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Summarizing tens of thousands of RNA-seq samples: themes and lessons Langmead, Ben
Description
The Sequence Read Archive contains RNA-seq data for over 450K samples, including over 140K from humans. Large-scale projects like GTEx and ICGC are generating RNA-seq data on many thousands of samples. Such huge datasets are valuable, but unwieldy for typical researchers. I will describe work toward the goal of making it easy for researchers to use the archived RNA-seq data available today. I will highlight Rail-RNA (http://rail.bio), its dbGaP-protected version (http://docs.rail.bio/dbgap/), as well as the recount resource (https://jhubiostatistics.shinyapps.io/recount/) and Snaptron service/API (http://snaptron.cs.jhu.edu). Besides showcasing these tools and resources, I'll expound three themes: (a) pulic data is valuable but not easy to use and computationalists should attack this; (b) scalability is not just about scaling software to be distributed & multi-threaded, but is also about making the best use of many datasets at once; (c) "strategically unplugging" from gene annotations can lead to clearer statements about splicing and differential expression.
Item Metadata
| Title |
Summarizing tens of thousands of RNA-seq samples: themes and lessons
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| Creator | |
| Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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| Date Issued |
2017-03-30T10:37
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| Description |
The Sequence Read Archive contains RNA-seq data for over 450K samples, including over 140K from humans. Large-scale projects like GTEx and ICGC are generating RNA-seq data on many thousands of samples. Such huge datasets are valuable, but unwieldy for typical researchers. I will describe work toward the goal of making it easy for researchers to use the archived RNA-seq data available today. I will highlight Rail-RNA (http://rail.bio), its dbGaP-protected version (http://docs.rail.bio/dbgap/), as well as the recount resource (https://jhubiostatistics.shinyapps.io/recount/) and Snaptron service/API (http://snaptron.cs.jhu.edu). Besides showcasing these tools and resources, I'll expound three themes: (a) pulic data is valuable but not easy to use and computationalists should attack this; (b) scalability is not just about scaling software to be distributed & multi-threaded, but is also about making the best use of many datasets at once; (c) "strategically unplugging" from gene annotations can lead to clearer statements about splicing and differential expression.
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| Extent |
26.0
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| Subject | |
| Type | |
| File Format |
video/mp4
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| Language |
eng
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| Notes |
Author affiliation: John Hopkins University
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| Series | |
| Date Available |
2019-03-12
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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| DOI |
10.14288/1.0376771
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| URI | |
| Affiliation | |
| Peer Review Status |
Unreviewed
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| Scholarly Level |
Faculty
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International