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Efficient RNA isoform identification and quantification from RNA-Seq data with network flows Jacob, Laurent
Description
Several state-of-the-art methods for isoform identification and quantification are based on l1- regularized regression, such as the Lasso. However, explicitly listing the - possibly exponentially - large set of candidate transcripts is intractable for genes with many exons. For this reason, existing approaches using the l1- penalty are either restricted to genes with few exons or only run the regression algorithm on a small set of preselected isoforms. We introduce a new technique called FlipFlop, which can efficiently tackle the sparse estimation problem on the full set of candidate isoforms by using network flow optimization. Our technique removes the need of a preselection step, leading to better isoform identification while keeping a low computational cost. Experiments with synthetic and real RNA-Seq data confirm that our approach is more accurate than alternative methods and one of the fastest available.
Item Metadata
Title |
Efficient RNA isoform identification and quantification from RNA-Seq data with network flows
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2015-08-03T09:14
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Description |
Several state-of-the-art methods for isoform identification and quantification are based on l1- regularized regression, such as the Lasso. However, explicitly listing the - possibly exponentially - large set of candidate transcripts is intractable for genes with many exons. For this reason, existing approaches using the l1- penalty are either restricted to genes with few exons or only run the regression algorithm on a small set of preselected isoforms. We introduce a new technique called FlipFlop, which can efficiently tackle the sparse estimation problem on the full set of candidate isoforms by using network flow optimization. Our technique removes the need of a preselection step, leading to better isoform identification while keeping a low computational cost. Experiments with synthetic and real RNA-Seq data confirm that our approach is more accurate than alternative methods and one of the fastest available.
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Extent |
23 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: Centre National de Recherche Scientifique
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Series | |
Date Available |
2016-04-18
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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 |
10.14288/1.0229552
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Other
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International