RNA-seq Expression Data Research Articles

Glbase: a framework for combining, analyzing and displaying heterogeneous genomic and high-throughput sequencing data.

Genomic datasets and the tools to analyze them have proliferated at an astonishing rate. However, such tools are often poorly integrated with each other: each program typically produces its own custom output in a variety of non-standard file formats. Here we present glbase, a framework that uses a flexible set of descriptors that can quickly parse non-binary data files. glbase includes many functions to intersect two lists of data, including operations on genomic interval data and support for the efficient random access to huge genomic data files. Many glbase functions can produce graphical outputs, including scatter plots, heatmaps, boxplots and other common analytical displays of high-throughput data such as RNA-seq, ChIP-seq and microarray expression data. glbase is designed to rapidly bring biological data into a Python-based analytical environment to facilitate analysis and data processing. In summary, glbase is a flexible and multifunctional toolkit that allows the combination and analysis of high-throughput data (especially next-generation sequencing and genome-wide data), and which has been instrumental in the analysis of complex data sets. glbase is freely available at http://bitbucket.org/oaxiom/glbase/.

DEXUS: identifying differential expression in RNA-Seq studies with unknown conditions

Detection of differential expression in RNA-Seq data is currently limited to studies in which two or more sample conditions are known a priori. However, these biological conditions are typically unknown in cohort, cross-sectional and nonrandomized controlled studies such as the HapMap, the ENCODE or the 1000 Genomes project. We present DEXUS for detecting differential expression in RNA-Seq data for which the sample conditions are unknown. DEXUS models read counts as a finite mixture of negative binomial distributions in which each mixture component corresponds to a condition. A transcript is considered differentially expressed if modeling of its read counts requires more than one condition. DEXUS decomposes read count variation into variation due to noise and variation due to differential expression. Evidence of differential expression is measured by the informative/noninformative (I/NI) value, which allows differentially expressed transcripts to be extracted at a desired specificity (significance level) or sensitivity (power). DEXUS performed excellently in identifying differentially expressed transcripts in data with unknown conditions. On 2400 simulated data sets, I/NI value thresholds of 0.025, 0.05 and 0.1 yielded average specificities of 92, 97 and 99% at sensitivities of 76, 61 and 38%, respectively. On real-world data sets, DEXUS was able to detect differentially expressed transcripts related to sex, species, tissue, structural variants or quantitative trait loci. The DEXUS R package is publicly available from Bioconductor and the scripts for all experiments are available at http://www.bioinf.jku.at/software/dexus/.

Computational Identification of Diverse Mechanisms Underlying Transcription Factor-DNA Occupancy

ChIP-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. This has led to great interest in the underlying biochemical mechanisms that direct TF-DNA binding, with the ultimate goal of computationally predicting a TF's occupancy profile in any cellular condition. In this study, we examined the influence of various potential determinants of TF-DNA binding on a much larger scale than previously undertaken. We used a thermodynamics-based model of TF-DNA binding, called “STAP,” to analyze 45 TF-ChIP data sets from Drosophila embryonic development. We built a cross-validation framework that compares a baseline model, based on the ChIP'ed (“primary”) TF's motif, to more complex models where binding by secondary TFs is hypothesized to influence the primary TF's occupancy. Candidates interacting TFs were chosen based on RNA-SEQ expression data from the time point of the ChIP experiment. We found widespread evidence of both cooperative and antagonistic effects by secondary TFs, and explicitly quantified these effects. We were able to identify multiple classes of interactions, including (1) long-range interactions between primary and secondary motifs (separated by ≤150 bp), suggestive of indirect effects such as chromatin remodeling, (2) short-range interactions with specific inter-site spacing biases, suggestive of direct physical interactions, and (3) overlapping binding sites suggesting competitive binding. Furthermore, by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility, we were able to categorize the effects into those that are likely to be mediated by the secondary TF's effect on local accessibility and those that utilize accessibility-independent mechanisms. Finally, we conducted in vitro pull-down assays to test model-based predictions of short-range cooperative interactions, and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA.

Abstract B40: Methylation signatures specific to triple negative breast cancer subtypes

Abstract Objective: We identify methylation signatures specific to triple negative breast cancer (TNBC) subtypes using publicly available data, and examine their association with expression. Materials and Methods: DNA methylation, RNAseq expression and clinical data from breast invasive carcinoma patients were obtained from The Cancer Genome Atlas (TCGA) Data portal. The classification of each patient sample as TNBC was defined according to a combination of FISH and/or IHC results for ER, PR and HER2 negativity. Each TNBC specimen was classified into one of the following molecular subtypes by applying the gene expression-based model of Lehman (2011): basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (MES), mesenchymal stem-like (MSL), luminal androgen receptor (LAR), and unstable (UNS). Among the 116 identified TNBC specimens, 61 had level-three data available on both methylation and RNAseq expression. For methylation, beta values were extracted based on Illumina's Infinium Human DNA Methylation 450K platform; for expression, RNAseqV2 Expectation/Maximization normalized counts (RSEM: Li and Dewey, 2011) were extracted on 20,531 genes and a logarithmic transformation applied. Data were filtered to identify methylation changes specific to each TNBC subtype based on a novel, generalized statistic (Switchenko and Kowalski, 2013) and tested for median difference between subtypes using p < 0.10 to define significance. Results: Among the 61 TNBC samples, the following molecular subtypes were represented: BL1 (n=6), BL2 (n=6), IM (n=12), LAR (n=5), MES (n=15), MSL (n=4), and UNS (n=13). A number of genes were determined to be hypermethylated with corresponding decreased expression by RNAseq in several subtypes. Methylated genes specific to the LAR TNBC subtype included IGF1R (61% LAR vs. 2% other) and N4BP2L1 (44% LAR vs. 3% other). Methylated genes in the MES subtype included ACSL5 (80% vs. 50%), RHOH (82% vs. 6%) and OAS2 (26% vs. 7%), and specific to the BL2 subtype were EN1 (35% vs. 8%) and TRAFIP3 (60% vs. 35%). Two hypomethylated genes, PIK3CD and CD69, associated with respective subtypes, MSL and IM, showed corresponding increased expression levels. Conclusion: Our results provide further insight into the molecular heterogeneity surrounding TNBC patients by examining methylation changes specific to molecular subtypes, which may be further examined for their effect on clinical outcome. Citation Format: Jeanne Kowalski, Jeffery M. Switchenko, Bhakti Dwivedi, Dana Nickleach, Carlos S. Moreno. Methylation signatures specific to triple negative breast cancer subtypes. [abstract]. In: Proceedings of the AACR Special Conference on Chromatin and Epigenetics in Cancer; Jun 19-22, 2013; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2013;73(13 Suppl):Abstract nr B40.

Dictyostelium Development Shows a Novel Pattern of Evolutionary Conservation

von Baer's law states that early stages of animal development are the most conserved. More recent evidence supports a modified "hourglass" pattern in which an early but somewhat later stage is most conserved. Both patterns have been explained by the relative complexity of either temporal or spatial interactions; the greatest conservation and lowest evolvability occur at the time of the most complex interactions, because these cause larger effects that are harder for selection to alter. This general kind of explanation might apply universally across independent multicellular systems, as supported by the recent finding of the hourglass pattern in plants. We use RNA-seq expression data from the development of the slime mold Dictyostelium to demonstrate that it does not follow either of the two canonical patterns but instead tends to show the strongest conservation and weakest evolvability late in development. We propose that this is consistent with a version of the spatial constraints model, modified for organisms that never achieve a high degree of developmental modularity.

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

Guide: a desktop application for analysing gene expression data.

BackgroundMultiplecompeting bioinformatics tools exist for next-generation sequencing data analysis. Many of these tools are available as R/Bioconductor modules, and it can be challenging for the bench biologist without any programming background to quickly analyse genomics data. Here, we present an application that is designed to be simple to use, while leveraging the power of R as the analysis engine behind the scenes.ResultsGenome Informatics Data Explorer (Guide) is a desktop application designed for the bench biologist to analyse RNA-seq and microarray gene expression data. It requires a text file of summarised read counts or expression values as input data, and performs differential expression analyses at both the gene and pathway level. It uses well-established R/Bioconductor packages such as limma for its analyses, without requiring the user to have specific knowledge of the underlying R functions. Results are presented in figures or interactive tables which integrate useful data from multiple sources such as gene annotation and orthologue data. Advanced options include the ability to edit R commands to customise the analysis pipeline.ConclusionsGuide is a desktop application designed to query gene expression data in a user-friendly way while automatically communicating with R. Its customisation options make it possible to use different bioinformatics tools available through R/Bioconductor for its analyses, while keeping the core usage simple. Guide is written in the cross-platform framework of Qt, and is freely available for use from http://guide.wehi.edu.au.

Integrating GWAS and Expression Data for Functional Characterization of Disease-Associated SNPs: An Application to Follicular Lymphoma

Development of post-GWAS (genome-wide association study) methods are greatly needed for characterizing the function of trait-associated SNPs. Strategies integrating various biological data sets with GWAS results will provide insights into the mechanistic role of associated SNPs. Here, we present a method that integrates RNA sequencing (RNA-seq) and allele-specific expression data with GWAS data to further characterize SNPs associated with follicular lymphoma (FL). We investigated the influence on gene expression of three established FL-associated loci-rs10484561, rs2647012, and rs6457327-by measuring their correlation with human-leukocyte-antigen (HLA) expression levels obtained from publicly available RNA-seq expression data sets from lymphoblastoid cell lines. Our results suggest that SNPs linked to the protective variant rs2647012 exert their effect by a cis-regulatory mechanism involving modulation of HLA-DQB1 expression. In contrast, no effect on HLA expression was observed for the colocalized risk variant rs10484561. The application of integrative methods, such as those presented here, to other post-GWAS investigations will help identify causal disease variants and enhance our understanding of biological disease mechanisms.

Digital gene expression data, cross-species conservation and noncoding RNA

http://www.lirmm.fr/~rivalRecently developed sequencing technologies offer massively parallel production of short reads and become the technology of choice for a variety of sequencing-based assays, including gene expression. Among them, digital gene expression analysis (DGE), which combines generation of short tag signatures for cellular transcripts with massively parallel sequencing, offers a large dynamic range to detect transcripts and is limited only by sequencing depth. As recently described (Philippe et al, 2009), tag signatures can easily be mapped to a reference genome and used to perform gene discovery. This procedure distinguishes between transcripts originating from both DNA strands and categorizes tags corresponding to protein coding gene (CDS and 3’UTR), antisense, intronic or intergenic transcribed regions. Here, we have applied an integrated bioinformatics approach to investigate tags’ properties, including cross-species conservation, and the ability to reveal novel transcripts located outside the boundaries of known protein or RNA coding genes. We mapped the tags from a human DGE library obtained with Solexa sequencing, against the human, chimpanzee, and mouse genomic sequences. We considered the subset of uniquely mapped tags in the human genome, and given their genomic location, determined according to Ensembl if they fall within a region annotated by a gene (CDS, UTR and intron) or an intergenic region. We found that 76.4 % of the tags located in human also matched to the chimpanzee genome. The level of conservation between human and chimpanzee varied among annotation categories: 85 % of conserved tags in the CDS, 81 % in the UTR, 76% and 73% respectively in intron and intergenic regions. With the same procedure applied to human and mouse, we obtained 11% of conserved tags in the CDS, 7% in UTR, 1% and 3% respectively in intronic and intergenic regions. We analysed in depth the common CDS and UTR tags in human and mouse for their functional relevance: 90% of them correspond to orthologous genes with a common HUGO. We used DAVID database to extract biological features, the gene clustering revealed specific molecular functions belonging to transcription cofactor and regulator activity, nucleotide binding, ligase and protein kinase, hormone receptor, histone methyltransferase or GTPase activity, and also important signaling pathways like WNT pathway. Indeed, intergenic transcription includes mainly new, non protein-coding RNAs (npcRNAs), which could represent an important class of regulatory molecules. By integrating also SAGE genie and RNA-seq expression data, we selected intergenic tags conserved across species and assayed experimentally the npcRNA transcriptome with Q-PCR. We validated 80% of the 32 tested biological cases. These results demonstrate that considering tag conservation helps to identify conserved genes and functions, which is of great relevance when investigating expressed tags located in intergenic regions.

Expression divergence measured by transcriptome sequencing of four yeast species

BackgroundThe evolution of gene expression is a challenging problem in evolutionary biology, for which accurate, well-calibrated measurements and methods are crucial.ResultsWe quantified gene expression with whole-transcriptome sequencing in four diploid, prototrophic strains of Saccharomyces species grown under the same condition to investigate the evolution of gene expression. We found that variation in expression is gene-dependent with large variations in each gene's expression between replicates of the same species. This confounds the identification of genes differentially expressed across species. To address this, we developed a statistical approach to establish significance bounds for inter-species differential expression in RNA-Seq data based on the variance measured across biological replicates. This metric estimates the combined effects of technical and environmental variance, as well as Poisson sampling noise by isolating each component. Despite a paucity of large expression changes, we found a strong correlation between the variance of gene expression change and species divergence (R2 = 0.90).ConclusionWe provide an improved methodology for measuring gene expression changes in evolutionary diverged species using RNA Seq, where experimental artifacts can mimic evolutionary effects.GEO Accession Number: GSE32679

Abstract A207: Whole transcriptome profiling in upper aerodigestive tract cancers

Abstract Background: RNA-seq is a powerful technology that allows to obtain sequence and expression information simultaneously. We applied this technology to 4 upper aerodigestive tract cancers. Methods: RNA from four upper aerodigestive tract tumor specimens was extracted and sequencing libraries constructed. Samples were analyzed using an Illumina Genome Analyzer with a paired end module (36 or 54 base read length). Raw data was processed with a proprietary data pipeline. Potential mutations were filtered using both in house and external bioinformatic solutions, involving previously established databases of mutations (COSMIC) and SNPs (dbSNP), and removal of ancestral alleles identified from multiple sequence alignments. These SNPs were then parsed via in house scripts to determine whether the SNPs were present in coding regions, 3′UTR, 5′UTR, or in splice acceptor/donor sites. The coding SNPs were further parsed to determine which SNPs result in non-synonymous changes. RNA-Seq expression data was analyzed using Partek Genomics Suite and compared to microarray based expression data. Pathway analysis was performed using GeneGO Metacore and DAVID. Results: 500Mb to 2GB of data were obtained per sample. Between 700 and 2000 nsSNPs were identified, as well as a large number of alteration in the 3′ and 5′ untranslated regions. Genetic alterations in several commonly mutated genes were identified including TP53, ErbB2, H-RAS, and Cyclin D1. SNPs were enriched in pathways commonly involved in upper aerodigestive tract tumors including cell cycle control, cytoskeleton, and receptor tyrosine kinases. Furthermore we screened for viral sequences identifying one sample that was HPV16 positive. RNA-seq based and microarray based expression data generally correlated, but significant differences were also observed, that were at least partly due to the limited number of RNA-seq reads. Conclusion: Cancer transcriptome sequencing is a promising and cost effective approach for identifying mutations and obtaining expression analysis simultaneously. Transcriptome sequencing holds promise as a readily available platform for assessing potential treatment targets in a specific tumor. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A207.