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  • Computational Methods, Massively Parallel (Deep) Sequencing, Genomics  (16)
  • Computational Methods  (11)
  • Oxford University Press  (27)
  • Copernicus
  • Periodicals Archive Online (PAO)
  • Springer
  • 2010-2014  (27)
  • 1960-1964
  • 1
    Publication Date: 2012-03-29
    Description: Next-generation sequencing (NGS) technologies-based transcriptomic profiling method often called RNA-seq has been widely used to study global gene expression, alternative exon usage, new exon discovery, novel transcriptional isoforms and genomic sequence variations. However, this technique also poses many biological and informatics challenges to extracting meaningful biological information. The RNA-seq data analysis is built on the foundation of high quality initial genome localization and alignment information for RNA-seq sequences. Toward this goal, we have developed RNASEQR to accurately and effectively map millions of RNA-seq sequences. We have systematically compared RNASEQR with four of the most widely used tools using a simulated data set created from the Consensus CDS project and two experimental RNA-seq data sets generated from a human glioblastoma patient. Our results showed that RNASEQR yields more accurate estimates for gene expression, complete gene structures and new transcript isoforms, as well as more accurate detection of single nucleotide variants (SNVs). RNASEQR analyzes raw data from RNA-seq experiments effectively and outputs results in a manner that is compatible with a wide variety of specialized downstream analyses on desktop computers.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 2
    Publication Date: 2014-08-15
    Description: High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A , a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor.
    Keywords: Computational Methods
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 3
    Publication Date: 2013-10-19
    Description: Identifying variants using high-throughput sequencing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical artifact results from incorrectly aligning experimentally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We developed BlackOPs, an open-source tool that simulates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklists contain thousands of artifact variants that are indistinguishable from true variants and, for a given sample, are expected to be almost completely false positives. We show that these blacklist positions are specific to the alignment algorithm and read length used, and BlackOPs allows users to generate a blacklist specific to their experimental setup. We queried the dbSNP and COSMIC variant databases and found numerous variants indistinguishable from mapping errors. We demonstrate how filtering against blacklist positions reduces the number of potential false variants using an RNA-seq glioblastoma cell line data set. In summary, accounting for mapping-caused variants tuned to experimental setups reduces false positives and, therefore, improves genome characterization by high-throughput sequencing.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 4
    Publication Date: 2013-04-02
    Description: MicroRNAs (miRNAs) constitute an important class of small regulatory RNAs that are derived from distinct hairpin precursors (pre-miRNAs). In contrast to mature miRNAs, which have been characterized in numerous genome-wide studies of different organisms, research on global profiling of pre-miRNAs is limited. Here, using massive parallel sequencing, we have performed global characterization of both mouse mature and precursor miRNAs. In total, 87 369 704 and 252 003 sequencing reads derived from 887 mature and 281 precursor miRNAs were obtained, respectively. Our analysis revealed new aspects of miRNA/pre-miRNA processing and modification, including eight Ago2-cleaved pre-miRNAs, eight new instances of miRNA editing and exclusively 5' tailed mirtrons. Furthermore, based on the sequences of both mature and precursor miRNAs, we developed a miRNA discovery pipeline, miRGrep, which does not rely on the availability of genome reference sequences. In addition to 239 known mouse pre-miRNAs, miRGrep predicted 41 novel ones with high confidence. Similar as known ones, the mature miRNAs derived from most of these novel loci showed both reduced abundance following Dicer knockdown and the binding with Argonaute2. Evaluation on data sets obtained from Caenorhabditis elegans and Caenorhabditis sp.11 demonstrated that miRGrep could be widely used for miRNA discovery in metazoans, especially in those without genome reference sequences.
    Keywords: Computational Methods
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    Topics: Biology
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  • 5
    Publication Date: 2013-09-26
    Description: It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. This study developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense–antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan. CNCI software is available at http://www.bioinfo.org/software/cnci .
    Keywords: Computational Methods
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    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 6
    Publication Date: 2014-12-17
    Description: Combinatorial transcription factor (TF) binding is essential for cell-type-specific gene regulation. However, much remains to be learned about the mechanisms of TF interactions, including to what extent constrained spacing and orientation of interacting TFs are critical for regulatory element activity. To examine the relative prevalence of the ‘enhanceosome’ versus the ‘TF collective’ model of combinatorial TF binding, a comprehensive analysis of TF binding site sequences in large scale datasets is necessary. We developed a motif-pair discovery pipeline to identify motif co-occurrences with preferential distance(s) between motifs in TF-bound regions. Utilizing a compendium of 289 mouse haematopoietic TF ChIP-seq datasets, we demonstrate that haematopoietic-related motif-pairs commonly occur with highly conserved constrained spacing and orientation between motifs. Furthermore, motif clustering revealed specific associations for both heterotypic and homotypic motif-pairs with particular haematopoietic cell types. We also showed that disrupting the spacing between motif-pairs significantly affects transcriptional activity in a well-known motif-pair—E-box and GATA, and in two previously unknown motif-pairs with constrained spacing—Ets and Homeobox as well as Ets and E-box. In this study, we provide evidence for widespread sequence-specific TF pair interaction with DNA that conforms to the ‘enhanceosome’ model, and furthermore identify associations between specific haematopoietic cell-types and motif-pairs.
    Keywords: Computational Methods
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    Topics: Biology
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  • 7
    Publication Date: 2014-12-17
    Description: Homologous non-coding RNAs frequently exhibit domain insertions, where a branch of secondary structure is inserted in a sequence with respect to its homologs. Dynamic programming algorithms for common secondary structure prediction of multiple RNA homologs, however, do not account for these domain insertions. This paper introduces a novel dynamic programming algorithm methodology that explicitly accounts for the possibility of inserted domains when predicting common RNA secondary structures. The algorithm is implemented as Dynalign II, an update to the Dynalign software package for predicting the common secondary structure of two RNA homologs. This update is accomplished with negligible increase in computational cost. Benchmarks on ncRNA families with domain insertions validate the method. Over base pairs occurring in inserted domains, Dynalign II improves accuracy over Dynalign, attaining 80.8% sensitivity (compared with 14.4% for Dynalign) and 91.4% positive predictive value (PPV) for tRNA; 66.5% sensitivity (compared with 38.9% for Dynalign) and 57.0% PPV for RNase P RNA; and 50.1% sensitivity (compared with 24.3% for Dynalign) and 58.5% PPV for SRP RNA. Compared with Dynalign, Dynalign II also exhibits statistically significant improvements in overall sensitivity and PPV. Dynalign II is available as a component of RNAstructure, which can be downloaded from http://rna.urmc.rochester.edu/RNAstructure.html .
    Keywords: Computational Methods
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    Topics: Biology
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  • 8
    Publication Date: 2012-10-10
    Description: Several bioinformatics methods have been proposed for the detection and characterization of genomic structural variation (SV) from ultra high-throughput genome resequencing data. Recent surveys show that comprehensive detection of SV events of different types between an individual resequenced genome and a reference sequence is best achieved through the combination of methods based on different principles (split mapping, reassembly, read depth, insert size, etc.). The improvement of individual predictors is thus an important objective. In this study, we propose a new method that combines deviations from expected library insert sizes and additional information from local patterns of read mapping and uses supervised learning to predict the position and nature of structural variants. We show that our approach provides greatly increased sensitivity with respect to other tools based on paired end read mapping at no cost in specificity, and it makes reliable predictions of very short insertions and deletions in repetitive and low-complexity genomic contexts that can confound tools based on split mapping of reads.
    Keywords: Computational Methods, Massively Parallel (Deep) Sequencing, Genomics
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  • 9
    Publication Date: 2012-07-22
    Description: Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this ‘main’ SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.
    Keywords: Computational Methods
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  • 10
    Publication Date: 2012-07-22
    Description: Phase variation of surface structures occurs in diverse bacterial species due to stochastic, high frequency, reversible mutations. Multiple genes of Campylobacter jejuni are subject to phase variable gene expression due to mutations in polyC/G tracts. A modal length of nine repeats was detected for polyC/G tracts within C. jejuni genomes. Switching rates for these tracts were measured using chromosomally-located reporter constructs and high rates were observed for cj1139 (G8) and cj0031 (G9). Alteration of the cj1139 tract from G8 to G11 increased mutability 10-fold and changed the mutational pattern from predominantly insertions to mainly deletions. Using a multiplex PCR, major changes were detected in ‘on/off’ status for some phase variable genes during passage of C. jejuni in chickens. Utilization of observed switching rates in a stochastic, theoretical model of phase variation demonstrated links between mutability and genetic diversity but could not replicate observed population diversity. We propose that modal repeat numbers have evolved in C. jejuni genomes due to molecular drivers associated with the mutational patterns of these polyC/G repeats, rather than by selection for particular switching rates, and that factors other than mutational drift are responsible for generating genetic diversity during host colonization by this bacterial pathogen.
    Keywords: Computational Methods
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    Topics: Biology
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