Department of Medicine, Division of Infectious Diseases and Immunology
Algorithms; Animals; Binding Sites; Chromatin Immunoprecipitation; High-Throughput Nucleotide Sequencing; Mice, Inbred C57BL; Mice, Knockout; Models, Animal; Nucleotide Motifs; Sequence Analysis, DNA; Transcription Factors
Cell Biology | Computational Biology | Genetics | Genomics
Genome-wide assessment of protein-DNA interaction by chromatin immunoprecipitation followed by massive parallel sequencing (ChIP-seq) is a key technology for studying transcription factor (TF) localization and regulation of gene expression. Signal-to-noise-ratio and signal specificity in ChIP-seq studies depend on many variables, including antibody affinity and specificity. Thus far, efforts to improve antibody reagents for ChIP-seq experiments have focused mainly on generating higher quality antibodies. Here we introduce KOIN (knockout implemented normalization) as a novel strategy to increase signal specificity and reduce noise by using TF knockout mice as a critical control for ChIP-seq data experiments. Additionally, KOIN can identify 'hyper ChIPable regions' as another source of false-positive signals. As the use of the KOIN algorithm reduces false-positive results and thereby prevents misinterpretation of ChIP-seq data, it should be considered as the gold standard for future ChIP-seq analyses, particularly when developing ChIP-assays with novel antibody reagents.
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Copyright The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
DOI of Published Version
Nucleic Acids Res. 2014 Dec 1;42(21):13051-60. doi: 10.1093/nar/gku1078. Epub 2014 Nov 5. Link to article on publisher's site.
Nucleic acids research
Krebs W, Schmidt SV, Goren A, De Nardo D, Labzin L, Bovier A, Ulas T, Theis H, Kraut M, Latz E, Beyer M, Schultze JL. (2014). Optimization of transcription factor binding map accuracy utilizing knockout-mouse models. Open Access Articles. https://doi.org/10.1093/nar/gku1078. Retrieved from https://escholarship.umassmed.edu/oapubs/2580
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.