BinQuasi: a peak detection method for ChIP-sequencing data with biological replicates | Zendy

Emily Goren | Zendy; Peng Liu | Zendy; Chao Wang | Zendy; Chong Wang | Zendy

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BinQuasi: a peak detection method for ChIP-sequencing data with biological replicates

Author(s) -

Emily Goren,

Peng Liu,

Chao Wang,

Chong Wang

Publication year - 2018

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bty227

Subject(s) - computer science , replication (statistics) , joint (building) , data mining , source code , r package , code (set theory) , chip , false discovery rate , statistics , mathematics , set (abstract data type) , biology , architectural engineering , telecommunications , biochemistry , computational science , engineering , gene , programming language , operating system

ChIP-seq experiments that are aimed at detecting DNA-protein interactions require biological replication to draw inferential conclusions, however there is no current consensus on how to analyze ChIP-seq data with biological replicates. Very few methodologies exist for the joint analysis of replicated ChIP-seq data, with approaches ranging from combining the results of analyzing replicates individually to joint modeling of all replicates. Combining the results of individual replicates analyzed separately can lead to reduced peak classification performance compared to joint modeling. Currently available methods for joint analysis may fail to control the false discovery rate at the nominal level.

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