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

Abhinav Nellore, Leonardo Collado-Torres, Andrew E. Jaffe, James Morton, Jacob Pritt, José Alquicira-Hernández, Jeffrey T. Leek, Ben Langmead
doi: https://doi.org/10.1101/019067
Abhinav Nellore
1Department of Computer Science, Johns Hopkins University
2Department of Biostatistics, Johns Hopkins University
3Center for Computational Biology, Johns Hopkins University
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Leonardo Collado-Torres
2Department of Biostatistics, Johns Hopkins University
3Center for Computational Biology, Johns Hopkins University
4Lieber Institute for Brain Development, Johns Hopkins Medical Campus
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Andrew E. Jaffe
2Department of Biostatistics, Johns Hopkins University
3Center for Computational Biology, Johns Hopkins University
4Lieber Institute for Brain Development, Johns Hopkins Medical Campus
5Department of Mental Health, Johns Hopkins University
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James Morton
6Department of Computer Science, University of Colorado Boulder
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Jacob Pritt
1Department of Computer Science, Johns Hopkins University
3Center for Computational Biology, Johns Hopkins University
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José Alquicira-Hernández
2Department of Biostatistics, Johns Hopkins University
7Undergraduate Program on Genomic Sciences, National Autonomous University of Mexico
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Jeffrey T. Leek
2Department of Biostatistics, Johns Hopkins University
3Center for Computational Biology, Johns Hopkins University
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Ben Langmead
1Department of Computer Science, Johns Hopkins University
2Department of Biostatistics, Johns Hopkins University
3Center for Computational Biology, Johns Hopkins University
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Abstract

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted May 07, 2015.
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Rail-RNA: Scalable analysis of RNA-seq splicing and coverage
Abhinav Nellore, Leonardo Collado-Torres, Andrew E. Jaffe, James Morton, Jacob Pritt, José Alquicira-Hernández, Jeffrey T. Leek, Ben Langmead
bioRxiv 019067; doi: https://doi.org/10.1101/019067
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Rail-RNA: Scalable analysis of RNA-seq splicing and coverage
Abhinav Nellore, Leonardo Collado-Torres, Andrew E. Jaffe, James Morton, Jacob Pritt, José Alquicira-Hernández, Jeffrey T. Leek, Ben Langmead
bioRxiv 019067; doi: https://doi.org/10.1101/019067

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