UMMS Affiliation

Department of Medicine, Division of Infectious Diseases and Immunology

Date

12-1-2014

Document Type

Article

Subjects

Algorithms; Animals; Binding Sites; Chromatin Immunoprecipitation; High-Throughput Nucleotide Sequencing; Mice, Inbred C57BL; Mice, Knockout; Models, Animal; Nucleotide Motifs; Sequence Analysis, DNA; Transcription Factors

Disciplines

Cell Biology | Computational Biology | Genetics | Genomics

Abstract

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.

Rights and Permissions

Citation: 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.

DOI of Published Version

10.1093/nar/gku1078

Comments

© 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.

Related Resources

Link to Article in PubMed

Journal Title

Nucleic acids research

PubMed ID

25378309

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.