific enhancers genome-wide. Throughout this work, we apply our motif and accessibility based approach to comprehensively characterize the regulatory network of fruitfly embryonic development and show that the accuracy of our computational method compares favorably to approaches that rely on data from many experimental assays.
    Keywords: Computational Methods, Genomics
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    FOAM (Functional Ontology Assignments for Metagenomes): a Hidden Markov Model (HMM) database with environmental focus (2014)
    Oxford University Press
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    Publication Date: 2014-11-07
    Description: A new functional gene database, FOAM (Functional Ontology Assignments for Metagenomes), was developed to screen environmental metagenomic sequence datasets. FOAM provides a new functional ontology dedicated to classify gene functions relevant to environmental microorganisms based on Hidden Markov Models (HMMs). Sets of aligned protein sequences (i.e. ‘profiles’) were tailored to a large group of target KEGG Orthologs (KOs) from which HMMs were trained. The alignments were checked and curated to make them specific to the targeted KO. Within this process, sequence profiles were enriched with the most abundant sequences available to maximize the yield of accurate classifier models. An associated functional ontology was built to describe the functional groups and hierarchy. FOAM allows the user to select the target search space before HMM-based comparison steps and to easily organize the results into different functional categories and subcategories. FOAM is publicly available at http://portal.nersc.gov/project/m1317/FOAM/ .
    Keywords: Computational Methods, Genomics
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  • 21
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    De novo biosynthesis of resveratrol by site-specific integration of heterologous genes in Escherichia coli (2016)
    Oxford University Press
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    Publication Date: 2016-04-08
    Description: Resveratrol is a well-known triphenolic natural product present in red wine. For its contribution to human health, the demand for resveratrol as a food and nutrition supplement has increased significantly. In recent years, the rapid development of synthetic biology has promoted extensive work to increase the production of resveratrol in microbes. However, supplementation of expensive phenylpropanoic precursors was required in current engineered strains. Here, we first utilized the site-specific integration strategy to produce resveratrol in Escherichia coli . The genes tal , 4cl and sts were site-specific integrated into the loci of genes tyrR and trpED in the chromosome of E. coli BW25113 (DE3). The final strain was capable of producing 4.612 mg L –1 of resveratrol from glucose.
    Keywords: Biotechnology & Synthetic Biology
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    Overexpression of two stress-responsive, small, non-coding RNAs, 6S and tmRNA, imparts butanol tolerance in Clostridium acetobutylicum (2016)
    Oxford University Press
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    Publication Date: 2016-04-08
    Description: While extensively studied in several model organisms, the role of small, non-coding RNAs in the stress response remains largely unexplored in Clostridium organisms. About 100 years after the first industrial Acetone–Butanol–Ethanol fermentation process, based on the Weizmann Clostridium acetobutylicum strain, strain tolerance to butanol remains a crucial factor limiting the economics of the process. Several studies have examined the response of this organism to metabolite stress, and several genes have been engaged to impart enhanced tolerance, but no sRNAs have yet been directly engaged in this task. We show that the two stress-responsive sRNAs, 6S and tmRNA, upon overexpression impart tolerance to butanol as assessed by viability assays under process-relevant conditions. 6S overexpression enhances cell densities as well as butanol titres. We discuss the likely mechanisms that these two sRNAs might engage in this tolerance phenotype. Our data support the continued exploration of sRNAs as a basis for engineering enhanced tolerance and enhanced solvent production, especially because sRNA-based strategies impose a minimal metabolic burden on the cells.
    Keywords: Biotechnology & Synthetic Biology
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    A statistical framework for revealing signaling pathways perturbed by DNA variants (2015)
    Oxford University Press
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    Publication Date: 2015-06-24
    Description: Much of the inter-individual variation in gene expression is triggered via perturbations of signaling networks by DNA variants. We present a novel probabilistic approach for identifying the particular pathways by which DNA variants perturb the signaling network. Our procedure, called PINE, relies on a systematic integration of established biological knowledge of signaling networks with data on transcriptional responses to various experimental conditions. Unlike previous approaches, PINE provides statistical aspects that are critical for prioritizing hypotheses for followup experiments. Using simulated data, we show that higher accuracy is attained with PINE than with existing methods. We used PINE to analyze transcriptional responses of immune dendritic cells to several pathogenic stimulations. PINE identified statistically significant genetic perturbations in the pathogen-sensing signaling network, suggesting previously uncharacterized regulatory mechanisms for functional DNA variants.
    Keywords: Computational Methods, Genomics
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    Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses (2015)
    Oxford University Press
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    Publication Date: 2015-08-29
    Description: Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean–variance relationship of the log-counts-per-million using ‘voom’. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source ‘limma’ package.
    Keywords: Computational Methods, Genomics
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    RAX2: a genome-wide detection method of condition-associated transcription variation (2015)
    Oxford University Press
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    Publication Date: 2015-08-29
    Description: Most mammalian genes have mRNA variants due to alternative promoter usage, alternative splicing, and alternative cleavage and polyadenylation. Expression of alternative RNA isoforms has been found to be associated with tumorigenesis, proliferation and differentiation. Detection of condition-associated transcription variation requires association methods. Traditional association methods such as Pearson chi-square test and Fisher Exact test are single test methods and do not work on count data with replicates. Although the Cochran Mantel Haenszel (CMH) approach can handle replicated count data, our simulations showed that multiple CMH tests still had very low power. To identify condition-associated variation of transcription, we here proposed a ranking analysis of chi-squares (RAX2) for large-scale association analysis. RAX2 is a nonparametric method and has accurate and conservative estimation of FDR profile. Simulations demonstrated that RAX2 performs well in finding condition-associated transcription variants. We applied RAX2 to primary T-cell transcriptomic data and identified 1610 (16.3%) tags associated in transcription with immune stimulation at FDR 〈 0.05. Most of these tags also had differential expression. Analysis of two and three tags within genes revealed that under immune stimulation short RNA isoforms were preferably used.
    Keywords: Computational Methods, Genomics
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    Neofiscalin A and fiscalin C are potential novel indole alkaloid alternatives for the treatment of multidrug-resistant Gram-positive bacterial infections (2016)
    Oxford University Press
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    Publication Date: 2016-07-02
    Description: Ten indole alkaloids were obtained from the marine sponge-associated fungus Neosartorya siamensis KUFA 0017. We studied the antimicrobial properties of these and of three other compounds previously isolated from the soil fungus N. siamensis KUFC 6349. Only neofiscalin A showed antimicrobial activity against Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecalis (VRE); with a minimum inhibitory concentration (MIC) of 8 μg mL –1 against both strains. Another compound, fiscalin C, presented synergistic activity against MRSA when combined with oxacillin, although alone showed no antibacterial effect. Moreover, neofiscalin A, when present at sub-MICs, hampered the ability of both MRSA and VRE strains to form a biofilm. Additionally, the biofilm inhibitory concentration values of neofiscalin A against the MRSA and VRE isolates were 96 and 80 μg mL –1 , respectively. At a concentration of 200 μg mL –1 , neofiscalin A was able to reduce the metabolic activity of the biofilms by ~50%. One important fact is that our results also showed that neofiscalin A had no cytotoxicity against a human brain capillary endothelial cell line.
    Keywords: Biotechnology & Synthetic Biology
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    The unconventional secretion of PepN is independent of a functional autophagy machinery in the filamentous fungus Aspergillus niger (2016)
    Oxford University Press
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    Publication Date: 2016-07-02
    Description: During unconventional protein secretion (UPS), proteins do not pass through the classical endoplasmic reticulum (ER)–Golgi-dependent pathway, but are transported to the cell membrane via alternative routes. One type of UPS is dependent on several autophagy-related (Atg) proteins in yeast and mammalian cells, but mechanisms for unconventional secretion are largely unknown for filamentous fungi. In this study, we investigated whether the autophagy machinery is used for UPS in the filamentous fungus Aspergillus niger . An aspartic protease, which we called PepN, was identified as being likely to be secreted unconventionally, as this protein is highly abundant in culture filtrates during carbon starvation while it lacks a conventional N-terminal secretion sequence. We analysed the presence of PepN in the culture filtrates of carbon starved wild-type, atg1 and atg8 deletion mutant strains by Western blot analysis and by secretome analysis using nanoLC-ESI-MS/MS (wild-type and atg8 deletion mutant). Besides the presence of carbohydrate-active enzymes and other types of proteases, PepN was abundantly found in culture filtrates of both wild-type and atg deletion strains, indicating that the secretion of PepN is independent of the autophagy machinery in A. niger and hence most likely occurs via a different mechanism.
    Keywords: Biotechnology & Synthetic Biology
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    Effects of MreB paralogs on poly-{gamma}-glutamic acid synthesis and cell morphology in Bacillus amyloliquefaciens (2016)
    Oxford University Press
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    Publication Date: 2016-08-27
    Description: Actin-like MreB paralogs play important roles in cell shape maintenance, cell wall synthesis and the regulation of the D,L-endopeptidases, CwlO and LytE. The gram-positive bacteria, Bacillus amyloliquefaciens LL3, is a poly--glutamic acid (-PGA) producing strain that contains three MreB paralogs: MreB, Mbl and MreBH. In B. amyloliquefaciens , CwlO and LytE can degrade -PGA. In this study, we aimed to test the hypothesis that modulating transcript levels of MreB paralogs would alter the synthesis and degradation of -PGA. The results showed that overexpression or inhibition of MreB, Mbl or MreBH had distinct effects on cell morphology and the molecular weight of the -PGA products. In fermentation medium, cells of mreB inhibition mutant were 50.2% longer than LL3, and the -PGA titer increased by 55.7%. However, changing the expression level of mbl showed only slight effects on the morphology, -PGA molecular weight and titer. In the mreBH inhibition mutant, -PGA production and its molecular weight increased by 56.7% and 19.4%, respectively. These results confirmed our hypothesis that suppressing the expression of MreB paralogs might reduce -PGA degradation, and that improving the cell size could strengthen -PGA synthesis. This is the first report of enhanced -PGA production via suppression of actin-like MreB paralogs.
    Keywords: Biotechnology & Synthetic Biology
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    Unique transposon landscapes are pervasive across Drosophila melanogaster genomes (2015)
    Oxford University Press
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    Publication Date: 2015-12-16
    Description: To understand how transposon landscapes (TLs) vary across animal genomes, we describe a new method called the Transposon Insertion and Depletion AnaLyzer (TIDAL) and a database of 〉300 TLs in Drosophila melanogaster (TIDAL-Fly). Our analysis reveals pervasive TL diversity across cell lines and fly strains, even for identically named sub-strains from different laboratories such as the ISO1 strain used for the reference genome sequence. On average, 〉500 novel insertions exist in every lab strain, inbred strains of the Drosophila Genetic Reference Panel (DGRP), and fly isolates in the Drosophila Genome Nexus (DGN). A minority (〈25%) of transposon families comprise the majority (〉70%) of TL diversity across fly strains. A sharp contrast between insertion and depletion patterns indicates that many transposons are unique to the ISO1 reference genome sequence. Although TL diversity from fly strains reaches asymptotic limits with increasing sequencing depth, rampant TL diversity causes unsaturated detection of TLs in pools of flies. Finally, we show novel transposon insertions negatively correlate with Piwi-interacting RNA (piRNA) levels for most transposon families, except for the highly-abundant roo retrotransposon. Our study provides a useful resource for Drosophila geneticists to understand how transposons create extensive genomic diversity in fly cell lines and strains.
    Keywords: Computational Methods, Genomics
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    Industrial production of acetone and butanol by fermentation--100 years later (2016)
    Oxford University Press
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    Publication Date: 2016-06-08
    Description: Microbial production of acetone and butanol was one of the first large-scale industrial fermentation processes of global importance. During the first part of the 20th century, it was indeed the second largest fermentation process, superseded in importance only by the ethanol fermentation. After a rapid decline after the 1950s, acetone-butanol-ethanol (ABE) fermentation has recently gained renewed interest in the context of biorefinery approaches for the production of fuels and chemicals from renewable resources. The availability of new methods and knowledge opens many new doors for industrial microbiology, and a comprehensive view on this process is worthwhile due to the new interest. This thematic issue of FEMS Microbiology Letters, dedicated to the 100th anniversary of the first industrial exploitation of Chaim Weizmann's ABE fermentation process, covers the main aspects of old and new developments, thereby outlining a model development in biotechnology. All major aspects of industrial microbiology are exemplified by this single process. This includes new technologies, such as the latest developments in metabolic engineering, the exploitation of biodiversity and discoveries of new regulatory systems such as for microbial stress tolerance, as well as technological aspects, such as bio- and down-stream processing.
    Keywords: Biotechnology & Synthetic Biology
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    A new lactobacilli in vivo expression system for the production and delivery of heterologous proteins at mucosal surfaces (2016)
    Oxford University Press
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    Publication Date: 2016-06-02
    Description: Food-grade lactic acid bacteria, such as lactobacilli, represent good candidates for the development of mucosal vectors. Indeed, they are generally recognized as safe microorganisms and some strains display beneficial effects (probiotics). In this study, we described a new lactobacilli in vivo expression (LIVE) system for the production and delivery of therapeutic molecules at mucosal surfaces. The versatility and functionality of this system was successfully validated in several lactobacilli species; furthermore, we assessed in vivo LIVE system in two different mouse models of human pathologies: (i) a model of therapy against intestinal inflammation (inflammatory bowel diseases) and (ii) a model of vaccination against dental caries. We demonstrated that Lactobacillus gasseri expressing the anti-inflammatory cytokine IL-10 under LIVE system efficiently delivered the recombinant protein at mucosal surfaces and display anti-inflammatory effects. In the vaccination model against caries, LIVE system allowed the heterologous expression of Streptococcus mutans antigen GbpB by L. gasseri , leading to a stimulation of the host immune response.
    Keywords: Biotechnology & Synthetic Biology
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    Bioreactors and in situ product recovery techniques for acetone-butanol-ethanol fermentation (2016)
    Oxford University Press
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    Publication Date: 2016-06-02
    Description: The microbial fermentation process is one of the sustainable and environment-friendly ways to produce 1-butanol and other bio-based chemicals. The success of the fermentation process greatly relies on the choice of bioreactors and the separation methods. In this review, the history and the performance of bioreactors for the acetone–butanol–ethanol (ABE) fermentation is discussed. The subject is then focused on in situ product recovery (ISPR) techniques, particularly for the integrated extraction-gas stripping. The usefulness of this promising hybrid ISPR device is acknowledged by its incorporation with batch, fed-batch and continuous processes to improve the performance of ABE fermentation.
    Keywords: Biotechnology & Synthetic Biology
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    Mixed Integer Linear Programming based machine learning approach identifies regulators of telomerase in yeast (2016)
    Oxford University Press
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    Publication Date: 2016-06-03
    Description: Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.
    Keywords: Computational Methods, Genomics
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    f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome (2016)
    Oxford University Press
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    Publication Date: 2016-06-03
    Description: The ability to integrate ‘omics’ (i.e. transcriptomics and proteomics) is becoming increasingly important to the understanding of regulatory mechanisms. There are currently no tools available to identify differentially expressed genes (DEGs) across different ‘omics’ data types or multi-dimensional data including time courses. We present fCI (f-divergence Cut-out Index), a model capable of simultaneously identifying DEGs from continuous and discrete transcriptomic, proteomic and integrated proteogenomic data. We show that fCI can be used across multiple diverse sets of data and can unambiguously find genes that show functional modulation, developmental changes or misregulation. Applying fCI to several proteogenomics datasets, we identified a number of important genes that showed distinctive regulation patterns. The package fCI is available at R Bioconductor and http://software.steenlab.org/fCI/ .
    Keywords: Computational Methods, Genomics
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    CLASS2: accurate and efficient splice variant annotation from RNA-seq reads (2016)
    Oxford University Press
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    Publication Date: 2016-06-03
    Description: Next generation sequencing of cellular RNA is making it possible to characterize genes and alternative splicing in unprecedented detail. However, designing bioinformatics tools to accurately capture splicing variation has proven difficult. Current programs can find major isoforms of a gene but miss lower abundance variants, or are sensitive but imprecise. CLASS2 is a novel open source tool for accurate genome-guided transcriptome assembly from RNA-seq reads based on the model of splice graph. An extension of our program CLASS, CLASS2 jointly optimizes read patterns and the number of supporting reads to score and prioritize transcripts, implemented in a novel, scalable and efficient dynamic programming algorithm. When compared against reference programs, CLASS2 had the best overall accuracy and could detect up to twice as many splicing events with precision similar to the best reference program. Notably, it was the only tool to produce consistently reliable transcript models for a wide range of applications and sequencing strategies, including ribosomal RNA-depleted samples. Lightweight and multi-threaded, CLASS2 requires 〈3GB RAM and can analyze a 350 million read set within hours, and can be widely applied to transcriptomics studies ranging from clinical RNA sequencing, to alternative splicing analyses, and to the annotation of new genomes.
    Keywords: Computational Methods, Genomics
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    FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing (2016)
    Oxford University Press
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    Publication Date: 2016-09-20
    Description: Allele-specific copy number analysis (ASCN) from next generation sequencing (NGS) data can greatly extend the utility of NGS beyond the identification of mutations to precisely annotate the genome for the detection of homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH), allele-specific gains/amplifications. In addition, as targeted gene panels are increasingly used in clinical sequencing studies for the detection of ‘actionable’ mutations and copy number alterations to guide treatment decisions, accurate, tumor purity-, ploidy- and clonal heterogeneity-adjusted integer copy number calls are greatly needed to more reliably interpret NGS-based cancer gene copy number data in the context of clinical sequencing. We developed FACETS, an ASCN tool and open-source software with a broad application to whole genome, whole-exome, as well as targeted panel sequencing platforms. It is a fully integrated stand-alone pipeline that includes sequencing BAM file post-processing, joint segmentation of total- and allele-specific read counts, and integer copy number calls corrected for tumor purity, ploidy and clonal heterogeneity, with comprehensive output and integrated visualization. We demonstrate the application of FACETS using The Cancer Genome Atlas (TCGA) whole-exome sequencing of lung adenocarcinoma samples. We also demonstrate its application to a clinical sequencing platform based on a targeted gene panel.
    Keywords: Computational Methods, Genomics
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    A genome-wide approach for detecting novel insertion-deletion variants of mid-range size (2016)
    Oxford University Press
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    Publication Date: 2016-09-03
    Description: We present SWAN, a statistical framework for robust detection of genomic structural variants in next-generation sequencing data and an analysis of mid-range size insertion and deletions (〈10 Kb) for whole genome analysis and DNA mixtures. To identify these mid-range size events, SWAN collectively uses information from read-pair, read-depth and one end mapped reads through statistical likelihoods based on Poisson field models. SWAN also uses soft-clip/split read remapping to supplement the likelihood analysis and determine variant boundaries. The accuracy of SWAN is demonstrated by in silico spike-ins and by identification of known variants in the NA12878 genome. We used SWAN to identify a series of novel set of mid-range insertion/deletion detection that were confirmed by targeted deep re-sequencing. An R package implementation of SWAN is open source and freely available.
    Keywords: Computational Methods, Genomics
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    Comprehensive analysis of high-throughput screens with HiTSeekR (2016)
    Oxford University Press
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    Publication Date: 2016-08-20
    Description: High-throughput screening (HTS) is an indispensable tool for drug (target) discovery that currently lacks user-friendly software tools for the robust identification of putative hits from HTS experiments and for the interpretation of these findings in the context of systems biology. We developed HiTSeekR as a one-stop solution for chemical compound screens, siRNA knock-down and CRISPR/Cas9 knock-out screens, as well as microRNA inhibitor and -mimics screens. We chose three use cases that demonstrate the potential of HiTSeekR to fully exploit HTS screening data in quite heterogeneous contexts to generate novel hypotheses for follow-up experiments: (i) a genome-wide RNAi screen to uncover modulators of TNFα, (ii) a combined siRNA and miRNA mimics screen on vorinostat resistance and (iii) a small compound screen on KRAS synthetic lethality. HiTSeekR is publicly available at http://hitseekr.compbio.sdu.dk . It is the first approach to close the gap between raw data processing, network enrichment and wet lab target generation for various HTS screen types.
    Keywords: Computational Methods, Genomics
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    An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data (2015)
    Oxford University Press
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    Publication Date: 2015-04-21
    Description: Next-generation sequencing (NGS) approaches rapidly produce millions to billions of short reads, which allow pathogen detection and discovery in human clinical, animal and environmental samples. A major limitation of sequence homology-based identification for highly divergent microorganisms is the short length of reads generated by most highly parallel sequencing technologies. Short reads require a high level of sequence similarities to annotated genes to confidently predict gene function or homology. Such recognition of highly divergent homologues can be improved by reference-free ( de novo ) assembly of short overlapping sequence reads into larger contigs. We describe an ensemble strategy that integrates the sequential use of various de Bruijn graph and overlap-layout-consensus assemblers with a novel partitioned sub-assembly approach. We also proposed new quality metrics that are suitable for evaluating metagenome de novo assembly. We demonstrate that this new ensemble strategy tested using in silico spike-in, clinical and environmental NGS datasets achieved significantly better contigs than current approaches.
    Keywords: Computational Methods, Genomics
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    Distinguishing between productive and abortive promoters using a random forest classifier in Mycoplasma pneumoniae (2015)
    Oxford University Press
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    Publication Date: 2015-04-21
    Description: Distinguishing between promoter-like sequences in bacteria that belong to true or abortive promoters, or to those that do not initiate transcription at all, is one of the important challenges in transcriptomics. To address this problem, we have studied the genome-reduced bacterium Mycoplasma pneumoniae , for which the RNAs associated with transcriptional start sites have been recently experimentally identified. We determined the contribution to transcription events of different genomic features: the –10, extended –10 and –35 boxes, the UP element, the bases surrounding the –10 box and the nearest-neighbor free energy of the promoter region. Using a random forest classifier and the aforementioned features transformed into scores, we could distinguish between true, abortive promoters and non-promoters with good –10 box sequences. The methods used in this characterization of promoters can be extended to other bacteria and have important applications for promoter design in bacterial genome engineering.
    Keywords: Computational Methods, Genomics
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    Pathway analysis from lists of microRNAs: common pitfalls and alternative strategy (2015)
    Oxford University Press
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    Publication Date: 2015-04-21
    Description: MicroRNAs (miRNAs) are involved in the regulation of gene expression at a post-transcriptional level. As such, monitoring miRNA expression has been increasingly used to assess their role in regulatory mechanisms of biological processes. In large scale studies, once miRNAs of interest have been identified, the target genes they regulate are often inferred using algorithms or databases. A pathway analysis is then often performed in order to generate hypotheses about the relevant biological functions controlled by the miRNA signature. Here we show that the method widely used in scientific literature to identify these pathways is biased and leads to inaccurate results. In addition to describing the bias and its origin we present an alternative strategy to identify potential biological functions specifically impacted by a miRNA signature. More generally, our study exemplifies the crucial need of relevant negative controls when developing, and using, bioinformatics methods.
    Keywords: Computational Methods, Genomics
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    svaseq: removing batch effects and other unwanted noise from sequencing data (2014)
    Oxford University Press
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    Publication Date: 2014-11-28
    Description: It is now known that unwanted noise and unmodeled artifacts such as batch effects can dramatically reduce the accuracy of statistical inference in genomic experiments. These sources of noise must be modeled and removed to accurately measure biological variability and to obtain correct statistical inference when performing high-throughput genomic analysis. We introduced surrogate variable analysis (sva) for estimating these artifacts by (i) identifying the part of the genomic data only affected by artifacts and (ii) estimating the artifacts with principal components or singular vectors of the subset of the data matrix. The resulting estimates of artifacts can be used in subsequent analyses as adjustment factors to correct analyses. Here I describe a version of the sva approach specifically created for count data or FPKMs from sequencing experiments based on appropriate data transformation. I also describe the addition of supervised sva (ssva) for using control probes to identify the part of the genomic data only affected by artifacts. I present a comparison between these versions of sva and other methods for batch effect estimation on simulated data, real count-based data and FPKM-based data. These updates are available through the sva Bioconductor package and I have made fully reproducible analysis using these methods available from: https://github.com/jtleek/svaseq .
    Keywords: Computational Methods, Genomics
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    Insyght: navigating amongst abundant homologues, syntenies and gene functional annotations in bacteria, it's that symbol! (2014)
    Oxford University Press
    Details
    Publication Date: 2014-11-28
    Description: High-throughput techniques have considerably increased the potential of comparative genomics whilst simultaneously posing many new challenges. One of those challenges involves efficiently mining the large amount of data produced and exploring the landscape of both conserved and idiosyncratic genomic regions across multiple genomes. Domains of application of these analyses are diverse: identification of evolutionary events, inference of gene functions, detection of niche-specific genes or phylogenetic profiling. Insyght is a comparative genomic visualization tool that combines three complementary displays: (i) a table for thoroughly browsing amongst homologues, (ii) a comparator of orthologue functional annotations and (iii) a genomic organization view designed to improve the legibility of rearrangements and distinctive loci. The latter display combines symbolic and proportional graphical paradigms. Synchronized navigation across multiple species and interoperability between the views are core features of Insyght. A gene filter mechanism is provided that helps the user to build a biologically relevant gene set according to multiple criteria such as presence/absence of homologues and/or various annotations. We illustrate the use of Insyght with scenarios. Currently, only Bacteria and Archaea are supported. A public instance is available at http://genome.jouy.inra.fr/Insyght . The tool is freely downloadable for private data set analysis.
    Keywords: Computational Methods, Genomics
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    iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition (2014)
    Oxford University Press
    Details
    Publication Date: 2014-11-28
    Description: The 54 promoters are unique in prokaryotic genome and responsible for transcripting carbon and nitrogen-related genes. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the 54 promoters. Here, a predictor called ‘ iPro54-PseKNC ’ was developed. In the predictor, the samples of DNA sequences were formulated by a novel feature vector called ‘pseudo k -tuple nucleotide composition’, which was further optimized by the incremental feature selection procedure. The performance of iPro54-PseKNC was examined by the rigorous jackknife cross-validation tests on a stringent benchmark data set. As a user-friendly web-server, iPro54-PseKNC is freely accessible at http://lin.uestc.edu.cn/server/iPro54-PseKNC . For the convenience of the vast majority of experimental scientists, a step-by-step protocol guide was provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics that were presented in this paper just for its integrity. Meanwhile, we also discovered through an in-depth statistical analysis that the distribution of distances between the transcription start sites and the translation initiation sites were governed by the gamma distribution, which may provide a fundamental physical principle for studying the 54 promoters.
    Keywords: Computational Methods, Genomics
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    Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models (2014)
    Oxford University Press
    Details
    Publication Date: 2014-11-28
    Description: We present a discriminative learning method for pattern discovery of binding sites in nucleic acid sequences based on hidden Markov models. Sets of positive and negative example sequences are mined for sequence motifs whose occurrence frequency varies between the sets. The method offers several objective functions, but we concentrate on mutual information of condition and motif occurrence. We perform a systematic comparison of our method and numerous published motif-finding tools. Our method achieves the highest motif discovery performance, while being faster than most published methods. We present case studies of data from various technologies, including ChIP-Seq, RIP-Chip and PAR-CLIP, of embryonic stem cell transcription factors and of RNA-binding proteins, demonstrating practicality and utility of the method. For the alternative splicing factor RBM10, our analysis finds motifs known to be splicing-relevant. The motif discovery method is implemented in the free software package Discrover. It is applicable to genome- and transcriptome-scale data, makes use of available repeat experiments and aside from binary contrasts also more complex data configurations can be utilized.
    Keywords: Computational Methods, Genomics
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    Spatial localization of co-regulated genes exceeds genomic gene clustering in the Saccharomyces cerevisiae genome (2013)
    Oxford University Press
    Details
    Publication Date: 2013-02-20
    Description: While it has been long recognized that genes are not randomly positioned along the genome, the degree to which its 3D structure influences the arrangement of genes has remained elusive. In particular, several lines of evidence suggest that actively transcribed genes are spatially co-localized, forming transcription factories; however, a generalized systematic test has hitherto not been described. Here we reveal transcription factories using a rigorous definition of genomic structure based on Saccharomyces cerevisiae chromosome conformation capture data, coupled with an experimental design controlling for the primary gene order. We develop a data-driven method for the interpolation and the embedding of such datasets and introduce statistics that enable the comparison of the spatial and genomic densities of genes. Combining these, we report evidence that co-regulated genes are clustered in space, beyond their observed clustering in the context of gene order along the genome and show this phenomenon is significant for 64 out of 117 transcription factors. Furthermore, we show that those transcription factors with high spatially co-localized targets are expressed higher than those whose targets are not spatially clustered. Collectively, our results support the notion that, at a given time, the physical density of genes is intimately related to regulatory activity.
    Keywords: Computational Methods, Genomics
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    PanOCT: automated clustering of orthologs using conserved gene neighborhood for pan-genomic analysis of bacterial strains and closely related species (2012)
    Oxford University Press
    Details
    Publication Date: 2012-12-14
    Description: Pan-genome ortholog clustering tool ( PanOCT ) is a tool for pan-genomic analysis of closely related prokaryotic species or strains. PanOCT uses conserved gene neighborhood information to separate recently diverged paralogs into orthologous clusters where homology-only clustering methods cannot. The results from PanOCT and three commonly used graph-based ortholog-finding programs were compared using a set of four publicly available strains of the same bacterial species. All four methods agreed on ~70% of the clusters and ~86% of the proteins. The clusters that did not agree were inspected for evidence of correctness resulting in 85 high-confidence manually curated clusters that were used to compare all four methods.
    Keywords: Computational Methods, Genomics
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    A novel ab initio identification system of transcriptional regulation motifs in genome DNA sequences based on direct comparison scheme of signal/noise distributions (2012)
    Oxford University Press
    Details
    Publication Date: 2012-10-10
    Description: A novel ab initio parameter-tuning-free system to identify transcriptional factor (TF) binding motifs (TFBMs) in genome DNA sequences was developed. It is based on the comparison of two types of frequency distributions with respect to the TFBM candidates in the target DNA sequences and the non-candidates in the background sequence, with the latter generated by utilizing the intergenic sequences. For benchmark tests, we used DNA sequence datasets extracted by ChIP-on-chip and ChIP-seq techniques and identified 65 yeast and four mammalian TFBMs, with the latter including gaps. The accuracy of our system was compared with those of other available programs (i.e. MEME, Weeder, BioProspector, MDscan and DME) and was the best among them, even without tuning of the parameter set for each TFBM and pre-treatment/editing of the target DNA sequences. Moreover, with respect to some TFs for which the identified motifs are inconsistent with those in the references, our results were revealed to be correct, by comparing them with other existing experimental data. Thus, our identification system does not need any other biological information except for gene positions, and is also expected to be applicable to genome DNA sequences to identify unknown TFBMs as well as known ones.
    Keywords: Computational Methods, Genomics
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    Identification of new microRNA-regulated genes by conserved targeting in plant species (2012)
    Oxford University Press
    Details
    Publication Date: 2012-10-10
    Description: MicroRNAs (miRNAs) are major regulators of gene expression in multicellular organisms. They recognize their targets by sequence complementarity and guide them to cleavage or translational arrest. It is generally accepted that plant miRNAs have extensive complementarity to their targets and their prediction usually relies on the use of empirical parameters deduced from known miRNA–target interactions. Here, we developed a strategy to identify miRNA targets which is mainly based on the conservation of the potential regulation in different species. We applied the approach to expressed sequence tags datasets from angiosperms. Using this strategy, we predicted many new interactions and experimentally validated previously unknown miRNA targets in Arabidopsis thaliana . Newly identified targets that are broadly conserved include auxin regulators, transcription factors and transporters. Some of them might participate in the same pathways as the targets known before, suggesting that some miRNAs might control different aspects of a biological process. Furthermore, this approach can be used to identify targets present in a specific group of species, and, as a proof of principle, we analyzed Solanaceae -specific targets. The presented strategy can be used alone or in combination with other approaches to find miRNA targets in plants.
    Keywords: Computational Methods, Genomics
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    Use of ChIP-Seq data for the design of a multiple promoter-alignment method (2012)
    Oxford University Press
    Details
    Publication Date: 2012-04-15
    Description: We address the challenge of regulatory sequence alignment with a new method, Pro-Coffee, a multiple aligner specifically designed for homologous promoter regions. Pro-Coffee uses a dinucleotide substitution matrix estimated on alignments of functional binding sites from TRANSFAC. We designed a validation framework using several thousand families of orthologous promoters. This dataset was used to evaluate the accuracy for predicting true human orthologs among their paralogs. We found that whereas other methods achieve on average 73.5% accuracy, and 77.6% when trained on that same dataset, the figure goes up to 80.4% for Pro-Coffee. We then applied a novel validation procedure based on multi-species ChIP-seq data. Trained and untrained methods were tested for their capacity to correctly align experimentally detected binding sites. Whereas the average number of correctly aligned sites for two transcription factors is 284 for default methods and 316 for trained methods, Pro-Coffee achieves 331, 16.5% above the default average. We find a high correlation between a method's performance when classifying orthologs and its ability to correctly align proven binding sites. Not only has this interesting biological consequences, it also allows us to conclude that any method that is trained on the ortholog data set will result in functionally more informative alignments.
    Keywords: Computational Methods, Genomics
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    MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity (2012)
    Oxford University Press
    Details
    Publication Date: 2012-04-15
    Description: MCScan is an algorithm able to scan multiple genomes or subgenomes in order to identify putative homologous chromosomal regions, and align these regions using genes as anchors. The MCScanX toolkit implements an adjusted MCScan algorithm for detection of synteny and collinearity that extends the original software by incorporating 14 utility programs for visualization of results and additional downstream analyses. Applications of MCScanX to several sequenced plant genomes and gene families are shown as examples. MCScanX can be used to effectively analyze chromosome structural changes, and reveal the history of gene family expansions that might contribute to the adaptation of lineages and taxa. An integrated view of various modes of gene duplication can supplement the traditional gene tree analysis in specific families. The source code and documentation of MCScanX are freely available at http://chibba.pgml.uga.edu/mcscan2/ .
    Keywords: Computational Methods, Genomics
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    LARVA: an integrative framework for large-scale analysis of recurrent variants in noncoding annotations (2015)
    Oxford University Press
    Details
    Publication Date: 2015-09-30
    Description: In cancer research, background models for mutation rates have been extensively calibrated in coding regions, leading to the identification of many driver genes, recurrently mutated more than expected. Noncoding regions are also associated with disease; however, background models for them have not been investigated in as much detail. This is partially due to limited noncoding functional annotation. Also, great mutation heterogeneity and potential correlations between neighboring sites give rise to substantial overdispersion in mutation count, resulting in problematic background rate estimation. Here, we address these issues with a new computational framework called LARVA. It integrates variants with a comprehensive set of noncoding functional elements, modeling the mutation counts of the elements with a β-binomial distribution to handle overdispersion. LARVA, moreover, uses regional genomic features such as replication timing to better estimate local mutation rates and mutational hotspots. We demonstrate LARVA's effectiveness on 760 whole-genome tumor sequences, showing that it identifies well-known noncoding drivers, such as mutations in the TERT promoter. Furthermore, LARVA highlights several novel highly mutated regulatory sites that could potentially be noncoding drivers. We make LARVA available as a software tool and release our highly mutated annotations as an online resource ( larva.gersteinlab.org ).
    Keywords: Computational Methods, Genomics
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    Metabolic modeling of clostridia: current developments and applications (2016)
    Oxford University Press
    Details
    Publication Date: 2016-01-29
    Description: Anaerobic Clostridium spp. is an important bioproduction microbial genus that can produce solvents and utilize a broad spectrum of substrates including cellulose and syngas. Genome-scale metabolic (GSM) models are increasingly being put forth for various clostridial strains to explore their respective metabolic capabilities and suitability for various bioconversions. In this study, we have selected representative GSM models for six different clostridia ( Clostridium acetobutylicum , C. beijerinckii , C. butyricum , C. cellulolyticum , C. ljungdahlii and C. thermocellum ) and performed a detailed model comparison contrasting their metabolic repertoire. We also discuss various applications of these GSM models to guide metabolic engineering interventions as well as assessing cellular physiology.
    Keywords: Biotechnology & Synthetic Biology
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    Beegle: from literature mining to disease-gene discovery (2016)
    Oxford University Press
    Details
    Publication Date: 2016-01-30
    Description: Disease-gene identification is a challenging process that has multiple applications within functional genomics and personalized medicine. Typically, this process involves both finding genes known to be associated with the disease (through literature search) and carrying out preliminary experiments or screens (e.g. linkage or association studies, copy number analyses, expression profiling) to determine a set of promising candidates for experimental validation. This requires extensive time and monetary resources. We describe Beegle , an online search and discovery engine that attempts to simplify this process by automating the typical approaches. It starts by mining the literature to quickly extract a set of genes known to be linked with a given query, then it integrates the learning methodology of Endeavour (a gene prioritization tool) to train a genomic model and rank a set of candidate genes to generate novel hypotheses. In a realistic evaluation setup, Beegle has an average recall of 84% in the top 100 returned genes as a search engine, which improves the discovery engine by 12.6% in the top 5% prioritized genes. Beegle is publicly available at http://beegle.esat.kuleuven.be/ .
    Keywords: Computational Methods, Genomics
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    A computational method for studying the relation between alternative splicing and DNA methylation (2016)
    Oxford University Press
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    Publication Date: 2016-01-30
    Description: Alternative splicing is an important mechanism in eukaryotes that expands the transcriptome and proteome significantly. It plays an important role in a number of biological processes. Understanding its regulation is hence an important challenge. Recently, increasing evidence has been collected that supports an involvement of intragenic DNA methylation in the regulation of alternative splicing. The exact mechanisms of regulation, however, are largely unknown, and speculated to be complex: different methylation profiles might exist, each of which could be associated with a different regulation mechanism. We present a computational technique that is able to determine such stable methylation patterns and allows to correlate these patterns with inclusion propensity of exons. Pattern detection is based on dynamic time warping (DTW) of methylation profiles, a sophisticated similarity measure for signals that can be non-trivially transformed. We design a flexible self-organizing map approach to pattern grouping. Exemplary application on available data sets indicates that stable patterns which correlate non-trivially with exon inclusion do indeed exist. To improve the reliability of these predictions, further studies on larger data sets will be required. We have thus taken great care that our software runs efficiently on modern hardware, so that it can support future studies on large-scale data sets.
    Keywords: Computational Methods, Genomics
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    BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality using next generation sequencing data (2016)
    Oxford University Press
    Details
    Publication Date: 2016-03-01
    Description: Tumors are characterized by properties of genetic instability, heterogeneity, and significant oligoclonality. Elucidating this intratumoral heterogeneity is challenging but important. In this study, we propose a framework, BubbleTree, to characterize the tumor clonality using next generation sequencing (NGS) data. BubbleTree simultaneously elucidates the complexity of a tumor biopsy, estimating cancerous cell purity, tumor ploidy, allele-specific copy number, and clonality and represents this in an intuitive graph. We further developed a three-step heuristic method to automate the interpretation of the BubbleTree graph, using a divide-and-conquer strategy. In this study, we demonstrated the performance of BubbleTree with comparisons to similar commonly used tools such as THetA2, ABSOLUTE, AbsCN-seq and ASCAT, using both simulated and patient-derived data. BubbleTree outperformed these tools, particularly in identifying tumor subclonal populations and polyploidy. We further demonstrated BubbleTree's utility in tracking clonality changes from patients’ primary to metastatic tumor and dating somatic single nucleotide and copy number variants along the tumor clonal evolution. Overall, the BubbleTree graph and corresponding model is a powerful approach to provide a comprehensive spectrum of the heterogeneous tumor karyotype in human tumors. BubbleTree is R-based and freely available to the research community ( https://www.bioconductor.org/packages/release/bioc/html/BubbleTree.html ).
    Keywords: Computational Methods, Genomics
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    Acetone-butanol-ethanol fermentation of corn stover: current production methods, economic viability and commercial use (2016)
    Oxford University Press
    Details
    Publication Date: 2016-03-02
    Description: Biobutanol is a next-generation liquid biofuel with properties akin to those of gasoline. There is a widespread effort to commercialize biobutanol production from agricultural residues, such as corn stover, which do not compete with human and animal foods. This pursuit is backed by extensive government mandates to expand alternative energy sources. This review provides an overview of research on biobutanol production using corn stover feedstock. Structural composition, pretreatment, sugar yield (following pretreatment and hydrolysis) and generation of lignocellulose-derived microbial inhibitory compounds (LDMICs) from corn stover are discussed. The review also discusses different Clostridium species and strains employed for biobutanol production from corn stover-derived sugars with respect to solvent yields, tolerance to LDMICs and in situ solvent recovery (integrated fermentation). Further, the economics of cellulosic biobutanol production are highlighted and compared to corn starch-derived ethanol and gasoline. As discussed herein, the economic competitiveness of biobutanol production from corn stover largely depends on feedstock processing and fermentation process design.
    Keywords: Biotechnology & Synthetic Biology
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    A Pseudomonas putida double mutant deficient in butanol assimilation: a promising step for engineering a biological biofuel production platform (2016)
    Oxford University Press
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    Publication Date: 2016-02-20
    Description: Biological production in heterologous hosts is of interest for the production of the C4 alcohol (butanol) and other chemicals. However, some hurdles need to be overcome in order to achieve an economically viable process; these include avoiding the consumption of butanol and maintaining tolerance to this solvent during production. Pseudomonas putida is a potential host for solvent production; in order to further adapt P. putida to this role, we generated mini-Tn 5 mutant libraries in strain BIRD-1 that do not consume butanol. We analyzed the insertion site of the mini-Tn 5 in a mutant that was deficient in assimilation of butanol using arbitrary PCR followed by Sanger sequencing and found that the transposon was inserted in the malate synthase B gene. Here, we show that in a second round of mutagenesis a double mutant unable to take up butanol had an insertion in a gene coding for a multisensor hybrid histidine kinase. The genetic context of the histidine kinase sensor revealed the presence of a set of genes potentially involved in butanol assimilation; qRT-PCR analysis showed induction of this set of genes in the wild type and the malate synthase mutant but not in the double mutant.
    Keywords: Biotechnology & Synthetic Biology
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    Frontiers in microbial 1-butanol and isobutanol production (2016)
    Oxford University Press
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    Publication Date: 2016-02-20
    Description: The heavy dependence on petroleum-derived fuel has raised concerns about energy sustainability and climate change, which have prompted researchers to explore fuel production from renewable sources. 1-Butanol and isobutanol are promising biofuels that have favorable properties and can also serve as solvents or chemical feedstocks. Microbial production of these alcohols provides great opportunities to access a wide spectrum of renewable resources. In recent years, research has improved the native 1-butanol production and has engineered isobutanol production in various organisms to explore metabolic diversity and a broad range of substrates. This review focuses on progress in metabolic engineering for the production of these two compounds using various resources.
    Keywords: Biotechnology & Synthetic Biology
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    Recent progress in biobutanol tolerance in microbial systems with an emphasis on Clostridium (2016)
    Oxford University Press
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    Publication Date: 2016-02-20
    Description: Biobased production of butanol promises a more sustainable route for industrial production. However, butanol toxicity remains a barrier for achieving high product titers. Investigation into butanol stress has shed some light on its modes of toxicity. Unfortunately, there still remain significant shortfalls in our understanding of the complex interactions of butanol with cells. To address this knowledge gap, a diverse range of tools have been employed to gain a better understanding of the adverse effects of butanol on the cell. These findings have lead to the identification of possible molecular mechanisms associated with butanol tolerance, which can be harnessed for future strain development efforts. This review focuses on recent efforts to address the toxicity of butanol in microbial producers and offers some perspectives on the future direction of this research sector.
    Keywords: Biotechnology & Synthetic Biology
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    Synergistic effect of calcium and zinc on glucose/xylose utilization and butanol tolerance of Clostridium acetobutylicum (2016)
    Oxford University Press
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    Publication Date: 2016-02-25
    Description: Biobutanol outperforms bioethanol as an advanced biofuel, but is not economically competitive in terms of its titer, yield and productivity associated with feedstocks and energy cost. In this work, the synergistic effect of calcium and zinc was investigated in the acetone-butanol-ethanol (ABE) fermentation by Clostridium acetobutylicum using glucose, xylose and glucose/xylose mixtures as carbon source(s). Significant improvements associated with enhanced glucose/xylose utilization, cell growth, acids re-assimilation and butanol biosynthesis were achieved. Especially, the maximum butanol and ABE production of 16.1 and 25.9 g L –1 were achieved from 69.3 g L –1 glucose with butanol/ABE productivities of 0.40 and 0.65 g L –1 h –1 compared to those of 11.7 and 19.4 g/L with 0.18 and 0.30 g L –1 h –1 obtained in the control respectively without any supplement. More importantly, zinc was significantly involved in the butanol tolerance based on the improved xylose utilization under various butanol-shock conditions (2, 4, 6, 8 and 10 g L –1 butanol). Under the same conditions, calcium and zinc co-supplementation led to the best xylose utilization and butanol production. These results suggested that calcium and zinc could play synergistic roles improving ABE fermentation by C. acetobutylicum .
    Keywords: Biotechnology & Synthetic Biology
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    Pseudomonas putida KT2440 markerless gene deletion using a combination of {lambda} Red recombineering and Cre/loxP site-specific recombination (2016)
    Oxford University Press
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    Publication Date: 2016-02-07
    Description: Pseudomonas putida KT2440 is a saprophytic, environmental microorganism that plays important roles in the biodegradation of environmental toxic compounds and production of polymers, chemicals and secondary metabolites. Gene deletion of KT2440 usually involves cloning of the flanking homologous fragments of the gene of interest into a suicide vector followed by transferring into KT2440 via triparental conjugation. Selection and counterselection steps are then employed to generate gene deletion mutant. However, these methods are tedious and are not suitable for the manipulation of multiple genes simultaneously. Herein, a two-step, markerless gene deletion method is presented. First, homologous armsflanked loxP-neo-loxP was knocked-in to replace the gene of interest, then the kanamycin resistance marker is removed by Cre recombinase catalyzed site-specific recombination. Both two-plasmid and one-plasmid gene systems were established. MekR/P mekA regulated gene expression system was found to be suitable for tight Cre expression in one-plasmid deletion system. The straightforward, time saving and highly efficient markerless gene deletion strategy has the potential to facilitate the genetics and functional genomics study of P. putida KT2440.
    Keywords: Biotechnology & Synthetic Biology
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    Evaluation of viability, metabolic activity and spore quantity in clostridial cultures during ABE fermentation (2016)
    Oxford University Press
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    Publication Date: 2016-03-02
    Description: Flow cytometry, in combination with fluorescent staining, was used to evaluate population heterogeneity in acetone-butanol–ethanol fermentation that was carried out with type strain Clostridium beijerinckii NCIMB 8052 and non-type C. pasteurianum NRRL B-598. A combination of propidium iodide (PI) and carboxyfluorescein diacetate (CFDA), PI plus Syto-9 and bis-oxonol (BOX) alone were employed to distinguish between active and damaged cells together with simultaneous detection of spores. These strategies provided valuable information on the physiological state of clostridia. CFDA and PI staining gave the best separation of four distinct subpopulations of enzymatically active cells, doubly stained cells, damaged cells and spores. Proportional representation of cells in particular sub-regions correlated with growth characteristics, fermentation parameters such as substrate consumption and product formation in both species under different cultivation conditions.
    Keywords: Biotechnology & Synthetic Biology
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    Discovery and characterization of Alu repeat sequences via precise local read assembly (2015)
    Oxford University Press
    Details
    Publication Date: 2015-12-02
    Description: Alu insertions have contributed to 〉11% of the human genome and ~30–35 Alu subfamilies remain actively mobile, yet the characterization of polymorphic Alu insertions from short-read data remains a challenge. We build on existing computational methods to combine Alu detection and de novo assembly of WGS data as a means to reconstruct the full sequence of insertion events from Illumina paired end reads. Comparison with published calls obtained using PacBio long-reads indicates a false discovery rate below 5%, at the cost of reduced sensitivity due to the colocation of reference and non-reference repeats. We generate a highly accurate call set of 1614 completely assembled Alu variants from 53 samples from the Human Genome Diversity Project (HGDP) panel. We utilize the reconstructed alternative insertion haplotypes to genotype 1010 fully assembled insertions, obtaining 〉99% agreement with genotypes obtained by PCR. In our assembled sequences, we find evidence of premature insertion mechanisms and observe 5' truncation in 16% of Alu Ya5 and Alu Yb8 insertions. The sites of truncation coincide with stem-loop structures and SRP9/14 binding sites in the Alu RNA, implicating L1 ORF2p pausing in the generation of 5' truncations. Additionally, we identified variable Alu J and Alu S elements that likely arose due to non-retrotransposition mechanisms.
    Keywords: Computational Methods, Genomics
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    Simultaneous saccharification and fermentation of dilute alkaline-pretreated corn stover for enhanced butanol production by Clostridium saccharobutylicum DSM 13864 (2016)
    Oxford University Press
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    Publication Date: 2016-02-03
    Description: Simultaneous saccharification and fermentation (SSF) process was applied for biobutanol production by Clostridium saccharobutylicum DSM 13864 from corn stover (CS). The key influential factors in SSF process, including corn steep liquor concentration, dry biomass and enzyme loading, SSF temperature, inoculation size and pre-hydrolysis time were optimized. In 5-L bioreactor with SSF process, butanol titer and productivity of 12.3 g/L and 0.257 g/L/h were achieved at 48 h, which were 20.6% and 21.2% higher than those in separate hydrolysis and fermentation (SHF), respectively. The butanol yield reached 0.175 g/g pretreated CS in SSF, representing 50.9% increase than that in SHF (0.116 g/g pretreated CS). This study proves the feasibility of efficient and economic production of biobutanol from CS by SSF.
    Keywords: Biotechnology & Synthetic Biology
    Print ISSN: 0378-1097
    Electronic ISSN: 1574-6968
    Topics: Biology
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    A mostly traditional approach improves alignment of bisulfite-converted DNA (2012)
    Oxford University Press
    Details
    Publication Date: 2012-07-22
    Description: Cytosines in genomic DNA are sometimes methylated. This affects many biological processes and diseases. The standard way of measuring methylation is to use bisulfite, which converts unmethylated cytosines to thymines, then sequence the DNA and compare it to a reference genome sequence. We describe a method for the critical step of aligning the DNA reads to the correct genomic locations. Our method builds on classic alignment techniques, including likelihood-ratio scores and spaced seeds. In a realistic benchmark, our method has a better combination of sensitivity, specificity and speed than nine other high-throughput bisulfite aligners. This study enables more accurate and rational analysis of DNA methylation. It also illustrates how to adapt general-purpose alignment methods to a special case with distorted base patterns: this should be informative for other special cases such as ancient DNA and AT-rich genomes.
    Keywords: Computational Methods, Genomics
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    PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies (2012)
    Oxford University Press
    Details
    Publication Date: 2012-09-13
    Description: Prophages are phages in lysogeny that are integrated into, and replicated as part of, the host bacterial genome. These mobile elements can have tremendous impact on their bacterial hosts’ genomes and phenotypes, which may lead to strain emergence and diversification, increased virulence or antibiotic resistance. However, finding prophages in microbial genomes remains a problem with no definitive solution. The majority of existing tools rely on detecting genomic regions enriched in protein-coding genes with known phage homologs, which hinders the de novo discovery of phage regions. In this study, a weighted phage detection algorithm, PhiSpy was developed based on seven distinctive characteristics of prophages, i.e. protein length, transcription strand directionality, customized AT and GC skew, the abundance of unique phage words, phage insertion points and the similarity of phage proteins. The first five characteristics are capable of identifying prophages without any sequence similarity with known phage genes. PhiSpy locates prophages by ranking genomic regions enriched in distinctive phage traits, which leads to the successful prediction of 94% of prophages in 50 complete bacterial genomes with a 6% false-negative rate and a 0.66% false-positive rate.
    Keywords: Computational Methods, Genomics
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    Exploiting mid-range DNA patterns for sequence classification: binary abstraction Markov models (2012)
    Oxford University Press
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    Publication Date: 2012-06-06
    Description: Messenger RNA sequences possess specific nucleotide patterns distinguishing them from non-coding genomic sequences. In this study, we explore the utilization of modified Markov models to analyze sequences up to 44 bp, far beyond the 8-bp limit of conventional Markov models, for exon/intron discrimination. In order to analyze nucleotide sequences of this length, their information content is first reduced by conversion into shorter binary patterns via the application of numerous abstraction schemes. After the conversion of genomic sequences to binary strings, homogenous Markov models trained on the binary sequences are used to discriminate between exons and introns. We term this approach the Binary Abstraction Markov Model (BAMM). High-quality abstraction schemes for exon/intron discrimination are selected using optimization algorithms on supercomputers. The best MM classifiers are then combined using support vector machines into a single classifier. With this approach, over 95% classification accuracy is achieved without taking reading frame into account. With further development, the BAMM approach can be applied to sequences lacking the genetic code such as ncRNAs and 5'-untranslated regions.
    Keywords: Computational Methods, Genomics
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    New methods for finding common insertion sites and co-occurring common insertion sites in transposon- and virus-based genetic screens (2012)
    Oxford University Press
    Details
    Publication Date: 2012-05-13
    Description: Insertional mutagenesis screens in mice are used to identify individual genes that drive tumor formation. In these screens, candidate cancer genes are identified if their genomic location is proximal to a common insertion site (CIS) defined by high rates of transposon or retroviral insertions in a given genomic window. In this article, we describe a new method for defining CISs based on a Poisson distribution, the Poisson Regression Insertion Model, and show that this new method is an improvement over previously described methods. We also describe a modification of the method that can identify pairs and higher orders of co-occurring common insertion sites. We apply these methods to two data sets, one generated in a transposon-based screen for gastrointestinal tract cancer genes and another based on the set of retroviral insertions in the Retroviral Tagged Cancer Gene Database. We show that the new methods identify more relevant candidate genes and candidate gene pairs than found using previous methods. Identification of the biologically relevant set of mutations that occur in a single cell and cause tumor progression will aid in the rational design of single and combinatorial therapies in the upcoming age of personalized cancer therapy.
    Keywords: Computational Methods, Genomics
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    Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes (2014)
    Oxford University Press
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    Publication Date: 2014-05-01
    Description: Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant ( P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients’ molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification.
    Keywords: Computational Methods, Genomics
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    fourSig: a method for determining chromosomal interactions in 4C-Seq data (2014)
    Oxford University Press
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    Publication Date: 2014-05-01
    Description: The ability to correlate chromosome conformation and gene expression gives a great deal of information regarding the strategies used by a cell to properly regulate gene activity. 4C-Seq is a relatively new and increasingly popular technology where the set of genomic interactions generated by a single point in the genome can be determined. 4C-Seq experiments generate large, complicated data sets and it is imperative that signal is properly distinguished from noise. Currently, there are a limited number of methods for analyzing 4C-Seq data. Here, we present a new method, fourSig , which in addition to being precise and simple to use also includes a new feature that prioritizes detected interactions. Our results demonstrate the efficacy of fourSig with previously published and novel 4C-Seq data sets and show that our significance prioritization correlates with the ability to reproducibly detect interactions among replicates.
    Keywords: Computational Methods, Genomics
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    High-resolution functional annotation of human transcriptome: predicting isoform functions by a novel multiple instance-based label propagation method (2014)
    Oxford University Press
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    Publication Date: 2014-04-03
    Description: Alternative transcript processing is an important mechanism for generating functional diversity in genes. However, little is known about the precise functions of individual isoforms. In fact, proteins (translated from transcript isoforms), not genes, are the function carriers. By integrating multiple human RNA-seq data sets, we carried out the first systematic prediction of isoform functions, enabling high-resolution functional annotation of human transcriptome. Unlike gene function prediction, isoform function prediction faces a unique challenge: the lack of the training data—all known functional annotations are at the gene level. To address this challenge, we modelled the gene–isoform relationships as multiple instance data and developed a novel label propagation method to predict functions. Our method achieved an average area under the receiver operating characteristic curve of 0.67 and assigned functions to 15 572 isoforms. Interestingly, we observed that different functions have different sensitivities to alternative isoform processing, and that the function diversity of isoforms from the same gene is positively correlated with their tissue expression diversity. Finally, we surveyed the literature to validate our predictions for a number of apoptotic genes. Strikingly, for the famous ‘TP53’ gene, we not only accurately identified the apoptosis regulation function of its five isoforms, but also correctly predicted the precise direction of the regulation.
    Keywords: Computational Methods, Genomics
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    Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs (2012)
    Oxford University Press
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    Publication Date: 2012-03-29
    Description: Broadly, computational approaches for ortholog assignment is a three steps process: (i) identify all putative homologs between the genomes, (ii) identify gene anchors and (iii) link anchors to identify best gene matches given their order and context. In this article, we engineer two methods to improve two important aspects of this pipeline [specifically steps (ii) and (iii)]. First, computing sequence similarity data [step (i)] is a computationally intensive task for large sequence sets, creating a bottleneck in the ortholog assignment pipeline. We have designed a fast and highly scalable sort-join method (afree) based on k -mer counts to rapidly compare all pairs of sequences in a large protein sequence set to identify putative homologs. Second, availability of complex genomes containing large gene families with prevalence of complex evolutionary events, such as duplications, has made the task of assigning orthologs and co-orthologs difficult. Here, we have developed an iterative graph matching strategy where at each iteration the best gene assignments are identified resulting in a set of orthologs and co-orthologs. We find that the afree algorithm is faster than existing methods and maintains high accuracy in identifying similar genes. The iterative graph matching strategy also showed high accuracy in identifying complex gene relationships. Standalone afree available from http://vbc.med.monash.edu.au/~kmahmood/afree . EGM2, complete ortholog assignment pipeline (including afree and the iterative graph matching method) available from http://vbc.med.monash.edu.au/~kmahmood/EGM2 .
    Keywords: Computational Methods, Genomics
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    The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process (2012)
    Oxford University Press
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    Publication Date: 2012-03-29
    Description: With the availability of next-generation sequencing (NGS) technology, it is expected that sequence variants may be called on a genomic scale. Here, we demonstrate that a deeper understanding of the distribution of the variant call frequencies at heterozygous loci in NGS data sets is a prerequisite for sensitive variant detection. We model the crucial steps in an NGS protocol as a stochastic branching process and derive a mathematical framework for the expected distribution of alleles at heterozygous loci before measurement that is sequencing. We confirm our theoretical results by analyzing technical replicates of human exome data and demonstrate that the variance of allele frequencies at heterozygous loci is higher than expected by a simple binomial distribution. Due to this high variance, mutation callers relying on binomial distributed priors are less sensitive for heterozygous variants that deviate strongly from the expected mean frequency. Our results also indicate that error rates can be reduced to a greater degree by technical replicates than by increasing sequencing depth.
    Keywords: Computational Methods, Genomics
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    Unsupervised discovery of microbial population structure within metagenomes using nucleotide base composition (2012)
    Oxford University Press
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    Publication Date: 2012-03-14
    Description: An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial communities can be attained, more so than gene-centric approaches that explore overall functionality. In this study, we propose an unsupervised, model-based binning method with two clustering tiers, which uses a novel transformation of the oligonucleotide frequency-derived error gradient and GC content to generate coarse groups at the first tier of clustering; and tetranucleotide frequency to refine these groups at the secondary clustering tier. The proposed method has a demonstrated improvement over PhyloPythia, S-GSOM, TACOA and TaxSOM on all three benchmarks that were used for evaluation in this study. The proposed method is then applied to a pyrosequenced metagenomic library of mud volcano sediment sampled in southwestern Taiwan, with the inferred population structure validated against complementary sequencing of 16S ribosomal RNA marker genes. Finally, the proposed method was further validated against four publicly available metagenomes, including a highly complex Antarctic whale-fall bone sample, which was previously assumed to be too complex for binning prior to functional analysis.
    Keywords: Computational Methods, Genomics
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    NTRFinder: a software tool to find nested tandem repeats (2012)
    Oxford University Press
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    Publication Date: 2012-02-17
    Description: We introduce the software tool NTRFinder to search for a complex repetitive structure in DNA we call a nested tandem repeat (NTR). An NTR is a recurrence of two or more distinct tandem motifs interspersed with each other. We propose that NTRs can be used as phylogenetic and population markers. We have tested our algorithm on both real and simulated data, and present some real NTRs of interest. NTRFinder can be downloaded from http://www.maths.otago.ac.nz/~aamatroud/ .
    Keywords: Computational Methods, Genomics
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    sPARTA: a parallelized pipeline for integrated analysis of plant miRNA and cleaved mRNA data sets, including new miRNA target-identification software (2014)
    Oxford University Press
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    Publication Date: 2014-10-10
    Description: Parallel analysis of RNA ends (PARE) is a technique utilizing high-throughput sequencing to profile uncapped, mRNA cleavage or decay products on a genome-wide basis. Tools currently available to validate miRNA targets using PARE data employ only annotated genes, whereas important targets may be found in unannotated genomic regions. To handle such cases and to scale to the growing availability of PARE data and genomes, we developed a new tool, ‘ sPARTA ’ (small RNA-PARE target analyzer) that utilizes a built-in, plant-focused target prediction module (aka ‘ miRferno ’). sPARTA not only exhibits an unprecedented gain in speed but also it shows greater predictive power by validating more targets, compared to a popular alternative. In addition, the novel ‘seed-free’ mode, optimized to find targets irrespective of complementarity in the seed-region, identifies novel intergenic targets. To fully capitalize on the novelty and strengths of sPARTA , we developed a web resource, ‘ comPARE ’, for plant miRNA target analysis; this facilitates the systematic identification and analysis of miRNA-target interactions across multiple species, integrated with visualization tools. This collation of high-throughput small RNA and PARE datasets from different genomes further facilitates re-evaluation of existing miRNA annotations, resulting in a ‘cleaner’ set of microRNAs.
    Keywords: Computational Methods, Genomics
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    A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions (2014)
    Oxford University Press
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    Publication Date: 2014-10-10
    Description: Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher's exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.
    Keywords: Computational Methods, Genomics
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    COMET: adaptive context-based modeling for ultrafast HIV-1 subtype identification (2014)
    Oxford University Press
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    Publication Date: 2014-10-10
    Description: Viral sequence classification has wide applications in clinical, epidemiological, structural and functional categorization studies. Most existing approaches rely on an initial alignment step followed by classification based on phylogenetic or statistical algorithms. Here we present an ultrafast alignment-free subtyping tool for human immunodeficiency virus type one (HIV-1) adapted from Prediction by Partial Matching compression. This tool, named COMET, was compared to the widely used phylogeny-based REGA and SCUEAL tools using synthetic and clinical HIV data sets (1 090 698 and 10 625 sequences, respectively). COMET's sensitivity and specificity were comparable to or higher than the two other subtyping tools on both data sets for known subtypes. COMET also excelled in detecting and identifying new recombinant forms, a frequent feature of the HIV epidemic. Runtime comparisons showed that COMET was almost as fast as USEARCH. This study demonstrates the advantages of alignment-free classification of viral sequences, which feature high rates of variation, recombination and insertions/deletions. COMET is free to use via an online interface.
    Keywords: Computational Methods, Genomics
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    Large-scale modeling of condition-specific gene regulatory networks by information integration and inference (2014)
    Oxford University Press
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    Publication Date: 2014-11-28
    Description: Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components. The integration of prior biological knowledge into the modeling process of systems-wide gene regulation enables the large-scale interpretation of gene expression signals in the context of known regulatory relations. We developed COGERE ( http://mips.helmholtz-muenchen.de/cogere ), a method for the inference of condition-specific gene regulatory networks in human and mouse. We integrated existing knowledge of regulatory interactions from multiple sources to a comprehensive model of prior information. COGERE infers condition-specific regulation by evaluating the mutual dependency between regulator (transcription factor or miRNA) and target gene expression using prior information. This dependency is scored by the non-parametric, nonlinear correlation coefficient 2 (eta squared) that is derived by a two-way analysis of variance. We show that COGERE significantly outperforms alternative methods in predicting condition-specific gene regulatory networks on simulated data sets. Furthermore, by inferring the cancer-specific gene regulatory network from the NCI-60 expression study, we demonstrate the utility of COGERE to promote hypothesis-driven clinical research.
    Keywords: Computational Methods, Genomics
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    ptRNApred: computational identification and classification of post-transcriptional RNA (2014)
    Oxford University Press
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    Publication Date: 2014-12-17
    Description: Non-coding RNAs (ncRNAs) are known to play important functional roles in the cell. However, their identification and recognition in genomic sequences remains challenging. In silico methods, such as classification tools, offer a fast and reliable way for such screening and multiple classifiers have already been developed to predict well-defined subfamilies of RNA. So far, however, out of all the ncRNAs, only tRNA, miRNA and snoRNA can be predicted with a satisfying sensitivity and specificity. We here present ptRNApred , a tool to detect and classify subclasses of non-coding RNA that are involved in the regulation of post-transcriptional modifications or DNA replication, which we here call post-transcriptional RNA (ptRNA). It (i) detects RNA sequences coding for post-transcriptional RNA from the genomic sequence with an overall sensitivity of 91% and a specificity of 94% and (ii) predicts ptRNA-subclasses that exist in eukaryotes: snRNA, snoRNA, RNase P, RNase MRP, Y RNA or telomerase RNA. AVAILABILITY: The ptRNApred software is open for public use on http://www.ptrnapred.org/ .
    Keywords: Computational Methods, Genomics
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    The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data (2014)
    Oxford University Press
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    Publication Date: 2014-12-17
    Description: Rapid development of next generation sequencing technology has enabled the identification of genomic alterations from short sequencing reads. There are a number of software pipelines available for calling single nucleotide variants from genomic DNA but, no comprehensive pipelines to identify, annotate and prioritize expressed SNVs (eSNVs) from non-directional paired-end RNA-Seq data. We have developed the eSNV-Detect, a novel computational system, which utilizes data from multiple aligners to call, even at low read depths, and rank variants from RNA-Seq. Multi-platform comparisons with the eSNV-Detect variant candidates were performed. The method was first applied to RNA-Seq from a lymphoblastoid cell-line, achieving 99.7% precision and 91.0% sensitivity in the expressed SNPs for the matching HumanOmni2.5 BeadChip data. Comparison of RNA-Seq eSNV candidates from 25 ER+ breast tumors from The Cancer Genome Atlas (TCGA) project with whole exome coding data showed 90.6–96.8% precision and 91.6–95.7% sensitivity. Contrasting single-cell mRNA-Seq variants with matching traditional multicellular RNA-Seq data for the MD-MB231 breast cancer cell-line delineated variant heterogeneity among the single-cells. Further, Sanger sequencing validation was performed for an ER+ breast tumor with paired normal adjacent tissue validating 29 out of 31 candidate eSNVs. The source code and user manuals of the eSNV-Detect pipeline for Sun Grid Engine and virtual machine are available at http://bioinformaticstools.mayo.edu/research/esnv-detect/ .
    Keywords: Computational Methods, Genomics
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    Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks (2015)
    Oxford University Press
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    Publication Date: 2015-03-14
    Description: Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback–Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni .
    Keywords: Computational Methods, Genomics
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    CODEX: a normalization and copy number variation detection method for whole exome sequencing (2015)
    Oxford University Press
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    Publication Date: 2015-04-02
    Description: High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but detecting and characterizing CNV from exome sequencing is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for whole exome sequencing data. The Poisson latent factor model in CODEX includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. CODEX is compared to existing methods on a population analysis of HapMap samples from the 1000 Genomes Project, and shown to be more accurate on three microarray-based validation data sets. We further evaluate performance on 222 neuroblastoma samples with matched normals and focus on a well-studied rare somatic CNV within the ATRX gene. We show that the cross-sample normalization procedure of CODEX removes more noise than normalizing the tumor against the matched normal and that the segmentation procedure performs well in detecting CNVs with nested structures.
    Keywords: Computational Methods, Genomics
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    Hunting complex differential gene interaction patterns across molecular contexts (2014)
    Oxford University Press
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    Publication Date: 2014-04-15
    Description: Heterogeneity in genetic networks across different signaling molecular contexts can suggest molecular regulatory mechanisms. Here we describe a comparative chi-square analysis (CP 2 ) method, considerably more flexible and effective than other alternatives, to screen large gene expression data sets for conserved and differential interactions. CP 2 decomposes interactions across conditions to assess homogeneity and heterogeneity. Theoretically, we prove an asymptotic chi-square null distribution for the interaction heterogeneity statistic. Empirically, on synthetic yeast cell cycle data, CP 2 achieved much higher statistical power in detecting differential networks than alternative approaches. We applied CP 2 to Drosophila melanogaster wing gene expression arrays collected under normal conditions, and conditions with overexpressed E2F and Cabut, two transcription factor complexes that promote ectopic cell cycling. The resulting differential networks suggest a mechanism by which E2F and Cabut regulate distinct gene interactions, while still sharing a small core network. Thus, CP 2 is sensitive in detecting network rewiring, useful in comparing related biological systems.
    Keywords: Computational Methods, Genomics
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    DNA motif elucidation using belief propagation (2013)
    Oxford University Press
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    Publication Date: 2013-09-06
    Description: Protein-binding microarray (PBM) is a high-throughout platform that can measure the DNA-binding preference of a protein in a comprehensive and unbiased manner. A typical PBM experiment can measure binding signal intensities of a protein to all the possible DNA k-mers (k = 8 ~10); such comprehensive binding affinity data usually need to be reduced and represented as motif models before they can be further analyzed and applied. Since proteins can often bind to DNA in multiple modes, one of the major challenges is to decompose the comprehensive affinity data into multimodal motif representations. Here, we describe a new algorithm that uses Hidden Markov Models (HMMs) and can derive precise and multimodal motifs using belief propagations. We describe an HMM-based approach using belief propagations (kmerHMM), which accepts and preprocesses PBM probe raw data into median-binding intensities of individual k-mers. The k-mers are ranked and aligned for training an HMM as the underlying motif representation. Multiple motifs are then extracted from the HMM using belief propagations. Comparisons of kmerHMM with other leading methods on several data sets demonstrated its effectiveness and uniqueness. Especially, it achieved the best performance on more than half of the data sets. In addition, the multiple binding modes derived by kmerHMM are biologically meaningful and will be useful in interpreting other genome-wide data such as those generated from ChIP-seq. The executables and source codes are available at the authors’ websites: e.g. http://www.cs.toronto.edu/~wkc/kmerHMM .
    Keywords: Computational Methods, Genomics
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    Improved search heuristics find 20 000 new alignments between human and mouse genomes (2014)
    Oxford University Press
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    Publication Date: 2014-04-15
    Description: Sequence similarity search is a fundamental way of analyzing nucleotide sequences. Despite decades of research, this is not a solved problem because there exist many similarities that are not found by current methods. Search methods are typically based on a seed-and-extend approach, which has many variants (e.g. spaced seeds, transition seeds), and it remains unclear how to optimize this approach. This study designs and tests seeding methods for inter-mammal and inter-insect genome comparison. By considering substitution patterns of real genomes, we design sets of multiple complementary transition seeds, which have better performance (sensitivity per run time) than previous seeding strategies. Often the best seed patterns have more transition positions than those used previously. We also point out that recent computer memory sizes (e.g. 60 GB) make it feasible to use multiple (e.g. eight) seeds for whole mammal genomes. Interestingly, the most sensitive settings achieve diminishing returns for human–dog and melanogaster–pseudoobscura comparisons, but not for human–mouse, which suggests that we still miss many human–mouse alignments. Our optimized heuristics find ~20 000 new human–mouse alignments that are missing from the standard UCSC alignments. We tabulate seed patterns and parameters that work well so they can be used in future research.
    Keywords: Computational Methods, Genomics
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    EDDY: a novel statistical gene set test method to detect differential genetic dependencies (2014)
    Oxford University Press
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    Publication Date: 2014-04-15
    Description: Identifying differential features between conditions is a popular approach to understanding molecular features and their mechanisms underlying a biological process of particular interest. Although many tests for identifying differential expression of gene or gene sets have been proposed, there was limited success in developing methods for differential interactions of genes between conditions because of its computational complexity. We present a method for Evaluation of Dependency DifferentialitY (EDDY), which is a statistical test for differential dependencies of a set of genes between two conditions. Unlike previous methods focused on differential expression of individual genes or correlation changes of individual gene–gene interactions, EDDY compares two conditions by evaluating the probability distributions of dependency networks from genes. The method has been evaluated and compared with other methods through simulation studies, and application to glioblastoma multiforme data resulted in informative cancer and glioblastoma multiforme subtype-related findings. The comparison with Gene Set Enrichment Analysis, a differential expression-based method, revealed that EDDY identifies the gene sets that are complementary to those identified by Gene Set Enrichment Analysis. EDDY also showed much lower false positives than Gene Set Co-expression Analysis, a method based on correlation changes of individual gene–gene interactions, thus providing more informative results. The Java implementation of the algorithm is freely available to noncommercial users. Download from: http://biocomputing.tgen.org/software/EDDY .
    Keywords: Computational Methods, Genomics
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    Comprehensive discovery of DNA motifs in 349 human cells and tissues reveals new features of motifs (2015)
    Oxford University Press
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    Publication Date: 2015-01-10
    Description: Comprehensive motif discovery under experimental conditions is critical for the global understanding of gene regulation. To generate a nearly complete list of human DNA motifs under given conditions, we employed a novel approach to de novo discover significant co-occurring DNA motifs in 349 human DNase I hypersensitive site datasets. We predicted 845 to 1325 motifs in each dataset, for a total of 2684 non-redundant motifs. These 2684 motifs contained 54.02 to 75.95% of the known motifs in seven large collections including TRANSFAC. In each dataset, we also discovered 43 663 to 2 013 288 motif modules, groups of motifs with their binding sites co-occurring in a significant number of short DNA regions. Compared with known interacting transcription factors in eight resources, the predicted motif modules on average included 84.23% of known interacting motifs. We further showed new features of the predicted motifs, such as motifs enriched in proximal regions rarely overlapped with motifs enriched in distal regions, motifs enriched in 5' distal regions were often enriched in 3' distal regions, etc. Finally, we observed that the 2684 predicted motifs classified the cell or tissue types of the datasets with an accuracy of 81.29%. The resources generated in this study are available at http://server.cs.ucf.edu/predrem/ .
    Keywords: Computational Methods, Genomics
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    Inferring population structure and relationship using minimal independent evolutionary markers in Y-chromosome: a hybrid approach of recursive feature selection for hierarchical clustering (2014)
    Oxford University Press
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    Publication Date: 2014-09-02
    Description: Inundation of evolutionary markers expedited in Human Genome Project and 1000 Genome Consortium has necessitated pruning of redundant and dependent variables. Various computational tools based on machine-learning and data-mining methods like feature selection/extraction have been proposed to escape the curse of dimensionality in large datasets. Incidentally, evolutionary studies, primarily based on sequentially evolved variations have remained un-facilitated by such advances till date. Here, we present a novel approach of recursive feature selection for hierarchical clustering of Y-chromosomal SNPs/haplogroups to select a minimal set of independent markers, sufficient to infer population structure as precisely as deduced by a larger number of evolutionary markers. To validate the applicability of our approach, we optimally designed MALDI-TOF mass spectrometry-based multiplex to accommodate independent Y-chromosomal markers in a single multiplex and genotyped two geographically distinct Indian populations. An analysis of 105 world-wide populations reflected that 15 independent variations/markers were optimal in defining population structure parameters, such as F ST , molecular variance and correlation-based relationship. A subsequent addition of randomly selected markers had a negligible effect (close to zero, i.e. 1 x 10 –3 ) on these parameters. The study proves efficient in tracing complex population structures and deriving relationships among world-wide populations in a cost-effective and expedient manner.
    Keywords: Computational Methods, Genomics
    Print ISSN: 0305-1048
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    Multivariate inference of pathway activity in host immunity and response to therapeutics (2014)
    Oxford University Press
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    Publication Date: 2014-09-17
    Description: Developing a quantitative view of how biological pathways are regulated in response to environmental factors is central for understanding of disease phenotypes. We present a computational framework, named Multivariate Inference of Pathway Activity (MIPA), which quantifies degree of activity induced in a biological pathway by computing five distinct measures from transcriptomic profiles of its member genes. Statistical significance of inferred activity is examined using multiple independent self-contained tests followed by a competitive analysis. The method incorporates a new algorithm to identify a subset of genes that may regulate the extent of activity induced in a pathway. We present an in-depth evaluation of specificity, robustness, and reproducibility of our method. We benchmarked MIPA's false positive rate at less than 1%. Using transcriptomic profiles representing distinct physiological and disease states, we illustrate applicability of our method in (i) identifying gene–gene interactions in autophagy-dependent response to Salmonella infection, (ii) uncovering gene–environment interactions in host response to bacterial and viral pathogens and (iii) identifying driver genes and processes that contribute to wound healing and response to anti-TNFα therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method.
    Keywords: Computational Methods, Genomics
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    MosaicSolver: a tool for determining recombinants of viral genomes from pileup data (2014)
    Oxford University Press
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    Publication Date: 2014-09-17
    Description: Viral recombination is a key evolutionary mechanism, aiding escape from host immunity, contributing to changes in tropism and possibly assisting transmission across species barriers. The ability to determine whether recombination has occurred and to locate associated specific recombination junctions is thus of major importance in understanding emerging diseases and pathogenesis. This paper describes a method for determining recombinant mosaics (and their proportions) originating from two parent genomes, using high-throughput sequence data. The method involves setting the problem geometrically and the use of appropriately constrained quadratic programming. Recombinants of the honeybee deformed wing virus and the Varroa destructor virus-1 are inferred to illustrate the method from both siRNAs and reads sampling the viral genome population (cDNA library); our results are confirmed experimentally. Matlab software (MosaicSolver) is available.
    Keywords: Computational Methods, Genomics
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    Quantification of read species behavior within whole genome sequencing of cancer genomes for the stratification and visualization of genomic variation (2016)
    Oxford University Press
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    Publication Date: 2016-05-20
    Description: The cancer genome is abnormal genome, and the ability to monitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using 〉200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA). With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.
    Keywords: Computational Methods, Genomics
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    Circular RNA profile in gliomas revealed by identification tool UROBORUS (2016)
    Oxford University Press
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    Publication Date: 2016-05-20
    Description: Recent evidence suggests that many endogenous circular RNAs (circRNAs) may play roles in biological processes. However, the expression patterns and functions of circRNAs in human diseases are not well understood. Computationally identifying circRNAs from total RNA-seq data is a primary step in studying their expression pattern and biological roles. In this work, we have developed a computational pipeline named UROBORUS to detect circRNAs in total RNA-seq data. By applying UROBORUS to RNA-seq data from 46 gliomas and normal brain samples, we detected thousands of circRNAs supported by at least two read counts, followed by successful experimental validation on 24 circRNAs from the randomly selected 27 circRNAs. UROBORUS is an efficient tool that can detect circRNAs with low expression levels in total RNA-seq without RNase R treatment. The circRNAs expression profiling revealed more than 476 circular RNAs differentially expressed in control brain tissues and gliomas. Together with parental gene expression, we found that circRNA and its parental gene have diversified expression patterns in gliomas and control brain tissues. This study establishes an efficient and sensitive approach for predicting circRNAs using total RNA-seq data. The UROBORUS pipeline can be accessed freely for non-commercial purposes at http://uroborus.openbioinformatics.org/ .
    Keywords: Computational Methods, Genomics
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    Modeling the combined effect of RNA-binding proteins and microRNAs in post-transcriptional regulation (2016)
    Oxford University Press
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    Publication Date: 2016-05-20
    Description: Recent studies show that RNA-binding proteins (RBPs) and microRNAs (miRNAs) function in coordination with each other to control post-transcriptional regulation (PTR). Despite this, the majority of research to date has focused on the regulatory effect of individual RBPs or miRNAs. Here, we mapped both RBP and miRNA binding sites on human 3'UTRs and utilized this collection to better understand PTR. We show that the transcripts that lack competition for HuR binding are destabilized more after HuR depletion. We also confirm this finding for PUM1(2) by measuring genome-wide expression changes following the knockdown of PUM1(2) in HEK293 cells. Next, to find potential cooperative interactions, we identified the pairs of factors whose sites co-localize more often than expected by random chance. Upon examining these results for PUM1(2), we found that transcripts where the sites of PUM1(2) and its interacting miRNA form a stem-loop are more stabilized upon PUM1(2) depletion. Finally, using dinucleotide frequency and counts of regulatory sites as features in a regression model, we achieved an AU-ROC of 0.86 in predicting mRNA half-life in BEAS-2B cells. Altogether, our results suggest that future studies of PTR must consider the combined effects of RBPs and miRNAs, as well as their interactions.
    Keywords: Computational Methods, Genomics
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    Using intron position conservation for homology-based gene prediction (2016)
    Oxford University Press
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    Publication Date: 2016-05-20
    Description: Annotation of protein-coding genes is very important in bioinformatics and biology and has a decisive influence on many downstream analyses. Homology-based gene prediction programs allow for transferring knowledge about protein-coding genes from an annotated organism to an organism of interest. Here, we present a homology-based gene prediction program called GeMoMa. GeMoMa utilizes the conservation of intron positions within genes to predict related genes in other organisms. We assess the performance of GeMoMa and compare it with state-of-the-art competitors on plant and animal genomes using an extended best reciprocal hit approach. We find that GeMoMa often makes more precise predictions than its competitors yielding a substantially increased number of correct transcripts. Subsequently, we exemplarily validate GeMoMa predictions using Sanger sequencing. Finally, we use RNA-seq data to compare the predictions of homology-based gene prediction programs, and find again that GeMoMa performs well. Hence, we conclude that exploiting intron position conservation improves homology-based gene prediction, and we make GeMoMa freely available as command-line tool and Galaxy integration.
    Keywords: Computational Methods, Genomics
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    Comparison of circular RNA prediction tools (2016)
    Oxford University Press
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    Publication Date: 2016-04-08
    Description: CircRNAs are novel members of the non-coding RNA family. For several decades circRNAs have been known to exist, however only recently the widespread abundance has become appreciated. Annotation of circRNAs depends on sequencing reads spanning the backsplice junction and therefore map as non-linear reads in the genome. Several pipelines have been developed to specifically identify these non-linear reads and consequently predict the landscape of circRNAs based on deep sequencing datasets. Here, we use common RNAseq datasets to scrutinize and compare the output from five different algorithms; circRNA_finder, find_circ, CIRCexplorer, CIRI, and MapSplice and evaluate the levels of bona fide and false positive circRNAs based on RNase R resistance. By this approach, we observe surprisingly dramatic differences between the algorithms specifically regarding the highly expressed circRNAs and the circRNAs derived from proximal splice sites. Collectively, this study emphasizes that circRNA annotation should be handled with care and that several algorithms should ideally be combined to achieve reliable predictions.
    Keywords: Computational Methods, Genomics
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    Construction and characterization of three protein-targeting expression system in Lactobacillus casei (2016)
    Oxford University Press
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    Publication Date: 2016-03-13
    Description: We previously reported that the β-1,4-Mannanase ( manB ) gene from Bacillus pumilus functions as a good reporter gene in Lactobacillus casei . Two vectors were constructed. One carries the signal peptide of secretion protein Usp45 (SP Usp45 ) from Lactococcus lactis (pELSH), and the other carries the full-length S-layer protein, SlpA, from L. acidophilus (pELWH). In this work, another vector, pELSPH, was constructed to include the signal peptide of protein SlpA (SP SlpA ), and the capacity of all three vectors to drive expression of the manB gene in L. casei was evaluated. The results showed that SP Usp45 is functionally recognized and processed by the L. casei secretion machinery. The SP Usp45 -mediated secretion efficiency was ~87%, and SP SlpA drove the export of secreted ManB with ~80% efficiency. SP SlpA secretion was highly efficient, and expressed SlpA was anchored to the cell wall by an unknown secretion mechanism. Full-length SlpA drove the cell wall-anchored expression of an SlpA-ManB fusion protein but at a much lower level than that of protein SlpA.
    Keywords: Biotechnology & Synthetic Biology
    Print ISSN: 0378-1097
    Electronic ISSN: 1574-6968
    Topics: Biology
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    Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data (2016)
    Oxford University Press
    Details
    Publication Date: 2016-03-19
    Description: Background: Fusion transcripts are formed by either fusion genes (DNA level) or trans-splicing events (RNA level). They have been recognized as a promising tool for diagnosing, subtyping and treating cancers. RNA-seq has become a precise and efficient standard for genome-wide screening of such aberration events. Many fusion transcript detection algorithms have been developed for paired-end RNA-seq data but their performance has not been comprehensively evaluated to guide practitioners. In this paper, we evaluated 15 popular algorithms by their precision and recall trade-off, accuracy of supporting reads and computational cost. We further combine top-performing methods for improved ensemble detection. Results: Fifteen fusion transcript detection tools were compared using three synthetic data sets under different coverage, read length, insert size and background noise, and three real data sets with selected experimental validations. No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA. We further demonstrated the potential of a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation. Conclusion: Our result provides insightful recommendations when applying individual tool or combining top performers to identify fusion transcript candidates.
    Keywords: Computational Methods, Genomics
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    Hetero-oligomeric MspA pores in Mycobacterium smegmatis (2016)
    Oxford University Press
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    Publication Date: 2016-03-13
    Description: The porin MspA of Mycobacterium smegmatis is a biological nanopore used for DNA sequencing. The octameric MspA pore can be isolated from M. smegmatis in milligram quantities, is extremely stable against denaturation and rapidly inserts into lipid membranes. Here, we show that MspA pores composed of different Msp subunits are formed in M. smegmatis and that hetero-oligomers of different Msp monomers increase the heterogeneity of MspA pores designed for DNA sequencing. To improve the quality of preparations of mutant MspA proteins, all four msp genes were deleted from the M. smegmatis genome after insertion of an inducible porin gene from M. tuberculosis. In the msp quadruple mutant M. smegmatis ML712 no Msp porins were detected and mutant MspA proteins were produced at wild-type levels. Lipid bilayer experiments demonstrated that MspA pores isolated from ML712 formed functional channels and had a narrower conductance distribution than pores purified from M. smegmatis with background msp expression. Thus, the M. smegmatis msp quadruple mutant improves the homogeneity of MspA pores designed for DNA sequencing and might also facilitate the identification and functional characterization of other mycobacterial pore proteins.
    Keywords: Biotechnology & Synthetic Biology
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