QChIPat

A quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions

Bin Liu, Jimmy Yi, Aishwarya SV, Xun Lan, Yilin Ma, Hui-ming Huang, Gustavo Leone, Victor X Jin

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Background: Many computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples.Results: In this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.Conclusions: Several advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at http://motif.bmi.ohio-state.edu/QChIPat.

Original languageEnglish (US)
Article numberS3
JournalBMC Genomics
Volume14
Issue numberSUPP 8
DOIs
StatePublished - Dec 9 2013

Fingerprint

Histone Code
Benchmarking
MCF-7 Cells
Binding Sites

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

Cite this

QChIPat : A quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions. / Liu, Bin; Yi, Jimmy; SV, Aishwarya; Lan, Xun; Ma, Yilin; Huang, Hui-ming; Leone, Gustavo; Jin, Victor X.

In: BMC Genomics, Vol. 14, No. SUPP 8, S3, 09.12.2013.

Research output: Contribution to journalArticle

@article{e3e5a8ed0f274864b2ca68985408b9d1,
title = "QChIPat: A quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions",
abstract = "Background: Many computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples.Results: In this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.Conclusions: Several advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at http://motif.bmi.ohio-state.edu/QChIPat.",
author = "Bin Liu and Jimmy Yi and Aishwarya SV and Xun Lan and Yilin Ma and Hui-ming Huang and Gustavo Leone and Jin, {Victor X}",
year = "2013",
month = "12",
day = "9",
doi = "10.1186/1471-2164-14-S8-S3",
language = "English (US)",
volume = "14",
journal = "BMC Genomics",
issn = "1471-2164",
publisher = "BioMed Central",
number = "SUPP 8",

}

TY - JOUR

T1 - QChIPat

T2 - A quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions

AU - Liu, Bin

AU - Yi, Jimmy

AU - SV, Aishwarya

AU - Lan, Xun

AU - Ma, Yilin

AU - Huang, Hui-ming

AU - Leone, Gustavo

AU - Jin, Victor X

PY - 2013/12/9

Y1 - 2013/12/9

N2 - Background: Many computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples.Results: In this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.Conclusions: Several advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at http://motif.bmi.ohio-state.edu/QChIPat.

AB - Background: Many computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples.Results: In this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.Conclusions: Several advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at http://motif.bmi.ohio-state.edu/QChIPat.

UR - http://www.scopus.com/inward/record.url?scp=84889687271&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84889687271&partnerID=8YFLogxK

U2 - 10.1186/1471-2164-14-S8-S3

DO - 10.1186/1471-2164-14-S8-S3

M3 - Article

VL - 14

JO - BMC Genomics

JF - BMC Genomics

SN - 1471-2164

IS - SUPP 8

M1 - S3

ER -