Rail-RNA: scalable analysis of RNA-seq splicing and coverage

The recount2 resource is composed of over 70,000 uniformly processed human RNA-seq samples spanning TCGA and SRA, including GTEx. The processed data can be accessed via the recount2 website and the recountBioconductor package. This workflow explains in detail how to use the recountpackage and how to integrate it with other Bioconductor packages for several analyses that can be carried out with the recount2 resource. In particular, we describe how the coverage count matrices were computed in recount2 as well as different ways of obtaining public metadata, which can facilitate downstream analyses. Step-by-step directions show how to do a gene-level differential expression analysis, visualize base-level genome coverage data, and perform an analyses at multiple feature levels. This workflow thus provides further information to understand the data in recount2 and a compendium of R code to use the data.

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“…Reads in the recount2 resource were aligned with the splice-aware Rail-RNA aligner 11 . Figure 4 shows the reads aligned to the reference genome.…”

Section: Analysis Of Rna-seq Data Available At Recount2mentioning

confidence: 99%

recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor

2017

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“…Likewise, the 23 need to analyze this data from scratch leads to unnecessary duplications of effort across research 24 groups 3,4 . To address this, we previously developed a bioinformatics pipeline (Rail-RNA 5,6 ) and created 25 the recount2 7 resource and accompanying Snaptron 8 query engine. Together, these allow researchers to 26 query publicly available RNA-Seq data in a standardized and reproducible manner.…”

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confidence: 99%

ASCOT identifies key regulators of neuronal subtype-specific splicing

Ling

Wilks

Charles

et al. 2018

Preprint

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1Public archives of next-generation sequencing data are growing exponentially, but the difficulty of 2 marshaling this data has led to its underutilization by scientists. Here we present ASCOT, a resource that 3 allows researchers to summarize, visualize, and query alternative splicing patterns in public RNA-Seq 4 data. ASCOT enables rapid identification of splice-variants across tens of thousands of bulk and single-5 cell RNA-Seq datasets in human and mouse. To demonstrate the utility of ASCOT, we first focused on the 6 nervous system and identified many alternative exons used only by a single neuronal subtype. We then 7 leveraged datasets from the ENCODE and GTEx consortiums to study the unique splicing patterns of rod 8 photoreceptors and found that PTBP1 knockdown combined with overexpression of MSI1 and PCBP2 9 activates rod-specific exons in HepG2 liver cancer cells. Furthermore, we observed that MSI1 targets 10 intronic UAG motifs proximal to the 5' splice site and interacts synergistically with PTBP1 11 downregulation. Finally, we show that knockdown of MSI1 in the retina abolishes rod-specific splicing. 12This work demonstrates how large-scale analysis of public RNA-Seq datasets can yield key insights into 13 cell type-specific control of RNA splicing and underscores the importance of considering both annotated 14 and unannotated splicing events. ASCOT splicing and gene expression data tables, software, and 15 interactive browser are available at http://ascot.cs.jhu.edu. 16 17

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“…This atlas of RNA-Seq count table improves the process of data acquisition and allows cross-study comparisons since all the count tables were produced from one single pipeline reducing batch effects and promoting alternative normalization. Recount was then extended to Recount2 consisting of more than 4.4 trillion reads using 70,603 human RNA-seq samples from the Sequence Read Archive (SRA), GTEx, and TCGA that were uniformly processed, quantified with Rail-RNA [53], and included in the recent Recount2 interface [34].…”

Section: Design and Implementationmentioning

confidence: 99%

Analyses of cancer data in the Genomic Data Commons Data Portal with new functionalities in the TCGAbiolinks R/Bioconductor package

Mounir

Silva

Lucchetta

et al. 2018

Preprint

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The advent of Next Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data.The Genomic Data Commons (GDC) Data Portal is a large data collection platform that includes different genomic studies included the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data without renouncing a robust statistical and theoretical framework are needed. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from the Genotype-Tissue-Expression (GTEx) platform iv) support for other genomics datasets, here exemplified by the TARGET data.Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. Henceforth, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGABiolinks v 2.8 and higher released in Bioconductor version 3.7.

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Rail-RNA: scalable analysis of RNA-seq splicing and coverage

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 60 publications

References 46 publications

recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor

recount workflow: Accessing over 70,000 human RNA-seq samples with Bioconductor

ASCOT identifies key regulators of neuronal subtype-specific splicing

Analyses of cancer data in the Genomic Data Commons Data Portal with new functionalities in the TCGAbiolinks R/Bioconductor package

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