Universal Count Correction for High-Throughput Sequencing
Name
Hashimoto-2014-Universal count corr.pdf
Size
783.91 KB
Format
Adobe PDF
Checksum (MD5)
d592ecb5f87ee29560c9056fd0e54b1b
Author(s)
Hashimoto, Tatsunori Benjamin
Edwards, Matthew Douglas
Gifford, David K.
Date Issued
March 2014
Journal
PLoS Computational Biology
Publisher
Public Library of Science
Citation
Hashimoto, Tatsunori B., Matthew D. Edwards, and David K. Gifford. “Universal Count Correction for High-Throughput Sequencing.” Edited by Alice Carolyn McHardy. PLoS Comput Biol 10, no. 3 (March 6, 2014): e1003494.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1003494
