Universal Count Correction for High-Throughput Sequencing

Universal Count Correction for High-Throughput Sequencing

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 seque...

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Bibliographic Details
Main Authors: Hashimoto, Tatsunori Benjamin, Edwards, Matthew Douglas, Gifford, David K.
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Public Library of Science 2014
Online Access:http://hdl.handle.net/1721.1/86363
https://orcid.org/0000-0003-0521-5855
https://orcid.org/0000-0002-5845-748X
https://orcid.org/0000-0003-1709-4034

Internet

http://hdl.handle.net/1721.1/86363
https://orcid.org/0000-0003-0521-5855
https://orcid.org/0000-0002-5845-748X
https://orcid.org/0000-0003-1709-4034

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