Public RNA Sequencing Data Research Articles

SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis

Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy ( source code: https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in markdown language, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

Dendritic potassium channel dysfunction may contribute to dendrite degeneration in spinocerebellar ataxia type 1.

Purkinje neuron dendritic degeneration precedes cell loss in cerebellar ataxia, but the basis for dendritic vulnerability in ataxia remains poorly understood. Recent work has suggested that potassium (K+) channel dysfunction and consequent spiking abnormalities contribute to Purkinje neuron degeneration, but little attention has been paid to how K+ channel dysfunction impacts dendritic excitability and the role this may play in the degenerative process. We examined the relationship between K+ channel dysfunction, dendritic excitability and dendritic degeneration in spinocerebellar ataxia type 1 (SCA1). Examination of published RNA sequencing data from SCA1 mice revealed reduced expression of several K+ channels that are important regulators of excitability in Purkinje neuron dendrites. Patch clamp recordings in Purkinje neurons from SCA1 mice identified increased dendritic excitability in the form of enhanced back-propagation of action potentials and an increased propensity to produce dendritic calcium spikes. Dendritic excitability could be rescued by restoring expression of large-conductance calcium-activated potassium (BK) channels and activating other K+ channels with baclofen. Importantly, this treatment combination improves motor performance and mitigates dendritic degeneration in SCA1 mice. These results suggest that reduced expression of K+ channels results in persistently increased dendritic excitability at all stages of disease in SCA1, which in turn may contribute to the dendritic degeneration that precedes cell loss.

SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis

Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy ( source code: https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in Jupyter Notebook, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

Identification of the group IIa WRKY subfamily and the functional analysis of GhWRKY17 in upland cotton (Gossypium hirsutum L.).

WRKY transcription factors play important roles in plant defense, stress response, leaf senescence, and plant growth and development. Previous studies have revealed the important roles of the group IIa GhWRKY genes in cotton. To comprehensively analyze the group IIa GhWRKY genes in upland cotton, we identified 15 candidate group IIa GhWRKY genes in the Gossypium hirsutum genome. The phylogenetic tree, intron-exon structure, motif prediction and Ka/Ks analyses indicated that most group IIa GhWRKY genes shared high similarity and conservation and underwent purifying selection during evolution. In addition, we detected the expression patterns of several group IIa GhWRKY genes in individual tissues as well as during leaf senescence using public RNA sequencing data and real-time quantitative PCR. To better understand the functions of group IIa GhWRKYs in cotton, GhWRKY17 (KF669857) was isolated from upland cotton, and its sequence alignment, promoter cis-acting elements and subcellular localization were characterized. Moreover, the over-expression of GhWRKY17 in Arabidopsis up-regulated the senescence-associated genes AtWRKY53, AtSAG12 and AtSAG13, enhancing the plant’s susceptibility to leaf senescence. These findings lay the foundation for further analysis and study of the functions of WRKY genes in cotton.

Human pluripotent stem cells recurrently acquire and expand dominant negative P53 mutations.

Human pluripotent stem cells (hPSCs) can self-renew indefinitely, making them an attractive source for regenerative therapies. This expansion potential has been linked with acquisition of large copy number variants (CNVs) that provide mutant cells with a growth advantage in culture1–3. However, the nature, extent, and functional impact of other acquired genome sequence mutations in cultured hPSCs is not known. Here, we sequenced the protein-coding genes (exomes) of 140 independent human embryonic stem cell (hESC) lines, including 26 lines prepared for potential clinical use4. We then applied computational strategies for identifying mutations present in a subset of cells5. Though such mosaic mutations were generally rare, we identified five unrelated hESC lines that carried six mutations in the TP53 gene that encodes the tumor suppressor P53. Notably, the TP53 mutations we observed are dominant negative and are the mutations most commonly seen in human cancers. We used droplet digital PCR to demonstrate that the TP53 mutant allelic fraction increased with passage number under standard culture conditions, suggesting that P53 mutation confers selective advantage. When we then mined published RNA sequencing data from 117 hPSC lines, we observed another nine TP53 mutations, all resulting in coding changes in the DNA binding domain of P53. Strikingly, in three lines, the allelic fraction exceeded 50%, suggesting additional selective advantage resulting from loss of heterozygosity at the TP53 locus. As the acquisition and favored expansion of cancer-associated mutations in hPSCs may go unnoticed during most applications, we suggest that careful genetic characterization of hPSCs and their differentiated derivatives should be carried out prior to clinical use.

Psoriasis-Specific RNA Isoforms Identified by RNA-Seq Analysis of 173,446 Transcripts.

Several studies have been published that investigated potential links between transcriptome changes and psoriasis using microarrays and RNA-seq technologies, but no previous study has analyzed expression profile of alternatively spliced transcripts in psoriasis. Identification of potential alternatively spliced RNA isoforms with disease-specific expression profile. Using our published RNA sequencing data from lesional psoriatic (LP), non-lesional psoriatic (NLP), and normal control skin (C), we analyzed the differential expression of RNA splicing variants. LP sample was compared with NLP, as was LP with C and NLP with C. Transcript-based annotation analyzed 173,446 transcripts (RNA isoforms), and around 9,000 transcripts were identified as differentially expressed between study groups. Several previously undescribed RNA variants were found. For instance, transcript ETV3_3 (ENST00000326786) was significantly downregulated in LP and NLP skin. ETV3 is a transcriptional repressor that contributes to the downstream anti-inflammatory effects of IL-10. We also identified diseases-specific transcripts (S100A7A, IL36RN_4, and IL36G_3) of genes already recognized to be involved in inflammation and immune response. Psoriasis is characterized by significant differences in the expression of RNA alternative isoforms. Description of these new isoforms improves our knowledge about this complex disease.

Quantitative Proteomic and Transcriptomic Study on Autotetraploid Paulownia and Its Diploid Parent Reveal Key Metabolic Processes Associated with Paulownia Autotetraploidization.

Polyploidy plays a very important role in speciation and plant evolution by way of genomic merging and doubling. In the process of polyploidy, rapid genomic, and transcriptomic changes have been observed and researched. However, proteomic divergence caused by the effects of polyploidization is still poorly understood. In the present study, we used iTRAQ coupled with mass spectrometry to quantitatively analyze proteomic changes in the leaves of autotetraploid Paulownia and its diploid parent. A total of 2963 proteins were identified and quantified. Among them, 463 differentially abundant proteins were detected between autotetraploid Paulownia and its diploid parent, and 198 proteins were found to be non-additively abundant in autotetraploid Paulownia, suggesting the presence of non-additive protein regulation during genomic merger and doubling. We also detected 1808 protein-encoding genes in previously published RNA sequencing data. We found that 59 of the genes that showed remarkable changes at mRNA level encoded proteins with consistant changes in their abundance levels, while a further 48 genes that showed noteworthy changes in their expression levels encoded proteins with opposite changes in their abundance levels. Proteins involved in posttranslational modification, protein turnover, and response to stimulus, were significantly enriched among the non-additive proteins, which may provide some of the driving power for variation and adaptation in autopolyploids. Quantitative real-time PCR analysis verified the expression patterns of related protein-coding genes. In addition, we found that the percentage of differentially abundant proteins that matched previously reported differentially expressed genes was relatively low.

Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels.

BackgroundRNA-sequencing (RNA-seq) is a powerful technique for the identification of genetic variants that affect gene-expression levels, either through expression quantitative trait locus (eQTL) mapping or through allele-specific expression (ASE) analysis. Given increasing numbers of RNA-seq samples in the public domain, we here studied to what extent eQTLs and ASE effects can be identified when using public RNA-seq data while deriving the genotypes from the RNA-sequencing reads themselves.MethodsWe downloaded the raw reads for all available human RNA-seq datasets. Using these reads we performed gene expression quantification. All samples were jointly normalized and subjected to a strict quality control. We also derived genotypes using the RNA-seq reads and used imputation to infer non-coding variants. This allowed us to perform eQTL mapping and ASE analyses jointly on all samples that passed quality control. Our results were validated using samples for which DNA-seq genotypes were available.Results4,978 public human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though these data originated from many different laboratories, samples reflecting the same cell type clustered together, suggesting that technical biases due to different sequencing protocols are limited. In a joint analysis on the 1,262 samples with high quality genotypes, we identified cis-eQTLs effects for 8,034 unique genes (at a false discovery rate ≤0.05). eQTL mapping on individual tissues revealed that a limited number of samples already suffice to identify tissue-specific eQTLs for known disease-associated genetic variants. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels.ConclusionsBy deriving and imputing genotypes from RNA-seq data, it is possible to identify both eQTLs and ASE effects. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become especially relevant for studying the effects of tissue-specific and rare pathogenic genetic variants to aid clinical interpretation of exome and genome sequencing.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0152-4) contains supplementary material, which is available to authorized users.

A systematic analysis of genetic dilated cardiomyopathy reveals numerous ubiquitously expressed and muscle-specific genes.

Despite considerable progress being made in genetic diagnostics for dilated cardiomyopathy (DCM) using panels of the most prevalent genes, the cause remains unsolved in a substantial percentage of patients. We hypothesize that several previously described DCM genes with low or unknown prevalence have been neglected, which, if catalogued, could increase the yield of diagnostic DCM testing. The aim of this study is to catalogue all genetic evidence on DCM comprehensively. We have conducted a systematic literature search on PubMed, Embase, and OMIM to find genes implicated in syndromic and non-syndromic DCM and peripartum cardiomyopathy (PPCM). Our search yielded 110 nuclear protein-coding genes and 24 mitochondrial DNA genes. For nuclear genes, in addition to 42 genes sufficiently reviewed previously (group A), we provide a comprehensive annotation of the level of genetic evidence for the remaining 68 genes (group B). Next, we investigated the tissue specificity of the collected genes using public RNA sequencing data. We show that genes primarily expressed in heart and skeletal muscle are more likely to result in DCM with possible skeletal myopathies, while genes expressed ubiquitously cause DCM with extramuscular manifestations. This comprehensive analysis of DCM-associated genes revealed a much higher number of genes than currently screened in diagnostics. Since most genes in group B have only been found mutated in single DCM patients or families, their importance for DCM genetic diagnostics needs to be validated in large cohorts. Targeted sequencing of validated DCM-implicated protein-coding genes and mitochondrial DNA, together with consideration of the tissue specificity of mutated genes, may facilitate further genotype-phenotype studies in DCM.

IRcall and IRclassifier: two methods for flexible detection of intron retention events from RNA-Seq data.

BackgroundThe emergence of next-generation RNA sequencing (RNA-Seq) provides tremendous opportunities for researchers to analyze alternative splicing on a genome-wide scale. However, accurate detection of intron retention (IR) events from RNA-Seq data has remained an unresolved challenge in next-generation sequencing (NGS) studies.ResultsWe propose two new methods: IRcall and IRclassifier to detect IR events from RNA-Seq data. Our methods combine together gene expression information, read coverage within an intron, and read counts (within introns, within flanking exons, supporting splice junctions, and overlapping with 5' splice site/ 3' splice site), employing ranking strategy and classifiers to detect IR events. We applied our approaches to one published RNA-Seq data on contrasting skip mutant and wild-type in Arabidopsis thaliana. Compared with three state-of-the-art methods, IRcall and IRclassifier could effectively filter out false positives, and predict more accurate IR events.AvailabilityThe data and codes of IRcall and IRclassifier are available at http://mlg.hit.edu.cn/ybai/IR/IRcallAndIRclass.html

Analysis of Transcriptome Complexity Through RNA Sequencing in Normal and Failing Murine Hearts

Accurate and comprehensive de novo transcriptome profiling in heart is a central issue to better understand cardiac physiology and diseases. Although significant progress has been made in genome-wide profiling for quantitative changes in cardiac gene expression, current knowledge offers limited insights to the total complexity in cardiac transcriptome at individual exon level. To develop more robust bioinformatic approaches to analyze high-throughput RNA sequencing (RNA-Seq) data, with the focus on the investigation of transcriptome complexity at individual exon and transcript levels. In addition to overall gene expression analysis, the methods developed in this study were used to analyze RNA-Seq data with respect to individual transcript isoforms, novel spliced exons, novel alternative terminal exons, novel transcript clusters (ie, novel genes), and long noncoding RNA genes. We applied these approaches to RNA-Seq data obtained from mouse hearts after pressure-overload-induced by transaortic constriction. Based on experimental validations, analyses of the features of the identified exons/transcripts, and expression analyses including previously published RNA-Seq data, we demonstrate that the methods are highly effective in detecting and quantifying individual exons and transcripts. Novel insights inferred from the examined aspects of the cardiac transcriptome open ways to further experimental investigations. Our work provided a comprehensive set of methods to analyze mouse cardiac transcriptome complexity at individual exon and transcript levels. Applications of the methods may infer important new insights to gene regulation in normal and disease hearts in terms of exon utilization and potential involvement of novel components of cardiac transcriptome.