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Universal Count Correction for High-Throughput Sequencing.
- Source :
- PLoS Computational Biology; Mar2014, Vol. 10 Issue 3, p1-11, 11p, 1 Chart, 7 Graphs
- Publication Year :
- 2014
-
Abstract
- We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called Fixseq. We demonstrate that Fixseq substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1553734X
- Volume :
- 10
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 95434903
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1003494