Loading…
Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation
Motivation: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulat...
Saved in:
Published in: | Bioinformatics 2006-08, Vol.22 (16), p.2005-2011 |
---|---|
Main Authors: | , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c433t-4d50f75ad7277a9a70a14bdf0e14bf2713c5a9e25f7f656ba9680b35949471ff3 |
---|---|
cites | cdi_FETCH-LOGICAL-c433t-4d50f75ad7277a9a70a14bdf0e14bf2713c5a9e25f7f656ba9680b35949471ff3 |
container_end_page | 2011 |
container_issue | 16 |
container_start_page | 2005 |
container_title | Bioinformatics |
container_volume | 22 |
creator | Joung, Je-Gun Shin, Dongho Seong, Rho Hyun Zhang, Byoung-Tak |
description | Motivation: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulatory modules. To obtain a unified description of the regulatory modules, we characterized major stem cell species by employing a co-clustering latent variable model (LVM). The LVM-based method allowed us to elucidate the cell type-specific transcription factors, using genomic sequences as well as expression profiles. Results: We used a list of genes enriched in each of 21 stem cell subpopulations, and their upstream genomic sequences. The LVM-based study allowed us to uncover the regulatory modules for each stem cell cluster, e.g. GABP and E2F for the proliferation phase, and Ap2α and Ap2γ for the quiescence phase. Furthermore, the identities of the stem cell clusters were well revealed by the constituent genes that were directly targeted by the modules. Consequently, our analytical framework was demonstrated to be useful through a detailed case study of stem cell differentiation and can be applied to problems with similar characteristics. Contact:btzhang@bi.snu.ac.kr, rhseong@snu.ac.kr Supplementary Information: Supplementary data are available at . |
doi_str_mv | 10.1093/bioinformatics/btl343 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_68727707</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>19465006</sourcerecordid><originalsourceid>FETCH-LOGICAL-c433t-4d50f75ad7277a9a70a14bdf0e14bf2713c5a9e25f7f656ba9680b35949471ff3</originalsourceid><addsrcrecordid>eNqFkU1rFTEUhoMotlZ_ghIE3Y09mXxN3EmxH1CxQgVxEzKZ5JKambTJjPT-ezPei0U3rk7gPO9DDi9CLwm8I6DocR9SmHzKo5mDLcf9HCmjj9AhYQKaFrh6XN9UyIZ1QA_Qs1JuADhhjD1FB0R0SjFFDtHmYnDTHHyw1ZMmnDzObrNEM6e8xWMalugK7rfYpsbGpcwuh2mD677G8E-Tg-mjW0EXy3tc9yO2LkY8BO9dXt2_xc_RE29icS_28wh9Pf14fXLeXH4-uzj5cNlYRuncsIGDl9wMspXSKCPBENYPHlwdvpWEWm6Ua7mXXnDRGyU66CmvxzBJvKdH6O3Oe5vT3eLKrMdQ1g-ZyaWlaNGtZpD_BYliggOICr7-B7xJS57qEZXpJO0EpxXiO8jmVEp2Xt_mMJq81QT02pf-uy-966vmXu3lSz-64SG1L6gCb_aAKdZEn81kQ3ngOmCK87ZyzY4LtYL7P3uTf2ghqeT6_Nt3_YnyL2dX16Cv6C9nR7Q4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>198738653</pqid></control><display><type>article</type><title>Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation</title><source>Oxford Academic Journals (Open Access)</source><creator>Joung, Je-Gun ; Shin, Dongho ; Seong, Rho Hyun ; Zhang, Byoung-Tak</creator><creatorcontrib>Joung, Je-Gun ; Shin, Dongho ; Seong, Rho Hyun ; Zhang, Byoung-Tak</creatorcontrib><description>Motivation: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulatory modules. To obtain a unified description of the regulatory modules, we characterized major stem cell species by employing a co-clustering latent variable model (LVM). The LVM-based method allowed us to elucidate the cell type-specific transcription factors, using genomic sequences as well as expression profiles. Results: We used a list of genes enriched in each of 21 stem cell subpopulations, and their upstream genomic sequences. The LVM-based study allowed us to uncover the regulatory modules for each stem cell cluster, e.g. GABP and E2F for the proliferation phase, and Ap2α and Ap2γ for the quiescence phase. Furthermore, the identities of the stem cell clusters were well revealed by the constituent genes that were directly targeted by the modules. Consequently, our analytical framework was demonstrated to be useful through a detailed case study of stem cell differentiation and can be applied to problems with similar characteristics. Contact:btzhang@bi.snu.ac.kr, rhseong@snu.ac.kr Supplementary Information: Supplementary data are available at .</description><identifier>ISSN: 1367-4803</identifier><identifier>EISSN: 1460-2059</identifier><identifier>EISSN: 1367-4811</identifier><identifier>DOI: 10.1093/bioinformatics/btl343</identifier><identifier>PMID: 16899491</identifier><identifier>CODEN: BOINFP</identifier><language>eng</language><publisher>Oxford: Oxford University Press</publisher><subject>Algorithms ; Animals ; Biological and medical sciences ; Cell Differentiation ; Cell Lineage ; Cell Proliferation ; Cluster Analysis ; Computational Biology - methods ; Fundamental and applied biological sciences. Psychology ; Gene Expression Profiling ; General aspects ; Genome ; Humans ; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) ; Multigene Family ; Oligonucleotide Array Sequence Analysis ; Pattern Recognition, Automated ; Stem Cells - cytology</subject><ispartof>Bioinformatics, 2006-08, Vol.22 (16), p.2005-2011</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright Oxford University Press(England) Aug 15, 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c433t-4d50f75ad7277a9a70a14bdf0e14bf2713c5a9e25f7f656ba9680b35949471ff3</citedby><cites>FETCH-LOGICAL-c433t-4d50f75ad7277a9a70a14bdf0e14bf2713c5a9e25f7f656ba9680b35949471ff3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18049552$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16899491$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Joung, Je-Gun</creatorcontrib><creatorcontrib>Shin, Dongho</creatorcontrib><creatorcontrib>Seong, Rho Hyun</creatorcontrib><creatorcontrib>Zhang, Byoung-Tak</creatorcontrib><title>Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation</title><title>Bioinformatics</title><addtitle>Bioinformatics</addtitle><description>Motivation: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulatory modules. To obtain a unified description of the regulatory modules, we characterized major stem cell species by employing a co-clustering latent variable model (LVM). The LVM-based method allowed us to elucidate the cell type-specific transcription factors, using genomic sequences as well as expression profiles. Results: We used a list of genes enriched in each of 21 stem cell subpopulations, and their upstream genomic sequences. The LVM-based study allowed us to uncover the regulatory modules for each stem cell cluster, e.g. GABP and E2F for the proliferation phase, and Ap2α and Ap2γ for the quiescence phase. Furthermore, the identities of the stem cell clusters were well revealed by the constituent genes that were directly targeted by the modules. Consequently, our analytical framework was demonstrated to be useful through a detailed case study of stem cell differentiation and can be applied to problems with similar characteristics. Contact:btzhang@bi.snu.ac.kr, rhseong@snu.ac.kr Supplementary Information: Supplementary data are available at .</description><subject>Algorithms</subject><subject>Animals</subject><subject>Biological and medical sciences</subject><subject>Cell Differentiation</subject><subject>Cell Lineage</subject><subject>Cell Proliferation</subject><subject>Cluster Analysis</subject><subject>Computational Biology - methods</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Gene Expression Profiling</subject><subject>General aspects</subject><subject>Genome</subject><subject>Humans</subject><subject>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</subject><subject>Multigene Family</subject><subject>Oligonucleotide Array Sequence Analysis</subject><subject>Pattern Recognition, Automated</subject><subject>Stem Cells - cytology</subject><issn>1367-4803</issn><issn>1460-2059</issn><issn>1367-4811</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><recordid>eNqFkU1rFTEUhoMotlZ_ghIE3Y09mXxN3EmxH1CxQgVxEzKZ5JKambTJjPT-ezPei0U3rk7gPO9DDi9CLwm8I6DocR9SmHzKo5mDLcf9HCmjj9AhYQKaFrh6XN9UyIZ1QA_Qs1JuADhhjD1FB0R0SjFFDtHmYnDTHHyw1ZMmnDzObrNEM6e8xWMalugK7rfYpsbGpcwuh2mD677G8E-Tg-mjW0EXy3tc9yO2LkY8BO9dXt2_xc_RE29icS_28wh9Pf14fXLeXH4-uzj5cNlYRuncsIGDl9wMspXSKCPBENYPHlwdvpWEWm6Ua7mXXnDRGyU66CmvxzBJvKdH6O3Oe5vT3eLKrMdQ1g-ZyaWlaNGtZpD_BYliggOICr7-B7xJS57qEZXpJO0EpxXiO8jmVEp2Xt_mMJq81QT02pf-uy-966vmXu3lSz-64SG1L6gCb_aAKdZEn81kQ3ngOmCK87ZyzY4LtYL7P3uTf2ghqeT6_Nt3_YnyL2dX16Cv6C9nR7Q4</recordid><startdate>20060815</startdate><enddate>20060815</enddate><creator>Joung, Je-Gun</creator><creator>Shin, Dongho</creator><creator>Seong, Rho Hyun</creator><creator>Zhang, Byoung-Tak</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>BSCLL</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7TO</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>20060815</creationdate><title>Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation</title><author>Joung, Je-Gun ; Shin, Dongho ; Seong, Rho Hyun ; Zhang, Byoung-Tak</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c433t-4d50f75ad7277a9a70a14bdf0e14bf2713c5a9e25f7f656ba9680b35949471ff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Biological and medical sciences</topic><topic>Cell Differentiation</topic><topic>Cell Lineage</topic><topic>Cell Proliferation</topic><topic>Cluster Analysis</topic><topic>Computational Biology - methods</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Gene Expression Profiling</topic><topic>General aspects</topic><topic>Genome</topic><topic>Humans</topic><topic>Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)</topic><topic>Multigene Family</topic><topic>Oligonucleotide Array Sequence Analysis</topic><topic>Pattern Recognition, Automated</topic><topic>Stem Cells - cytology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Joung, Je-Gun</creatorcontrib><creatorcontrib>Shin, Dongho</creatorcontrib><creatorcontrib>Seong, Rho Hyun</creatorcontrib><creatorcontrib>Zhang, Byoung-Tak</creatorcontrib><collection>Istex</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Joung, Je-Gun</au><au>Shin, Dongho</au><au>Seong, Rho Hyun</au><au>Zhang, Byoung-Tak</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation</atitle><jtitle>Bioinformatics</jtitle><addtitle>Bioinformatics</addtitle><date>2006-08-15</date><risdate>2006</risdate><volume>22</volume><issue>16</issue><spage>2005</spage><epage>2011</epage><pages>2005-2011</pages><issn>1367-4803</issn><eissn>1460-2059</eissn><eissn>1367-4811</eissn><coden>BOINFP</coden><abstract>Motivation: An important issue in stem cell biology is to understand how to direct differentiation towards a specific cell type. To elucidate the mechanism, previous studies have focused on identifying the responsible gene regulators, which have, however, failed to provide a systemic view of regulatory modules. To obtain a unified description of the regulatory modules, we characterized major stem cell species by employing a co-clustering latent variable model (LVM). The LVM-based method allowed us to elucidate the cell type-specific transcription factors, using genomic sequences as well as expression profiles. Results: We used a list of genes enriched in each of 21 stem cell subpopulations, and their upstream genomic sequences. The LVM-based study allowed us to uncover the regulatory modules for each stem cell cluster, e.g. GABP and E2F for the proliferation phase, and Ap2α and Ap2γ for the quiescence phase. Furthermore, the identities of the stem cell clusters were well revealed by the constituent genes that were directly targeted by the modules. Consequently, our analytical framework was demonstrated to be useful through a detailed case study of stem cell differentiation and can be applied to problems with similar characteristics. Contact:btzhang@bi.snu.ac.kr, rhseong@snu.ac.kr Supplementary Information: Supplementary data are available at .</abstract><cop>Oxford</cop><pub>Oxford University Press</pub><pmid>16899491</pmid><doi>10.1093/bioinformatics/btl343</doi><tpages>7</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1367-4803 |
ispartof | Bioinformatics, 2006-08, Vol.22 (16), p.2005-2011 |
issn | 1367-4803 1460-2059 1367-4811 |
language | eng |
recordid | cdi_proquest_miscellaneous_68727707 |
source | Oxford Academic Journals (Open Access) |
subjects | Algorithms Animals Biological and medical sciences Cell Differentiation Cell Lineage Cell Proliferation Cluster Analysis Computational Biology - methods Fundamental and applied biological sciences. Psychology Gene Expression Profiling General aspects Genome Humans Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Multigene Family Oligonucleotide Array Sequence Analysis Pattern Recognition, Automated Stem Cells - cytology |
title | Identification of regulatory modules by co-clustering latent variable models: stem cell differentiation |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T23%3A15%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Identification%20of%20regulatory%20modules%20by%20co-clustering%20latent%20variable%20models:%20stem%20cell%20differentiation&rft.jtitle=Bioinformatics&rft.au=Joung,%20Je-Gun&rft.date=2006-08-15&rft.volume=22&rft.issue=16&rft.spage=2005&rft.epage=2011&rft.pages=2005-2011&rft.issn=1367-4803&rft.eissn=1460-2059&rft.coden=BOINFP&rft_id=info:doi/10.1093/bioinformatics/btl343&rft_dat=%3Cproquest_cross%3E19465006%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c433t-4d50f75ad7277a9a70a14bdf0e14bf2713c5a9e25f7f656ba9680b35949471ff3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=198738653&rft_id=info:pmid/16899491&rfr_iscdi=true |