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  • Articles  (22)
  • Transcriptome Mapping - Monitoring Gene Expression  (14)
  • Protein-nucleic acid interaction, Computational Methods  (8)
  • Oxford University Press  (22)
  • 2015-2019  (22)
  • 1985-1989
  • Biology  (22)
  • Chemistry and Pharmacology
  • 1
    Publication Date: 2016-06-21
    Description: RNA sequencing (RNAseq) has become the method of choice for transcriptome analysis, yet no consensus exists as to the most appropriate pipeline for its analysis, with current benchmarks suffering important limitations. Here, we address these challenges through a rich benchmarking resource harnessing (i) two RNAseq datasets including ERCC ExFold spike-ins; (ii) Nanostring measurements of a panel of 150 genes on the same samples; (iii) a set of internal, genetically-determined controls; (iv) a reanalysis of the SEQC dataset; and (v) a focus on relative quantification (i.e. across-samples). We use this resource to compare different approaches to each step of RNAseq analysis, from alignment to differential expression testing. We show that methods providing the best absolute quantification do not necessarily provide good relative quantification across samples, that count-based methods are superior for gene-level relative quantification, and that the new generation of pseudo-alignment-based software performs as well as established methods, at a fraction of the computing time. We also assess the impact of library type and size on quantification and differential expression analysis. Finally, we have created a R package and a web platform to enable the simple and streamlined application of this resource to the benchmarking of future methods.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 2
    Publication Date: 2016-05-06
    Description: Eukaryotic gene expression is regulated by transcription factors (TFs) binding to promoter as well as distal enhancers. TFs recognize short, but specific binding sites (TFBSs) that are located within the promoter and enhancer regions. Functionally relevant TFBSs are often highly conserved during evolution leaving a strong phylogenetic signal. While multiple sequence alignment (MSA) is a potent tool to detect the phylogenetic signal, the current MSA implementations are optimized to align the maximum number of identical nucleotides. This approach might result in the omission of conserved motifs that contain interchangeable nucleotides such as the ETS motif (IUPAC code: GGAW). Here, we introduce ConBind, a novel method to enhance alignment of short motifs, even if their mutual sequence similarity is only partial. ConBind improves the identification of conserved TFBSs by improving the alignment accuracy of TFBS families within orthologous DNA sequences. Functional validation of the Gfi1b + 13 enhancer reveals that ConBind identifies additional functionally important ETS binding sites that were missed by all other tested alignment tools. In addition to the analysis of known regulatory regions, our web tool is useful for the analysis of TFBSs on so far unknown DNA regions identified through ChIP-sequencing.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 3
    Publication Date: 2015-05-03
    Description: Cap analysis of gene expression (CAGE) is a high-throughput method for transcriptome analysis that provides a single base-pair resolution map of transcription start sites (TSS) and their relative usage. Despite their high resolution and functional significance, published CAGE data are still underused in promoter analysis due to the absence of tools that enable its efficient manipulation and integration with other genome data types. Here we present CAGEr , an R implementation of novel methods for the analysis of differential TSS usage and promoter dynamics, integrated with CAGE data processing and promoterome mining into a first comprehensive CAGE toolbox on a common analysis platform. Crucially, we provide collections of TSSs derived from most published CAGE datasets, as well as direct access to FANTOM5 resource of TSSs for numerous human and mouse cell/tissue types from within R, greatly increasing the accessibility of precise context-specific TSS data for integrative analyses. The CAGEr package is freely available from Bioconductor at http://www.bioconductor.org/packages/release/bioc/html/CAGEr.html .
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 4
    Publication Date: 2016-04-08
    Description: MicroRNAs (miRNAs), small non-coding RNA molecules, are important biomarkers for research and medical purposes. Here, we describe the development of a fast and simple method using highly fluorescent oligonucleotide-silver nanocluster probes (DNA/AgNCs) to efficiently detect specific miRNAs. Due to the great sequence diversity of miRNAs in humans and other organisms, a uniform strategy for miRNA detection is attractive. The concept presented is an oligonucleotide-based locking-to-unlocking system that can be endowed with miRNA complementarity while maintaining the same secondary structure. The locking-to-unlocking system is based on fold-back anchored DNA templates that consist of a cytosine-rich loop for AgNCs stabilization, an miRNA recognition site and an overlap region for hairpin stabilization. When an miRNA is recognized, fluorescence in the visible region is specifically extinguished in a concentration-dependent manner. Here, the exact composition of the fold-back anchor for the locking-to-unlocking system has been systematically optimized, balancing propensity for loop-structure formation, encapsulation of emissive AgNCs and target sensitivity. It is demonstrated that the applied strategy successfully can detect a number of cancer related miRNAs in RNA extracts from human cancer cell lines.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 5
    Publication Date: 2016-01-09
    Description: Recent comprehensive assessments of RNA-seq technology support its utility in quantifying gene expression in various samples. The next step of rigorously quantifying differences between sample groups, however, still lacks well-defined best practices. Although a number of advanced statistical methods have been developed, several studies demonstrate that their performance depends strongly on the data under analysis, which compromises practical utility in real biomedical studies. As a solution, we propose to use a data-adaptive procedure that selects an optimal statistic capable of maximizing reproducibility of detections. After demonstrating its improved sensitivity and specificity in a controlled spike-in study, the utility of the procedure is confirmed in a real biomedical study by identifying prognostic markers for clear cell renal cell carcinoma (ccRCC). In addition to identifying several genes previously associated with ccRCC prognosis, several potential new biomarkers among genes regulating cell growth, metabolism and solute transport were detected.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 6
    Publication Date: 2016-04-08
    Description: The variability and complexity of the transcription initiation process was examined by adapting RNA ligase-mediated rapid amplification of 5' cDNA ends (5'-RACE) to Next-Generation Sequencing (NGS). We oligo-labelled 5'-m 7 G-capped mRNA from two genes, the simple mono-exonic Beta-2-Adrenoceptor (ADRB2R) and the complex multi-exonic Glucocorticoid Receptor (GR, NR3C1) , and detected a variability in TSS location that has received little attention up to now. Transcription was not initiated at a fixed TSS, but from loci of 4 to 10 adjacent nucleotides. Individual TSSs had frequencies from 〈0.001% to 38.5% of the total gene-specific 5' m 7 G-capped transcripts. ADRB2R used a single locus consisting of 4 adjacent TSSs. Unstimulated, the GR used a total of 358 TSSs distributed throughout 38 loci, that were principally in the 5' UTRs and were spliced using established donor and acceptor sites. Complete demethylation of the epigenetically sensitive GR promoter with 5-azacytidine induced one new locus and 127 TSSs, 12 of which were unique. We induced GR transcription with dexamethasone and Interferon-, adding one new locus and 185 additional TSSs distributed throughout the promoter region. In-vitro the TSS microvariability regulated mRNA translation efficiency and the relative abundance of the different GR N-terminal protein isoform levels.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 7
    Publication Date: 2015-12-16
    Description: There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provide focus for the more costly and time consuming functional assays that follow. DNA-binding proteins are an important class of proteins that require annotation, but current computational methods are not applicable for genome wide predictions in plant species. Here, we explore the use of species and lineage specific models for the prediction of DNA-binding proteins in plants. We show that a species specific support vector machine model based on Arabidopsis sequence data is more accurate (accuracy 81%) than a generic model (74%), and based on this we develop a plant specific model for predicting DNA-binding proteins. We apply this model to the tomato proteome and demonstrate its ability to perform accurate high-throughput prediction of DNA-binding proteins. In doing so, we have annotated 36 currently uncharacterised proteins by assigning a putative DNA-binding function. Our model is publically available and we propose it be used in combination with existing tools to help increase annotation levels of DNA-binding proteins encoded in plant genomes.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 8
    Publication Date: 2016-09-03
    Description: Northern blots enable detection of a target RNA of interest in a biological sample using standard benchtop equipment. miRNAs are the most challenging targets as they must be detected with a single short nucleic acid probe. With existing approaches, it is cumbersome to perform multiplexed blots in which several RNAs are detected simultaneously, impeding the study of interacting regulatory elements. Here, we address this shortcoming by demonstrating multiplexed northern blotting based on the mechanism of hybridization chain reaction (HCR). With this approach, nucleic acid probes complementary to RNA targets trigger chain reactions in which fluorophore-labeled DNA hairpins self-assemble into tethered fluorescent amplification polymers. The programmability of HCR allows multiple amplifiers to operate simultaneously and independently within a blot, enabling straightforward multiplexing. We demonstrate simultaneous detection of three endogenous miRNAs in total RNA extracted from 293T and HeLa cells. For a given target, HCR signal scales linearly with target abundance, enabling relative and absolute quantitation. Using non-radioactive HCR, sensitive and selective miRNA detection is achieved using 2'OMe-RNA probes. The HCR northern blot protocol takes ~1.5 days independent of the number of target RNAs.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 9
    Publication Date: 2016-09-03
    Description: Although RNA-Seq data provide unprecedented isoform-level expression information, detection of alternative isoform regulation (AIR) remains difficult, particularly when working with an incomplete transcript annotation. We introduce JunctionSeq, a new method that builds on the statistical techniques used by the well-established DEXSeq package to detect differential usage of both exonic regions and splice junctions. In particular, JunctionSeq is capable of detecting differential usage of novel splice junctions without the need for an additional isoform assembly step, greatly improving performance when the available transcript annotation is flawed or incomplete. JunctionSeq also provides a powerful and streamlined visualization toolset that allows bioinformaticians to quickly and intuitively interpret their results. We tested our method on publicly available data from several experiments performed on the rat pineal gland and Toxoplasma gondii , successfully detecting known and previously validated AIR genes in 19 out of 19 gene-level hypothesis tests. Due to its ability to query novel splice sites, JunctionSeq is still able to detect these differences even when all alternative isoforms for these genes were not included in the transcript annotation. JunctionSeq thus provides a powerful method for detecting alternative isoform regulation even with low-quality annotations. An implementation of JunctionSeq is available as an R/Bioconductor package.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 10
    Publication Date: 2015-05-20
    Description: Single genes are often subject to alternative splicing, which generates alternative mature mRNAs. This phenomenon is widespread in animals, and observed in over 90% of human genes. Recent data suggest it may also be common in Apicomplexa. These parasites have small genomes, and economy of DNA is evolutionarily favoured in this phylum. We investigated the mechanism of alternative splicing in Toxoplasma gondii , and have identified and localized TgSR3, a homologue of ASF/SF2 (alternative-splicing factor/splicing factor 2, a serine-arginine–rich, or SR protein) to a subnuclear compartment. In addition, we conditionally overexpressed this protein, which was deleterious to growth. qRT-PCR was used to confirm perturbation of splicing in a known alternatively-spliced gene. We performed high-throughput RNA-seq to determine the extent of splicing modulated by this protein. Current RNA-seq algorithms are poorly suited to compact parasite genomes, and hence we complemented existing tools by writing a new program, GeneGuillotine, that addresses this deficiency by segregating overlapping reads into distinct genes. In order to identify the extent of alternative splicing, we released another program, JunctionJuror, that detects changes in intron junctions. Using this program, we identified about 2000 genes that were constitutively alternatively spliced in T. gondii . Overexpressing the splice regulator TgSR3 perturbed alternative splicing in over 1000 genes.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 11
    Publication Date: 2015-06-24
    Description: We describe a general binding score for predicting the nucleic acid binding probability in proteins. The score is directly derived from physicochemical and evolutionary features and integrates a residue neighboring network approach. Our process achieves stable and high accuracies on both DNA- and RNA-binding proteins and illustrates how the main driving forces for nucleic acid binding are common. Because of the effective integration of the synergetic effects of the network of neighboring residues and the fact that the prediction yields a hierarchical scoring on the protein surface, energy funnels for nucleic acid binding appear on protein surfaces, pointing to the dynamic process occurring in the binding of nucleic acids to proteins.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 12
    Publication Date: 2016-01-30
    Description: We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein–RNA interfaces to probe the binding hot spots at protein–RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein–protein and protein–RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental G values in a particular class, and provides a stepping-stone towards the engineering of protein–RNA recognition sites with desired affinity.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 13
    Publication Date: 2016-03-01
    Description: Most RNAs within polarized cells such as neurons are sorted subcellularly in a coordinated manner. Despite advances in the development of methods for profiling polyadenylated RNAs from small amounts of input RNA, techniques for profiling coding and non-coding RNAs simultaneously are not well established. Here, we optimized a transcriptome profiling method based on double-random priming and applied it to serially diluted total RNA down to 10 pg. Read counts of expressed genes were robustly correlated between replicates, indicating that the method is both reproducible and scalable. Our transcriptome profiling method detected both coding and long non-coding RNAs sized 〉300 bases. Compared to total RNAseq using a conventional approach our protocol detected 70% more genes due to reduced capture of ribosomal RNAs. We used our method to analyze the RNA composition of compartmentalized motoneurons. The somatodendritic compartment was enriched for transcripts with post-synaptic functions as well as for certain nuclear non-coding RNAs such as 7SK. In axons, transcripts related to translation were enriched including the cytoplasmic non-coding RNA 7SL. Our profiling method can be applied to a wide range of investigations including perturbations of subcellular transcriptomes in neurodegenerative diseases and investigations of microdissected tissue samples such as anatomically defined fiber tracts.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 14
    Publication Date: 2015-12-02
    Description: The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 15
    Publication Date: 2015-12-02
    Description: As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 16
    Publication Date: 2015-01-10
    Description: Methods that permit controlled changes in the expression of genes are important tools for biological and medical research, and for biotechnological applications. Conventional methods are directed at individually changing each gene, its regulatory elements or its mRNA's translation rate. We demonstrate that the CRISPR-associated DNA-binding Cascade complex can be used for efficient, long-lasting and programmable gene silencing. When Cascade is targeted to a promoter sequence the transcription of the downstream gene is inhibited, resulting in dramatically reduced expression. The specificity of Cascade binding is provided by the integral crRNA component, which is easily designed to target virtually any stretch of DNA. Cascade targeted to the ORF sequence of the gene can also silence expression, albeit at lower efficiency. The system can be used to silence plasmid and chromosome targets, simultaneously target several genes and is active in different bacterial species and strains. The findings described here are an addition to the expanding range of CRISPR-based technologies and may be adapted to additional organisms and cell systems.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 17
    Publication Date: 2016-05-20
    Description: It is well established that the correct identification of the messenger RNA targeted by a given microRNA (miRNA) is a difficult problem, and that available methods all suffer from low specificity. We hypothesize that the correct identification of the pairing should take into account the effect of the Argonaute protein (AGO), an essential catalyst of the recognition process. Therefore, we developed a strategy named MiREN for building and scoring three-dimensional models of the ternary complex formed by AGO, a miRNA and 22 nt of a target mRNA that putatively interacts with it. We show here that MiREN can be used to assess the likelihood that an RNA molecule is the target of a given miRNA and that this approach is more accurate than other existing methods, usually based on sequence or sequence-related features. Our results also suggest that AGO plays a relevant role in the selection of the miRNA targets. Our method can represent an additional step for refining predictions made by faster but less accurate classical methods for the identification of miRNA targets.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 18
    Publication Date: 2016-05-20
    Description: Computational techniques have been used to design a novel class of RNA architecture with expected improved resistance to nuclease degradation, while showing interference RNA activity. The in silico designed structure consists of a 24–29 bp duplex RNA region linked on both ends by N-alkyl-N dimeric nucleotides (BCn dimers; n = number of carbon atoms of the alkyl chain). A series of N-alkyl-N capped dumbbell-shaped structures were efficiently synthesized by double ligation of BCn-loop hairpins. The resulting BCn-loop dumbbells displayed experimentally higher biostability than their 3'-N-alkyl-N linear version, and were active against a range of mRNA targets. We studied first the effect of the alkyl chain and stem lengths on RNAi activity in a screen involving two series of dumbbell analogues targeting Renilla and Firefly luciferase genes. The best dumbbell design (containing BC6 loops and 29 bp) was successfully used to silence GRB7 expression in HER2+ breast cancer cells for longer periods of time than natural siRNAs and known biostable dumbbells. This BC6-loop dumbbell-shaped structure displayed greater anti-proliferative activity than natural siRNAs.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 19
    Publication Date: 2016-10-14
    Description: mRNA expression involves transcription initiation, elongation and degradation. In cells, these dynamic processes are highly regulated. However, experimental characterization of the dynamic processes in vivo is difficult due to the paucity of methods capable of direct measurements. We present a highly sensitive and versatile method enabling direct characterization of the dynamic processes. Our method is based on single-molecule fluorescence in situ hybridization (smFISH) and quantitative analyses of hybridization signals. We hybridized multiple probes labelled with spectrally distinct fluorophores to multiple sub-regions of single mRNAs, and visualized the kinetics of synthesis and degradation of the sub-regions. Quantitative analyses of the data lead to absolute quantification of the lag time of mRNA induction (the time it takes for external signals to activate transcription initiation), transcription initiation rate, transcription elongation speed (i.e. mRNA chain-growth speed), the rate of premature termination of transcripts and degradation rates. Applying our method to three different biological problems, we demonstrated how our method may be applicable to reveal dynamics of mRNA expression that was difficult to study previously. We expect such absolute quantification can greatly facilitate understanding of gene expression and its regulation working at the levels of transcriptional initiation, elongation and degradation.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 20
    Publication Date: 2016-12-04
    Description: Current approaches to profiling tissue-specific gene expression in C. elegans require delicate manipulation and are difficult under certain conditions, e.g. from dauer or aging worms. We have developed an easy and robust method for tissue-specific RNA-seq by taking advantage of the endogenous trans-splicing process. In this method, transgenic worms are generated in which a spliced leader (SL) RNA gene is fused with a sequence tag and driven by a tissue-specific promoter. Only in the tissue of interest, the tagged SL RNA gene is transcribed and then trans-spliced onto mRNAs. The tag allows enrichment and sequencing of mRNAs from that tissue only. As a proof of principle, we profiled the muscle transcriptome, which showed high coverage and efficient enrichment of muscle specific genes, with low background noise. To demonstrate the robustness of our method, we profiled muscle gene expression in dauer larvae and aging worms, revealing gene expression changes consistent with the physiology of these stages. The resulting muscle transcriptome also revealed 461 novel RNA transcripts, likely muscle-expressed long non-coding RNAs. In summary, the splicing-based RNA tagging (SRT) method provides a convenient and robust tool to profile trans-spliced genes and identify novel transcripts in a tissue-specific manner, with a low false positive rate.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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  • 21
    Publication Date: 2016-01-09
    Description: Transcription factors (TF) can change shape to bind and recognize DNA, shifting the energy landscape from a weak binding, rapid search mode to a higher affinity recognition mode. However, the mechanism(s) driving this conformational change remains unresolved and in most cases high-resolution structures of the non-specific complexes are unavailable. Here, we investigate the conformational switch of the human mitochondrial transcription termination factor MTERF1, which has a modular, superhelical topology complementary to DNA. Our goal was to characterize the details of the non-specific search mode to complement the crystal structure of the specific binding complex, providing a basis for understanding the recognition mechanism. In the specific complex, MTERF1 binds a significantly distorted and unwound DNA structure, exhibiting a protein conformation incompatible with binding to B-form DNA. In contrast, our simulations of apo MTERF1 revealed significant flexibility, sampling structures with superhelical pitch and radius complementary to the major groove of B-DNA. Docking these structures to B-DNA followed by unrestrained MD simulations led to a stable complex in which MTERF1 was observed to undergo spontaneous diffusion on the DNA. Overall, the data support an MTERF1-DNA binding and recognition mechanism driven by intrinsic dynamics of the MTERF1 superhelical topology.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 22
    Publication Date: 2016-12-04
    Description: Myoblasts are precursor skeletal muscle cells that differentiate into fused, multinucleated myotubes. Current single-cell microfluidic methods are not optimized for capturing very large, multinucleated cells such as myotubes. To circumvent the problem, we performed single-nucleus transcriptome analysis. Using immortalized human myoblasts, we performed RNA-seq analysis of single cells (scRNA-seq) and single nuclei (snRNA-seq) and found them comparable, with a distinct enrichment for long non-coding RNAs (lncRNAs) in snRNA-seq. We then compared snRNA-seq of myoblasts before and after differentiation. We observed the presence of mononucleated cells (MNCs) that remained unfused and analyzed separately from multi-nucleated myotubes. We found that while the transcriptome profiles of myoblast and myotube nuclei are relatively homogeneous, MNC nuclei exhibited significant heterogeneity, with the majority of them adopting a distinct mesenchymal state. Primary transcripts for microRNAs (miRNAs) that participate in skeletal muscle differentiation were among the most differentially expressed lncRNAs, which we validated using NanoString. Our study demonstrates that snRNA-seq provides reliable transcriptome quantification for cells that are otherwise not amenable to current single-cell platforms. Our results further indicate that snRNA-seq has unique advantage in capturing nucleus-enriched lncRNAs and miRNA precursors that are useful in mapping and monitoring differential miRNA expression during cellular differentiation.
    Keywords: Transcriptome Mapping - Monitoring Gene Expression
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