Dataset Information

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

ABSTRACT: Motivation:RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it requires extra work to obtain analysis products that incorporate data from across samples. Results:We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 667 RNA-seq samples from the GEUVADIS project on Amazon Web Services in under 16?h for US$0.91 per sample. Rail-RNA outputs alignments in SAM/BAM format; but it also outputs (i) base-level coverage bigWigs for each sample; (ii) coverage bigWigs encoding normalized mean and median coverages at each base across samples analyzed; and (iii) exon-exon splice junctions and indels (features) in columnar formats that juxtapose coverages in samples in which a given feature is found. Supplementary outputs are ready for use with downstream packages for reproducible statistical analysis. We use Rail-RNA to identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounding variables. Availability and Implementation:Rail-RNA is open-source software available at http://rail.bio. Contacts:anellore@gmail.com or langmea@cs.jhu.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Nellore A

PROVIDER: S-EPMC5860083 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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

Nellore Abhinav A Collado-Torres Leonardo L Jaffe Andrew E AE Alquicira-Hernández José J Wilks Christopher C Pritt Jacob J Morton James J Leek Jeffrey T JT Langmead Ben B

Bioinformatics (Oxford, England) 20171201 24

<h4>Motivation</h4>RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it requires extra work to obtain analysis products that incorporate data from across samples.<h4>Results</h4>We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work ...[more]

PMID: 27592709

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Project description:BACKGROUND:Direct cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cell populations without the need for sample pooling or RNA extraction. We term the use of single-cell chemistries for sequencing low numbers of cells limiting-cell RNA-seq (lcRNA-seq). Currently, there is no customized algorithm to select robust/low-noise transcripts from lcRNA-seq data for between-group comparisons. METHODS:Herein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed genes (DEG) analysis. Total RNA obtained from primary chronic lymphocytic leukemia (CLL) CD5+?and CD5- cells were used to develop the CLEAR algorithm. Once established, the performance of CLEAR was evaluated with FACS-sorted cells enriched from mouse Dentate Gyrus (DG). RESULTS:When using CLEAR transcripts vs. using all transcripts in CLL samples, downstream analyses revealed a higher proportion of shared transcripts across three input amounts and improved principal component analysis (PCA) separation of the two cell types. In mouse DG samples, CLEAR identifies noisy transcripts and their removal improves PCA separation of the anticipated cell populations. In addition, CLEAR was applied to two publicly-available datasets to demonstrate its utility in lcRNA-seq data from other institutions. If imputation is applied to limit the effect of missing data points, CLEAR can also be used in large clinical trials and in single cell studies. CONCLUSIONS:lcRNA-seq coupled with CLEAR is widely used in our institution for profiling immune cells (circulating or tissue-infiltrating) for its transcript preservation characteristics. CLEAR fills an important niche in pre-processing lcRNA-seq data to facilitate transcriptome profiling and DEG analysis. We demonstrate the utility of CLEAR in analyzing rare cell populations in clinical samples and in murine neural DG region without sample pooling.

Dataset Information

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

Publications

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

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