Universal Count Correction for High-Throughput Sequencing
Author(s)Hashimoto, Tatsunori Benjamin; Edwards, Matthew Douglas; Gifford, David K.
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
PLoS Computational Biology
Public Library of Science
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.
Final published version