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Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data
ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with ultra high-throughput massively parallel sequencing, is increasingly being used for mapping protein–DNA interactions in-vivo on a genome scale. Typically, short sequence reads from ChIP-Seq are mapped to a reference genome for furthe...
Autores principales: | , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Oxford University Press
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2532738/ https://www.ncbi.nlm.nih.gov/pubmed/18684996 http://dx.doi.org/10.1093/nar/gkn488 |
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author | Jothi, Raja Cuddapah, Suresh Barski, Artem Cui, Kairong Zhao, Keji |
author_facet | Jothi, Raja Cuddapah, Suresh Barski, Artem Cui, Kairong Zhao, Keji |
author_sort | Jothi, Raja |
collection | PubMed |
description | ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with ultra high-throughput massively parallel sequencing, is increasingly being used for mapping protein–DNA interactions in-vivo on a genome scale. Typically, short sequence reads from ChIP-Seq are mapped to a reference genome for further analysis. Although genomic regions enriched with mapped reads could be inferred as approximate binding regions, short read lengths (∼25–50 nt) pose challenges for determining the exact binding sites within these regions. Here, we present SISSRs (Site Identification from Short Sequence Reads), a novel algorithm for precise identification of binding sites from short reads generated from ChIP-Seq experiments. The sensitivity and specificity of SISSRs are demonstrated by applying it on ChIP-Seq data for three widely studied and well-characterized human transcription factors: CTCF (CCCTC-binding factor), NRSF (neuron-restrictive silencer factor) and STAT1 (signal transducer and activator of transcription protein 1). We identified 26 814, 5813 and 73 956 binding sites for CTCF, NRSF and STAT1 proteins, respectively, which is 32, 299 and 78% more than that inferred previously for the respective proteins. Motif analysis revealed that an overwhelming majority of the identified binding sites contained the previously established consensus binding sequence for the respective proteins, thus attesting for SISSRs’ accuracy. SISSRs’ sensitivity and precision facilitated further analyses of ChIP-Seq data revealing interesting insights, which we believe will serve as guidance for designing ChIP-Seq experiments to map in vivo protein–DNA interactions. We also show that tag densities at the binding sites are a good indicator of protein–DNA binding affinity, which could be used to distinguish and characterize strong and weak binding sites. Using tag density as an indicator of DNA-binding affinity, we have identified core residues within the NRSF and CTCF binding sites that are critical for a stronger DNA binding. |
format | Text |
id | pubmed-2532738 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-25327382008-09-16 Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data Jothi, Raja Cuddapah, Suresh Barski, Artem Cui, Kairong Zhao, Keji Nucleic Acids Res Computational Biology ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with ultra high-throughput massively parallel sequencing, is increasingly being used for mapping protein–DNA interactions in-vivo on a genome scale. Typically, short sequence reads from ChIP-Seq are mapped to a reference genome for further analysis. Although genomic regions enriched with mapped reads could be inferred as approximate binding regions, short read lengths (∼25–50 nt) pose challenges for determining the exact binding sites within these regions. Here, we present SISSRs (Site Identification from Short Sequence Reads), a novel algorithm for precise identification of binding sites from short reads generated from ChIP-Seq experiments. The sensitivity and specificity of SISSRs are demonstrated by applying it on ChIP-Seq data for three widely studied and well-characterized human transcription factors: CTCF (CCCTC-binding factor), NRSF (neuron-restrictive silencer factor) and STAT1 (signal transducer and activator of transcription protein 1). We identified 26 814, 5813 and 73 956 binding sites for CTCF, NRSF and STAT1 proteins, respectively, which is 32, 299 and 78% more than that inferred previously for the respective proteins. Motif analysis revealed that an overwhelming majority of the identified binding sites contained the previously established consensus binding sequence for the respective proteins, thus attesting for SISSRs’ accuracy. SISSRs’ sensitivity and precision facilitated further analyses of ChIP-Seq data revealing interesting insights, which we believe will serve as guidance for designing ChIP-Seq experiments to map in vivo protein–DNA interactions. We also show that tag densities at the binding sites are a good indicator of protein–DNA binding affinity, which could be used to distinguish and characterize strong and weak binding sites. Using tag density as an indicator of DNA-binding affinity, we have identified core residues within the NRSF and CTCF binding sites that are critical for a stronger DNA binding. Oxford University Press 2008-09 2008-08-06 /pmc/articles/PMC2532738/ /pubmed/18684996 http://dx.doi.org/10.1093/nar/gkn488 Text en © 2008 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Jothi, Raja Cuddapah, Suresh Barski, Artem Cui, Kairong Zhao, Keji Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data |
title | Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data |
title_full | Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data |
title_fullStr | Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data |
title_full_unstemmed | Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data |
title_short | Genome-wide identification of in vivo protein–DNA binding sites from ChIP-Seq data |
title_sort | genome-wide identification of in vivo protein–dna binding sites from chip-seq data |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2532738/ https://www.ncbi.nlm.nih.gov/pubmed/18684996 http://dx.doi.org/10.1093/nar/gkn488 |
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