Comparative study on ChIP-seq data: normalization and binding pattern characterization

Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive re...

Full description

Saved in:
Bibliographic Details
Published in:Bioinformatics Vol. 25; no. 18; pp. 2334 - 2340
Main Authors: Taslim, Cenny, Wu, Jiejun, Yan, Pearlly, Singer, Greg, Parvin, Jeffrey, Huang, Tim, Lin, Shili, Huang, Kun
Format: Journal Article
Language:English
Published: England Oxford University Press 15.09.2009
Oxford Publishing Limited (England)
Subjects:
Binding Sites
Bioinformatics
Breast Neoplasms - metabolism
Cell Line, Tumor
Chromatin Immunoprecipitation - methods
Comparative studies
Computational Biology - methods
Female
Humans
Original Papers
RNA Polymerase II - metabolism
Sequence Analysis, DNA - methods
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns. Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-NormalK mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples. Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/ Contact: taslim.2@osu.edu; khuang@bmi.osu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
AbstractList Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns. Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-NormalK mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples. Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/ Contact: taslim.2@osu.edu; khuang@bmi.osu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns. We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-Normal(K) mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples. Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/.
Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-Normal super(K) mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII, Supplementary information: Supplementary data are available at Bioinformatics online.
Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.MOTIVATIONAntibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-Normal(K) mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.RESULTSWe apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-Normal(K) mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/.AVAILABILITYData are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/.
Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns. Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-Normal K mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples. Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/ Contact: taslim.2@osu.edu; khuang@bmi.osu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns. Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-NormalK mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples. Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/ Contact:  taslim.2@osu.edu; khuang@bmi.osu.edu Supplementary information:  Supplementary data are available at Bioinformatics online.
Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns.Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-Normal(K) mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples.Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII, Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding patterns of specific protein molecules over the entire genome. ChIP-seq technology allows scientist to get more comprehensive results in shorter time. Here, we present a non-linear normalization algorithm and a mixture modeling method for comparing ChIP-seq data from multiple samples and characterizing genes based on their RNA polymerase II (Pol II) binding patterns. Results: We apply a two-step non-linear normalization method based on locally weighted regression (LOESS) approach to compare ChIP-seq data across multiple samples and model the difference using an Exponential-Normal[sup]K mixture model. Fitted model is used to identify genes associated with differential binding sites based on local false discovery rate (fdr). These genes are then standardized and hierarchically clustered to characterize their Pol II binding patterns. As a case study, we apply the analysis procedure comparing normal breast cancer (MCF7) to tamoxifen-resistant (OHT) cell line. We find enriched regions that are associated with cancer (P < 0.0001). Our findings also imply that there may be a dysregulation of cell cycle and gene expression control pathways in the tamoxifen-resistant cells. These results show that the non-linear normalization method can be used to analyze ChIP-seq data across multiple samples. Availability: Data are available at http://www.bmi.osu.edu/~khuang/Data/ChIP/RNAPII/ Contact: taslim.2@osu.edu; khuang@bmi.osu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Author Yan, Pearlly
Parvin, Jeffrey
Singer, Greg
Wu, Jiejun
Huang, Tim
Lin, Shili
Taslim, Cenny
Huang, Kun
AuthorAffiliation 1 Department of Molecular Virology, Immunology & Medical Genetics, 2 Department of Statistics, 3 Department of Biomedical Informatics and 4 OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
AuthorAffiliation_xml – name: 1 Department of Molecular Virology, Immunology & Medical Genetics, 2 Department of Statistics, 3 Department of Biomedical Informatics and 4 OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
Author_xml – sequence: 1
  givenname: Cenny
  surname: Taslim
  fullname: Taslim, Cenny
  organization: Department of Molecular Virology, Immunology & Medical Genetics, Department of Statistics, Department of Biomedical Informatics and OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
– sequence: 2
  givenname: Jiejun
  surname: Wu
  fullname: Wu, Jiejun
  organization: Department of Molecular Virology, Immunology & Medical Genetics, Department of Statistics, Department of Biomedical Informatics and OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
– sequence: 3
  givenname: Pearlly
  surname: Yan
  fullname: Yan, Pearlly
  organization: Department of Molecular Virology, Immunology & Medical Genetics, Department of Statistics, Department of Biomedical Informatics and OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
– sequence: 4
  givenname: Greg
  surname: Singer
  fullname: Singer, Greg
  organization: Department of Molecular Virology, Immunology & Medical Genetics, Department of Statistics, Department of Biomedical Informatics and OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
– sequence: 5
  givenname: Jeffrey
  surname: Parvin
  fullname: Parvin, Jeffrey
  organization: Department of Molecular Virology, Immunology & Medical Genetics, Department of Statistics, Department of Biomedical Informatics and OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
– sequence: 6
  givenname: Tim
  surname: Huang
  fullname: Huang, Tim
  organization: Department of Molecular Virology, Immunology & Medical Genetics, Department of Statistics, Department of Biomedical Informatics and OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
– sequence: 7
  givenname: Shili
  surname: Lin
  fullname: Lin, Shili
  organization: Department of Molecular Virology, Immunology & Medical Genetics, Department of Statistics, Department of Biomedical Informatics and OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
– sequence: 8
  givenname: Kun
  surname: Huang
  fullname: Huang, Kun
  organization: Department of Molecular Virology, Immunology & Medical Genetics, Department of Statistics, Department of Biomedical Informatics and OSUCCC Biomedical Informatics Shared Resources, The Ohio State University, Columbus, OH 43210, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/19561022$$D View this record in MEDLINE/PubMed
BookMark eNqNkk1rFTEUhoNU7If-BGVwoaux-ZxMLAhyqbZS0IpKcRMymUxv6kwyTTKl9deb61wvtgvrKgfyPG84OWcXbDnvDABPEXyFoCD7jfXWdT4MKlkd95s0kpo-ADuIVrDEkImtXJOKl7SGZBvsxngBIUOU0kdgGwlWIYjxDvi28MOoQg65MkVMU3tTeFcslsefymgui1Yl9bpwq2d6-zNT-VK5tmisa607L0aVkgmu0MucoXO5hh6Dh53qo3myPvfA13eHXxZH5cnH98eLtyelrihNZaM7w5HQhtOWaNZw0naCVq1SHTKGKFIJgrtKGyJyCWErOpzBLDVadxUke-DNnDtOzWBabVwKqpdjsIMKN9IrK2_fOLuU5_5K4hpCQnkOeLkOCP5yMjHJwUZt-l4546coOaGQMC7qTL74J0lozSjE8F4Q5_ExXOMMPr8DXvgpuPxfEomaI0bwCnr2d4eb1v6MMAMHM6CDjzGYTmqbfg8hN2x7iaBcLYy8vTByXphsszv25oF7PDh7fhr_WylnxcZkrjeSCj9kxQln8ujsuzyj7PPp6QcuBfkFCcPvSg
CODEN BOINFP
CitedBy_id crossref_primary_10_1186_1471_2105_15_S2_S10
crossref_primary_10_1093_nar_gkaa105
crossref_primary_10_1038_ncomms8973
crossref_primary_10_1093_bioinformatics_btv094
crossref_primary_10_1093_bib_bbad036
crossref_primary_10_1186_1471_2105_12_277
crossref_primary_10_1371_journal_pcbi_1003326
crossref_primary_10_1073_pnas_1204398110
crossref_primary_10_1038_s41590_019_0343_z
crossref_primary_10_1371_journal_pone_0011471
crossref_primary_10_1093_nar_gkr1205
crossref_primary_10_4137_CIN_S13972
crossref_primary_10_3390_app142411517
crossref_primary_10_1186_1471_2105_15_S12_S6
crossref_primary_10_1534_g3_117_300225
crossref_primary_10_1186_s13072_020_00342_y
crossref_primary_10_1261_rna_030916_111
crossref_primary_10_1186_1471_2105_12_39
crossref_primary_10_1093_nar_gkq1287
crossref_primary_10_1186_1748_7188_7_2
crossref_primary_10_1186_1741_7007_8_56
crossref_primary_10_1186_gb_2012_13_3_r16
crossref_primary_10_1093_nar_gks1010
crossref_primary_10_1186_s12859_016_1306_0
crossref_primary_10_1371_journal_pone_0024908
crossref_primary_10_1016_j_bbagen_2016_04_008
crossref_primary_10_1038_nrg2795
crossref_primary_10_1186_s12859_021_04320_3
crossref_primary_10_1515_sagmb_2012_0057
crossref_primary_10_1093_bib_bbw023
crossref_primary_10_1186_1471_2164_15_S9_S12
crossref_primary_10_1155_2014_758718
crossref_primary_10_1186_s12859_017_1655_3
Cites_doi 10.1080/01621459.1988.10478639
10.1093/bioinformatics/btn402
10.1186/1471-2105-6-173
10.1038/ng1901
10.1158/0008-5472.CAN-06-1666
10.1038/nbt.1505
10.1038/nbt.1518
10.1214/aoms/1177705155
10.1109/TASSP.1975.1162686
10.1200/JCO.2005.10.036
10.1186/gb-2008-9-9-r137
10.1016/S0076-6879(02)50979-4
10.1016/j.csda.2008.07.010
10.1126/science.1141319
10.1186/gb-2004-5-9-r66
10.1371/journal.pgen.0030087
10.1073/pnas.93.20.10614
10.1038/emboj.2009.88
10.1186/1471-2164-9-349
10.1186/1471-2164-9-S2-S23
10.1038/nbt.1508
10.1214/aoms/1177703862
10.1126/science.290.5500.2306
ContentType Journal Article
Copyright The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org 2009
The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Copyright_xml – notice: The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org 2009
– notice: The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
DBID BSCLL
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7QF
7QO
7QQ
7SC
7SE
7SP
7SR
7TA
7TB
7TM
7TO
7U5
8BQ
8FD
F28
FR3
H8D
H8G
H94
JG9
JQ2
K9.
KR7
L7M
L~C
L~D
P64
7X8
5PM
DOI 10.1093/bioinformatics/btp384
DatabaseName Istex
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
Aluminium Industry Abstracts
Biotechnology Research Abstracts
Ceramic Abstracts
Computer and Information Systems Abstracts
Corrosion Abstracts
Electronics & Communications Abstracts
Engineered Materials Abstracts
Materials Business File
Mechanical & Transportation Engineering Abstracts
Nucleic Acids Abstracts
Oncogenes and Growth Factors Abstracts
Solid State and Superconductivity Abstracts
METADEX
Technology Research Database
ANTE: Abstracts in New Technology & Engineering
Engineering Research Database
Aerospace Database
Copper Technical Reference Library
AIDS and Cancer Research Abstracts
Materials Research Database
ProQuest Computer Science Collection
ProQuest Health & Medical Complete (Alumni)
Civil Engineering Abstracts
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
Biotechnology and BioEngineering Abstracts
MEDLINE - Academic
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Materials Research Database
Oncogenes and Growth Factors Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Mechanical & Transportation Engineering Abstracts
Nucleic Acids Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
ProQuest Health & Medical Complete (Alumni)
Materials Business File
Aerospace Database
Copper Technical Reference Library
Engineered Materials Abstracts
Biotechnology Research Abstracts
AIDS and Cancer Research Abstracts
Advanced Technologies Database with Aerospace
ANTE: Abstracts in New Technology & Engineering
Civil Engineering Abstracts
Aluminium Industry Abstracts
Electronics & Communications Abstracts
Ceramic Abstracts
METADEX
Biotechnology and BioEngineering Abstracts
Computer and Information Systems Abstracts Professional
Solid State and Superconductivity Abstracts
Engineering Research Database
Corrosion Abstracts
MEDLINE - Academic
DatabaseTitleList
MEDLINE

Engineering Research Database
MEDLINE - Academic

CrossRef
Computer and Information Systems Abstracts
Materials Research Database
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Biology
EISSN 1460-2059
1367-4811
EndPage 2340
ExternalDocumentID PMC2800347
1852698561
19561022
10_1093_bioinformatics_btp384
10.1093/bioinformatics/btp384
ark_67375_HXZ_X45RQQJ7_9
Genre Comparative Study
Research Support, Non-U.S. Gov't
Journal Article
Research Support, N.I.H., Extramural
GrantInformation_xml – fundername: NCI NIH HHS
  grantid: U54CA113001
– fundername: NCI NIH HHS
  grantid: U54 CA113001
GroupedDBID -~X
.2P
.I3
482
48X
5GY
AAMVS
ABJNI
ABPTD
ACGFS
ACUFI
ADZXQ
ALMA_UNASSIGNED_HOLDINGS
BSCLL
CZ4
EE~
F5P
F9B
H5~
HAR
HW0
IOX
KSI
KSN
NGC
Q5Y
RD5
ROZ
RXO
TLC
TN5
TOX
WH7
~91
ADRIX
BCRHZ
KOP
ROX
---
-E4
.DC
0R~
1TH
23N
2WC
4.4
53G
5WA
70D
AAIJN
AAIMJ
AAJKP
AAJQQ
AAKPC
AAMDB
AAOGV
AAPQZ
AAPXW
AAUQX
AAVAP
AAVLN
AAYXX
ABEJV
ABEUO
ABIXL
ABNGD
ABNKS
ABPQP
ABQLI
ABWST
ABXVV
ABZBJ
ACIWK
ACPRK
ACUKT
ACUXJ
ACYTK
ADBBV
ADEYI
ADEZT
ADFTL
ADGKP
ADGZP
ADHKW
ADHZD
ADMLS
ADOCK
ADPDF
ADRDM
ADRTK
ADVEK
ADYVW
ADZTZ
AECKG
AEGPL
AEJOX
AEKKA
AEKSI
AELWJ
AEMDU
AENEX
AENZO
AEPUE
AETBJ
AEWNT
AFFNX
AFFZL
AFGWE
AFIYH
AFOFC
AFRAH
AGINJ
AGKEF
AGQPQ
AGQXC
AGSYK
AHMBA
AHXPO
AIJHB
AJEEA
AJEUX
AKHUL
AKWXX
ALTZX
ALUQC
AMNDL
APIBT
APWMN
ARIXL
ASPBG
AVWKF
AXUDD
AYOIW
AZFZN
AZVOD
BAWUL
BAYMD
BHONS
BQDIO
BQUQU
BSWAC
BTQHN
C1A
C45
CAG
CDBKE
CITATION
COF
CS3
DAKXR
DIK
DILTD
DU5
D~K
EBD
EBS
EJD
EMOBN
FEDTE
FHSFR
FLIZI
FLUFQ
FOEOM
FQBLK
GAUVT
GJXCC
GROUPED_DOAJ
GX1
H13
HVGLF
HZ~
J21
JXSIZ
KAQDR
KQ8
M-Z
MK~
ML0
N9A
NLBLG
NMDNZ
NOMLY
NU-
NVLIB
O0~
O9-
OAWHX
ODMLO
OJQWA
OK1
OVD
OVEED
P2P
PAFKI
PB-
PEELM
PQQKQ
Q1.
R44
RNS
ROL
RPM
RUSNO
RW1
SV3
TEORI
TJP
TR2
W8F
WOQ
X7H
YAYTL
YKOAZ
YXANX
ZKX
~KM
ABGNP
CGR
CUY
CVF
ECM
EIF
NPM
RIG
7QF
7QO
7QQ
7SC
7SE
7SP
7SR
7TA
7TB
7TM
7TO
7U5
8BQ
8FD
F28
FR3
H8D
H8G
H94
JG9
JQ2
K9.
KR7
L7M
L~C
L~D
P64
7X8
5PM
ID FETCH-LOGICAL-c644t-bcfe719ce74d3c5b73df946daaf1ee3a36932f6ce3936900d9f2d3cfe7bccf603
IEDL.DBID TOX
ISICitedReferencesCount 46
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000269607700007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1367-4803
1367-4811
IngestDate Tue Nov 04 02:01:06 EST 2025
Thu Sep 04 19:18:24 EDT 2025
Sun Nov 09 09:53:18 EST 2025
Tue Oct 07 09:35:16 EDT 2025
Fri Oct 03 09:30:56 EDT 2025
Mon Jul 21 05:30:30 EDT 2025
Sat Nov 29 05:33:42 EST 2025
Tue Nov 18 21:26:55 EST 2025
Wed Aug 28 03:24:17 EDT 2024
Sat Sep 20 11:02:12 EDT 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 18
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c644t-bcfe719ce74d3c5b73df946daaf1ee3a36932f6ce3936900d9f2d3cfe7bccf603
Notes To whom correspondence should be addressed.
ark:/67375/HXZ-X45RQQJ7-9
istex:29CE9E59EABE6A2EC3B8BFD3739B49E4B09D2AF8
ArticleID:btp384
Associate Editor: Joaquin Dopazo
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://academic.oup.com/bioinformatics/article-pdf/25/18/2334/48993845/bioinformatics_25_18_2334.pdf
PMID 19561022
PQID 198715322
PQPubID 36124
PageCount 7
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_2800347
proquest_miscellaneous_734035798
proquest_miscellaneous_34854020
proquest_miscellaneous_20935282
proquest_journals_198715322
pubmed_primary_19561022
crossref_citationtrail_10_1093_bioinformatics_btp384
crossref_primary_10_1093_bioinformatics_btp384
oup_primary_10_1093_bioinformatics_btp384
istex_primary_ark_67375_HXZ_X45RQQJ7_9
PublicationCentury 2000
PublicationDate 2009-09-15
PublicationDateYYYYMMDD 2009-09-15
PublicationDate_xml – month: 09
  year: 2009
  text: 2009-09-15
  day: 15
PublicationDecade 2000
PublicationPlace England
PublicationPlace_xml – name: England
– name: Oxford
PublicationTitle Bioinformatics
PublicationTitleAlternate Bioinformatics
PublicationYear 2009
Publisher Oxford University Press
Oxford Publishing Limited (England)
Publisher_xml – name: Oxford University Press
– name: Oxford Publishing Limited (England)
References Rozowsky (2023013112113923600_B18) 2009; 27
Singer (2023013112113923600_B20) 2008; 9
Ren (2023013112113923600_B17) 2000; 290
Akaike (2023013112113923600_B1) 1973
Fan (2023013112113923600_B5) 2006; 66
Teicher (2023013112113923600_B22) 1963; 34
Kharchenko (2023013112113923600_B12) 2008; 26
Schena (2023013112113923600_B19) 1996; 93
Xu (2023013112113923600_B24) 2008; 24
Feng (2023013112113923600_B6) 2008; 9
Zhang (2023013112113923600_B25) 2008; 9
Cleveland (2023013112113923600_B3) 1988; 85
Oetken (2023013112113923600_B15) 1975; 23
Ji (2023013112113923600_B9) 2008; 26
Horak (2023013112113923600_B7) 2002; 350
Lin (2023013112113923600_B13) 2004; 5
Lin (2023013112113923600_B14) 2007; 3
Teicher (2023013112113923600_B21) 1961; 32
Hulka (2023013112113923600_B8) 2004; 74
Carroll (2023013112113923600_B2) 2006; 38
Dean (2023013112113923600_B4) 2005; 6
Khalili (2023013112113923600_B11) 2009; 53
Johnson (2023013112113923600_B10) 2007; 316
Osborne (2023013112113923600_B16) 2005; 23
Welboren (2023013112113923600_B23) 2009
15345050 - Genome Biol. 2004;5(9):R66
8855227 - Proc Natl Acad Sci U S A. 1996 Oct 1;93(20):10614-9
19029915 - Nat Biotechnol. 2008 Dec;26(12):1351-9
17542648 - PLoS Genet. 2007 Jun;3(6):e87
18667444 - Bioinformatics. 2008 Oct 15;24(20):2344-9
17013392 - Nat Genet. 2006 Nov;38(11):1289-97
11125145 - Science. 2000 Dec 22;290(5500):2306-9
18798982 - Genome Biol. 2008;9(9):R137
20161265 - Comput Stat Data Anal. 2009 Mar 15;53(5):1701-1710
18978777 - Nat Biotechnol. 2008 Nov;26(11):1293-300
17178894 - Cancer Res. 2006 Dec 15;66(24):11954-66
18655706 - BMC Genomics. 2008;9:349
19122651 - Nat Biotechnol. 2009 Jan;27(1):66-75
16011807 - BMC Bioinformatics. 2005;6:173
15755967 - J Clin Oncol. 2005 Mar 10;23(8):1616-22
12073330 - Methods Enzymol. 2002;350:469-83
8039146 - Cancer. 1994 Aug 1;74(3 Suppl):1111-24
18831789 - BMC Genomics. 2008;9 Suppl 2:S23
19339991 - EMBO J. 2009 May 20;28(10):1418-28
17540862 - Science. 2007 Jun 8;316(5830):1497-502
References_xml – volume: 85
  start-page: 596
  year: 1988
  ident: 2023013112113923600_B3
  article-title: Locally-weighted regression: An approach to regression analysis by local fitting
  publication-title: J. Am. Stat. Assoc.
  doi: 10.1080/01621459.1988.10478639
– volume: 24
  start-page: 2344
  year: 2008
  ident: 2023013112113923600_B24
  article-title: An HMM approach to genome-wide identification of differential histone modification sites from chip-seq data
  publication-title: Bioinformatics
  doi: 10.1093/bioinformatics/btn402
– volume: 6
  start-page: 173
  year: 2005
  ident: 2023013112113923600_B4
  article-title: Normal uniform mixture differential gene expression detection for cDNA microarrays
  publication-title: BMC Bioinformatics
  doi: 10.1186/1471-2105-6-173
– volume: 38
  start-page: 1289
  year: 2006
  ident: 2023013112113923600_B2
  article-title: Genome-wide analysis of estrogen receptor binding sites
  publication-title: Nat. Genet.
  doi: 10.1038/ng1901
– volume: 66
  start-page: 11954
  year: 2006
  ident: 2023013112113923600_B5
  article-title: Diverse gene expression and DNA methylation profiles correlate with differential adaptation of breast cancer cells to the antiestrogens tamoxifen and fulvestrant
  publication-title: Cancer Res.
  doi: 10.1158/0008-5472.CAN-06-1666
– start-page: 267
  volume-title: International Symposium on Information Theory
  year: 1973
  ident: 2023013112113923600_B1
  article-title: Information theory and an extension of the maximum likelihood principle
– volume: 26
  start-page: 1293
  year: 2008
  ident: 2023013112113923600_B9
  article-title: An integrated software system for analyzing ChIP-chip and ChIP-seq data
  publication-title: Nat. Biotechnol.
  doi: 10.1038/nbt.1505
– volume: 27
  start-page: 66
  year: 2009
  ident: 2023013112113923600_B18
  article-title: Peakseq enables systematic scoring of ChIP-seq experiments relative to controls
  publication-title: Nat. Biotechnol.
  doi: 10.1038/nbt.1518
– volume: 32
  start-page: 244
  year: 1961
  ident: 2023013112113923600_B21
  article-title: Identifiability of mixtures
  publication-title: Ann. Math. Stat.
  doi: 10.1214/aoms/1177705155
– volume: 23
  start-page: 301
  year: 1975
  ident: 2023013112113923600_B15
  article-title: New results in the design of digital interpolators
  publication-title: IEEE Trans. Acoust. Speech Signal Process. [see also IEEE Trans. Signal Process.]
  doi: 10.1109/TASSP.1975.1162686
– volume: 23
  start-page: 1616
  year: 2005
  ident: 2023013112113923600_B16
  article-title: Estrogen-receptor biology: Continuing progress and therapeutic implications
  publication-title: J. Clin. Oncol.
  doi: 10.1200/JCO.2005.10.036
– volume: 9
  start-page: R137+
  year: 2008
  ident: 2023013112113923600_B25
  article-title: Model-based analysis of ChIP-Seq (MACS)
  publication-title: Genome Biol.
  doi: 10.1186/gb-2008-9-9-r137
– volume: 350
  start-page: 469
  year: 2002
  ident: 2023013112113923600_B7
  article-title: ChIP-chip: a genomic approach for identifying transcription factor binding sites
  publication-title: Methods Enzymol
  doi: 10.1016/S0076-6879(02)50979-4
– volume: 53
  start-page: 1701
  year: 2009
  ident: 2023013112113923600_B11
  article-title: A robust unified approach to analyzing methylation and gene expression data
  publication-title: Comput. Stat. Data Anal.
  doi: 10.1016/j.csda.2008.07.010
– volume: 316
  start-page: 1441
  year: 2007
  ident: 2023013112113923600_B10
  article-title: Genome-wide mapping of in vivo protein-DNA interactions
  publication-title: Science
  doi: 10.1126/science.1141319
– volume: 5
  start-page: R66
  year: 2004
  ident: 2023013112113923600_B13
  article-title: Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells
  publication-title: Genome Biol.
  doi: 10.1186/gb-2004-5-9-r66
– volume: 3
  start-page: e87
  year: 2007
  ident: 2023013112113923600_B14
  article-title: Whole-genome cartography of estrogen receptor alpha binding sites
  publication-title: PLoS Genet.
  doi: 10.1371/journal.pgen.0030087
– volume: 93
  start-page: 10614
  year: 1996
  ident: 2023013112113923600_B19
  article-title: Parallel human genome analysis: microarray-based expression monitoring of 1000 genes
  publication-title: Proc. Natl Acad. Sci. USA
  doi: 10.1073/pnas.93.20.10614
– volume: 74
  start-page: 1111
  issue: Suppl. 3
  year: 2004
  ident: 2023013112113923600_B8
  article-title: Steroid hormones and risk of breast cancer
  publication-title: Cancer
– year: 2009
  ident: 2023013112113923600_B23
  article-title: ChIP-Seq of ERalpha and RNA polymerase II defines genes differentially responding to ligands
  publication-title: EMBO J.
  doi: 10.1038/emboj.2009.88
– volume: 9
  start-page: 349
  year: 2008
  ident: 2023013112113923600_B20
  article-title: Genome-wide analysis of alternative promoters of human genes using a custom promoter tiling array
  publication-title: BMC Genomics
  doi: 10.1186/1471-2164-9-349
– volume: 9
  start-page: S23
  issue: Suppl. 2
  year: 2008
  ident: 2023013112113923600_B6
  article-title: A poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology
  publication-title: BMC Genomics
  doi: 10.1186/1471-2164-9-S2-S23
– volume: 26
  start-page: 1351
  year: 2008
  ident: 2023013112113923600_B12
  article-title: Design and analysis of ChIP-seq experiments for DNA-binding proteins
  publication-title: Nature Biotechnol.
  doi: 10.1038/nbt.1508
– volume: 34
  start-page: 1265
  year: 1963
  ident: 2023013112113923600_B22
  article-title: Identifiability of finite mixtures
  publication-title: Ann. Math. Stat.
  doi: 10.1214/aoms/1177703862
– volume: 290
  start-page: 2306
  year: 2000
  ident: 2023013112113923600_B17
  article-title: Genome-wide location and function of DNA binding proteins
  publication-title: Science
  doi: 10.1126/science.290.5500.2306
– reference: 16011807 - BMC Bioinformatics. 2005;6:173
– reference: 18831789 - BMC Genomics. 2008;9 Suppl 2:S23
– reference: 17178894 - Cancer Res. 2006 Dec 15;66(24):11954-66
– reference: 19029915 - Nat Biotechnol. 2008 Dec;26(12):1351-9
– reference: 11125145 - Science. 2000 Dec 22;290(5500):2306-9
– reference: 18978777 - Nat Biotechnol. 2008 Nov;26(11):1293-300
– reference: 17542648 - PLoS Genet. 2007 Jun;3(6):e87
– reference: 17540862 - Science. 2007 Jun 8;316(5830):1497-502
– reference: 18655706 - BMC Genomics. 2008;9:349
– reference: 12073330 - Methods Enzymol. 2002;350:469-83
– reference: 17013392 - Nat Genet. 2006 Nov;38(11):1289-97
– reference: 18667444 - Bioinformatics. 2008 Oct 15;24(20):2344-9
– reference: 20161265 - Comput Stat Data Anal. 2009 Mar 15;53(5):1701-1710
– reference: 8855227 - Proc Natl Acad Sci U S A. 1996 Oct 1;93(20):10614-9
– reference: 8039146 - Cancer. 1994 Aug 1;74(3 Suppl):1111-24
– reference: 19339991 - EMBO J. 2009 May 20;28(10):1418-28
– reference: 15345050 - Genome Biol. 2004;5(9):R66
– reference: 15755967 - J Clin Oncol. 2005 Mar 10;23(8):1616-22
– reference: 18798982 - Genome Biol. 2008;9(9):R137
– reference: 19122651 - Nat Biotechnol. 2009 Jan;27(1):66-75
SSID ssj0051444
ssj0005056
Score 2.1892428
Snippet Motivation: Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study...
Antibody-based Chromatin Immunoprecipitation assay followed by high-throughput sequencing technology (ChIP-seq) is a relatively new method to study the binding...
SourceID pubmedcentral
proquest
pubmed
crossref
oup
istex
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 2334
SubjectTerms Binding Sites
Bioinformatics
Breast Neoplasms - metabolism
Cell Line, Tumor
Chromatin Immunoprecipitation - methods
Comparative studies
Computational Biology - methods
Female
Humans
Original Papers
RNA Polymerase II - metabolism
Sequence Analysis, DNA - methods
Title Comparative study on ChIP-seq data: normalization and binding pattern characterization
URI https://api.istex.fr/ark:/67375/HXZ-X45RQQJ7-9/fulltext.pdf
https://www.ncbi.nlm.nih.gov/pubmed/19561022
https://www.proquest.com/docview/198715322
https://www.proquest.com/docview/20935282
https://www.proquest.com/docview/34854020
https://www.proquest.com/docview/734035798
https://pubmed.ncbi.nlm.nih.gov/PMC2800347
Volume 25
WOSCitedRecordID wos000269607700007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVASL
  databaseName: Oxford Journals Open Access Collection
  customDbUrl:
  eissn: 1460-2059
  dateEnd: 20220930
  omitProxy: false
  ssIdentifier: ssj0005056
  issn: 1367-4803
  databaseCode: TOX
  dateStart: 19850101
  isFulltext: true
  titleUrlDefault: https://academic.oup.com/journals/
  providerName: Oxford University Press
– providerCode: PRVASL
  databaseName: Oxford Journals Open Access Collection
  customDbUrl:
  eissn: 1460-2059
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0005056
  issn: 1367-4803
  databaseCode: TOX
  dateStart: 19850101
  isFulltext: true
  titleUrlDefault: https://academic.oup.com/journals/
  providerName: Oxford University Press
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwED6NAdJe-D0Ig-EHhMRDmBM7sc0bqpgGD2NDA0W8WLbjaBVT2rUdgv8eX5y2a6UK9hj5bCXns3yXu_s-gNc844Wtc5tamruUS29SU3KfZrYWvmS-zmvekU2I42NZVepkC-i8F2Y9ha_YgR2OehBRBC4-sLMxkwgAmhUSKQvOvlTLmg6KyDDxIXgCPFLaIrK3pGzev7NpyZWb6TYq-fda19s153O9hvLapXR4_-af8wDu9Q4o-RAt5iFs-fYR3I2UlH8ew_fBEg6cdNizZNSSwfmnk3TqLwkWlL4nLa570XdwEtPWxA677hgy7uA6W-IWONBR6Al8O_x4NjhKe-6F1AUPaZZa13iRKecFr5krrGB1o3hZG9Nk3jPDyuD4NaXzDBkBKa1VkwfBMMk615SU7cJ2O2r9MyDBhwlRkVGSFpabEA4iJp1RjBe8UcqJBPhc79r1wOTIj3GhY4Kc6VWV6aiyBN4tpo0jMse_JrzpNnUhbSY_saxNFPqo-qErXnw9Pf0stErgbdj1_110b24buj_9U40_csJNkucJvFqMhmOLuRjT-tHVVOeYgA7h7mYJxmWBwX0CZIOEYJyyQiiZwNNojMt3xn5lii8gVsx0IYCo4qsj7fC8QxfPJWIWiec3UMIe7MQkm0qz4gVszyZX_iXccb9mw-lkH26JSu53B_Uvgh1F8Q
linkProvider Oxford University Press
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Comparative+study+on+ChIP-seq+data%3A+normalization+and+binding+pattern+characterization&rft.jtitle=Bioinformatics+%28Oxford%2C+England%29&rft.au=Taslim%2C+Cenny&rft.au=Wu%2C+Jiejun&rft.au=Yan%2C+Pearlly&rft.au=Singer%2C+Greg&rft.date=2009-09-15&rft.issn=1367-4803&rft.volume=25&rft.issue=18&rft.spage=2334&rft.epage=2340&rft_id=info:doi/10.1093%2Fbioinformatics%2Fbtp384&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4803&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4803&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4803&client=summon