On the problem of confounders in modeling gene expression
Author(s) -
Florian Schmidt,
Marcel H. Schulz
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/bty674
Subject(s) - computer science , confounding , encode , computational biology , reliability (semiconductor) , data mining , chromatin , machine learning , gene , biology , genetics , statistics , mathematics , power (physics) , physics , quantum mechanics
Modeling of Transcription Factor (TF) binding from both ChIP-seq and chromatin accessibility data has become prevalent in computational biology. Several models have been proposed to generate new hypotheses on transcriptional regulation. However, there is no distinct approach to derive TF binding scores from ChIP-seq and open chromatin experiments. Here, we review biases of various scoring approaches and their effects on the interpretation and reliability of predictive gene expression models.
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