Gene function analysis in complex data sets using ErmineJ
ErmineJ is software for the analysis of functionally interesting patterns in large gene lists drawn from gene expression profiling data or other high-throughput genomics studies. It can be used by biologists with no bioinformatics background to conduct sophisticated analyses of gene sets with multip...
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| Veröffentlicht in: | Nature protocols 2010-06, Vol.5 (6), p.1148-1159 |
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| creator | Gillis, Jesse Mistry, Meeta Pavlidis, Paul |
| description | ErmineJ is software for the analysis of functionally interesting patterns in large gene lists drawn from gene expression profiling data or other high-throughput genomics studies. It can be used by biologists with no bioinformatics background to conduct sophisticated analyses of gene sets with multiple methods. It allows users to assess whether microarray data or other gene lists are enriched for a particular pathway or gene class. This protocol provides steps on how to format data files, determine analysis type, create custom gene sets and perform specific analyses—including overrepresentation analysis, genes score resampling and correlation resampling. ErmineJ differs from other methods in providing a rapid, simple and customizable analysis, including high-level visualization through its graphical user interface and scripting tools through its command-line interface, as well as custom gene sets and a variety of statistical methods. The protocol should take approximately 1 h, including (one-time) installation and setup. |
| doi_str_mv | 10.1038/nprot.2010.78 |
| format | Article |
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It can be used by biologists with no bioinformatics background to conduct sophisticated analyses of gene sets with multiple methods. It allows users to assess whether microarray data or other gene lists are enriched for a particular pathway or gene class. This protocol provides steps on how to format data files, determine analysis type, create custom gene sets and perform specific analyses—including overrepresentation analysis, genes score resampling and correlation resampling. ErmineJ differs from other methods in providing a rapid, simple and customizable analysis, including high-level visualization through its graphical user interface and scripting tools through its command-line interface, as well as custom gene sets and a variety of statistical methods. The protocol should take approximately 1 h, including (one-time) installation and setup.</description><identifier>ISSN: 1754-2189</identifier><identifier>EISSN: 1750-2799</identifier><identifier>DOI: 10.1038/nprot.2010.78</identifier><identifier>PMID: 20539290</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/1647/794 ; 631/208/191 ; Algorithms ; Analytical Chemistry ; Animals ; Applications software ; Automatic Data Processing ; Bioinformatics ; Biological Techniques ; Biologists ; Biomedical and Life Sciences ; Computational Biology - methods ; Computational Biology/Bioinformatics ; Databases, Genetic ; DNA microarrays ; Gene expression ; Gene Expression Profiling - statistics & numerical data ; Genomics ; Genomics - statistics & numerical data ; Humans ; Life Sciences ; Methods ; Microarrays ; Oligonucleotide Array Sequence Analysis - statistics & numerical data ; Organic Chemistry ; Physiological aspects ; Protocol ; Software ; Statistical methods ; User interface ; User-Computer Interface</subject><ispartof>Nature protocols, 2010-06, Vol.5 (6), p.1148-1159</ispartof><rights>Springer Nature Limited 2010</rights><rights>COPYRIGHT 2010 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Jun 2010</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c595t-53c69abe5e6261708a4a439577af9d20a6870bcae2696d87573ac6c9727777963</citedby><cites>FETCH-LOGICAL-c595t-53c69abe5e6261708a4a439577af9d20a6870bcae2696d87573ac6c9727777963</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/nprot.2010.78$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/nprot.2010.78$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51298</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20539290$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gillis, Jesse</creatorcontrib><creatorcontrib>Mistry, Meeta</creatorcontrib><creatorcontrib>Pavlidis, Paul</creatorcontrib><title>Gene function analysis in complex data sets using ErmineJ</title><title>Nature protocols</title><addtitle>Nat Protoc</addtitle><addtitle>Nat Protoc</addtitle><description>ErmineJ is software for the analysis of functionally interesting patterns in large gene lists drawn from gene expression profiling data or other high-throughput genomics studies. It can be used by biologists with no bioinformatics background to conduct sophisticated analyses of gene sets with multiple methods. It allows users to assess whether microarray data or other gene lists are enriched for a particular pathway or gene class. This protocol provides steps on how to format data files, determine analysis type, create custom gene sets and perform specific analyses—including overrepresentation analysis, genes score resampling and correlation resampling. ErmineJ differs from other methods in providing a rapid, simple and customizable analysis, including high-level visualization through its graphical user interface and scripting tools through its command-line interface, as well as custom gene sets and a variety of statistical methods. The protocol should take approximately 1 h, including (one-time) installation and setup.</description><subject>631/1647/794</subject><subject>631/208/191</subject><subject>Algorithms</subject><subject>Analytical Chemistry</subject><subject>Animals</subject><subject>Applications software</subject><subject>Automatic Data Processing</subject><subject>Bioinformatics</subject><subject>Biological Techniques</subject><subject>Biologists</subject><subject>Biomedical and Life Sciences</subject><subject>Computational Biology - methods</subject><subject>Computational Biology/Bioinformatics</subject><subject>Databases, Genetic</subject><subject>DNA microarrays</subject><subject>Gene expression</subject><subject>Gene Expression Profiling - statistics & numerical data</subject><subject>Genomics</subject><subject>Genomics - statistics & numerical data</subject><subject>Humans</subject><subject>Life Sciences</subject><subject>Methods</subject><subject>Microarrays</subject><subject>Oligonucleotide Array Sequence Analysis - statistics & numerical data</subject><subject>Organic Chemistry</subject><subject>Physiological aspects</subject><subject>Protocol</subject><subject>Software</subject><subject>Statistical methods</subject><subject>User interface</subject><subject>User-Computer Interface</subject><issn>1754-2189</issn><issn>1750-2799</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp90s1rFDEUAPBQKrZWj15loAcVmTUfk6_jstRaKRa00mPIZt4sKTOZNclA-9-b7Yd1pW1ySDLzy0teeAi9JXhGMFOfwzqOeUZxWUu1g_aJ5LimUuvdm3lTU6L0HnqV0iXGjWRCvkR7FHOmqcb7SB9DgKqbgst-DJUNtr9OPlU-VG4c1j1cVa3NtkqQUzUlH1bVURx8gG-v0YvO9gne3I0H6NeXo_PF1_r07PhkMT-tHdc815w5oe0SOAgqiMTKNrZhmktpO91SbIWSeOksUKFFqySXzDrhtKSyNC3YAXp_G7ck-nuClM3gk4O-twHGKRnJGFWCMVzkh2clKedSpogihR7-Ry_HKZbki2JU0IYIyh7UyvZgfOjGHK3bBDVzhoXi5Xq0qNkjqvQWBu_GAJ0v37c2fNzaUEyGq7yyU0rm5OePbfvpaTs_v1h839b1rXZxTClCZ9bRDzZeG4LNplzMTbmYTbkYqYp_d_cM03KA9q--r4-H3FL5FVYQ_3mnRyP-AY-qxOg</recordid><startdate>201006</startdate><enddate>201006</enddate><creator>Gillis, Jesse</creator><creator>Mistry, Meeta</creator><creator>Pavlidis, Paul</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</general><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>ATWCN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7T5</scope><scope>7T7</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P64</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7QO</scope><scope>7X8</scope></search><sort><creationdate>201006</creationdate><title>Gene function analysis in complex data sets using ErmineJ</title><author>Gillis, Jesse ; Mistry, Meeta ; Pavlidis, Paul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c595t-53c69abe5e6261708a4a439577af9d20a6870bcae2696d87573ac6c9727777963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>631/1647/794</topic><topic>631/208/191</topic><topic>Algorithms</topic><topic>Analytical Chemistry</topic><topic>Animals</topic><topic>Applications software</topic><topic>Automatic Data Processing</topic><topic>Bioinformatics</topic><topic>Biological Techniques</topic><topic>Biologists</topic><topic>Biomedical and Life Sciences</topic><topic>Computational Biology - methods</topic><topic>Computational Biology/Bioinformatics</topic><topic>Databases, Genetic</topic><topic>DNA microarrays</topic><topic>Gene expression</topic><topic>Gene Expression Profiling - statistics & numerical data</topic><topic>Genomics</topic><topic>Genomics - statistics & numerical data</topic><topic>Humans</topic><topic>Life Sciences</topic><topic>Methods</topic><topic>Microarrays</topic><topic>Oligonucleotide Array Sequence Analysis - statistics & numerical data</topic><topic>Organic Chemistry</topic><topic>Physiological aspects</topic><topic>Protocol</topic><topic>Software</topic><topic>Statistical methods</topic><topic>User interface</topic><topic>User-Computer Interface</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gillis, Jesse</creatorcontrib><creatorcontrib>Mistry, Meeta</creatorcontrib><creatorcontrib>Pavlidis, Paul</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Middle School</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Nature protocols</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gillis, Jesse</au><au>Mistry, Meeta</au><au>Pavlidis, Paul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gene function analysis in complex data sets using ErmineJ</atitle><jtitle>Nature protocols</jtitle><stitle>Nat Protoc</stitle><addtitle>Nat Protoc</addtitle><date>2010-06</date><risdate>2010</risdate><volume>5</volume><issue>6</issue><spage>1148</spage><epage>1159</epage><pages>1148-1159</pages><issn>1754-2189</issn><eissn>1750-2799</eissn><abstract>ErmineJ is software for the analysis of functionally interesting patterns in large gene lists drawn from gene expression profiling data or other high-throughput genomics studies. It can be used by biologists with no bioinformatics background to conduct sophisticated analyses of gene sets with multiple methods. It allows users to assess whether microarray data or other gene lists are enriched for a particular pathway or gene class. This protocol provides steps on how to format data files, determine analysis type, create custom gene sets and perform specific analyses—including overrepresentation analysis, genes score resampling and correlation resampling. ErmineJ differs from other methods in providing a rapid, simple and customizable analysis, including high-level visualization through its graphical user interface and scripting tools through its command-line interface, as well as custom gene sets and a variety of statistical methods. The protocol should take approximately 1 h, including (one-time) installation and setup.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>20539290</pmid><doi>10.1038/nprot.2010.78</doi><tpages>12</tpages></addata></record> |
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| subjects | 631/1647/794 631/208/191 Algorithms Analytical Chemistry Animals Applications software Automatic Data Processing Bioinformatics Biological Techniques Biologists Biomedical and Life Sciences Computational Biology - methods Computational Biology/Bioinformatics Databases, Genetic DNA microarrays Gene expression Gene Expression Profiling - statistics & numerical data Genomics Genomics - statistics & numerical data Humans Life Sciences Methods Microarrays Oligonucleotide Array Sequence Analysis - statistics & numerical data Organic Chemistry Physiological aspects Protocol Software Statistical methods User interface User-Computer Interface |
| title | Gene function analysis in complex data sets using ErmineJ |
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