RT Journal Article
SR Electronic
T1 Rail-RNA: Scalable analysis of RNA-seq splicing and coverage
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 019067
DO 10.1101/019067
A1 Abhinav Nellore
A1 Leonardo Collado-Torres
A1 Andrew E. Jaffe
A1 James Morton
A1 Jacob Pritt
A1 José Alquicira-Hernández
A1 Jeffrey T. Leek
A1 Ben Langmead
YR 2015
UL http://biorxiv.org/content/early/2015/05/07/019067.abstract
AB 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 is difficult to reproduce the exact analysis without access to original computing resources. 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 666 RNA-seq samples from the GEUVADIS project on Amazon Web Services in 12 hours for US$0.69 per sample. Rail-RNA produces alignments and base-resolution bigWig coverage files, ready for use with downstream packages for reproducible statistical analysis. We 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 confounders. Rail-RNA is open-source software available at http://rail.bio.