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AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification
2019
Nucleic Acids Research
Preserved Fulltext
We applied AIControl to 410 IP datasets in the ENCODE ChIP-seq database, using 440 control datasets from 107 cell types to impute background signal. ...
available control ChIP-seq datasets. ...
ACKNOWLEDGEMENTS We would like to acknowledge the following people for testing the AIControl software and giving suggestions on improving its usability: Ayse Berceste Dincer, Joseph D. ...
doi:10.1093/nar/gkz156
pmid:30869146
pmcid:PMC6547432
fatcat:2zkyqld7dvhi7kvoeahta26dei
AIControl: Replacing matched control experiments with machine learning improves ChIP-seq peak identification
[article]
2018
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
AIControl used 455 control ChIP-seq datasets from 107 cell lines and 9 laboratories to impute background ChIP-seq noise signals. ...
Because ChIP-seq is highly susceptible to background noise, the current practice obtains one matched "control" ChIP-seq dataset and estimates position-wise background distributions using ChIP-seq signals ...
experiments.
558 AIControl better removes common background signal among datasets. ...
doi:10.1101/278762
fatcat:i6s3krrvyfdh3li5r5s6s7k6gq