Arabidopsis bioinformatics: tools and strategies
Summary The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other ‘‐omic’ data. In this r...
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Veröffentlicht in: | The Plant journal : for cell and molecular biology 2021-12, Vol.108 (6), p.1585-1596 |
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creator | Cantó‐Pastor, Alex Mason, G. Alex Brady, Siobhan M. Provart, Nicholas J. |
description | Summary
The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other ‘‐omic’ data. In this review, we cover some more recent tools (and highlight the ‘classics’) for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co‐expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein–protein and protein–DNA interactions. We will touch on tools that integrate different data sets at the end of the article.
Significance Statement
Bioinformatic tools have become an essential part of a researcher’s toolbox. We review how dozens of such tools can be used for hypothesis generation in Arabidopsis research. |
doi_str_mv | 10.1111/tpj.15547 |
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The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other ‘‐omic’ data. In this review, we cover some more recent tools (and highlight the ‘classics’) for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co‐expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein–protein and protein–DNA interactions. We will touch on tools that integrate different data sets at the end of the article.
Significance Statement
Bioinformatic tools have become an essential part of a researcher’s toolbox. We review how dozens of such tools can be used for hypothesis generation in Arabidopsis research.</description><identifier>ISSN: 0960-7412</identifier><identifier>EISSN: 1365-313X</identifier><identifier>DOI: 10.1111/tpj.15547</identifier><identifier>PMID: 34695270</identifier><language>eng</language><publisher>HOBOKEN: Wiley</publisher><subject>Arabidopsis - genetics ; Arabidopsis - metabolism ; Arabidopsis Proteins - genetics ; Arabidopsis Proteins - metabolism ; Bioinformatics ; Computational Biology - methods ; co‐expression ; Databases, Genetic ; Deoxyribonucleic acid ; DNA ; Epigenomics - methods ; functional genomics ; Gene expression ; Gene Expression Profiling ; Gene Ontology ; Genomics ; hypothesis generation ; Life Sciences & Biomedicine ; Plant Sciences ; Promoter Regions, Genetic ; Protein Interaction Maps - physiology ; Proteins ; protein–protein interactions ; Proteomics ; Science & Technology ; Transcriptomics</subject><ispartof>The Plant journal : for cell and molecular biology, 2021-12, Vol.108 (6), p.1585-1596</ispartof><rights>2021 Society for Experimental Biology and John Wiley & Sons Ltd.</rights><rights>Copyright © 2021 John Wiley & Sons Ltd and the Society for Experimental Biology</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>10</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000715983800001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c3887-eefd56b6b7bb34181136643169d41302014f48423b474b1cbe42519a7e0cee393</citedby><cites>FETCH-LOGICAL-c3887-eefd56b6b7bb34181136643169d41302014f48423b474b1cbe42519a7e0cee393</cites><orcidid>0000-0001-9424-8055 ; 0000-0001-5551-7232</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Ftpj.15547$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Ftpj.15547$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,781,785,1418,1434,27929,27930,39263,45579,45580,46414,46838</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34695270$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cantó‐Pastor, Alex</creatorcontrib><creatorcontrib>Mason, G. Alex</creatorcontrib><creatorcontrib>Brady, Siobhan M.</creatorcontrib><creatorcontrib>Provart, Nicholas J.</creatorcontrib><title>Arabidopsis bioinformatics: tools and strategies</title><title>The Plant journal : for cell and molecular biology</title><addtitle>PLANT J</addtitle><addtitle>Plant J</addtitle><description>Summary
The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other ‘‐omic’ data. In this review, we cover some more recent tools (and highlight the ‘classics’) for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co‐expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein–protein and protein–DNA interactions. We will touch on tools that integrate different data sets at the end of the article.
Significance Statement
Bioinformatic tools have become an essential part of a researcher’s toolbox. We review how dozens of such tools can be used for hypothesis generation in Arabidopsis research.</description><subject>Arabidopsis - genetics</subject><subject>Arabidopsis - metabolism</subject><subject>Arabidopsis Proteins - genetics</subject><subject>Arabidopsis Proteins - metabolism</subject><subject>Bioinformatics</subject><subject>Computational Biology - methods</subject><subject>co‐expression</subject><subject>Databases, Genetic</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>Epigenomics - methods</subject><subject>functional genomics</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Ontology</subject><subject>Genomics</subject><subject>hypothesis generation</subject><subject>Life Sciences & Biomedicine</subject><subject>Plant Sciences</subject><subject>Promoter Regions, Genetic</subject><subject>Protein Interaction Maps - physiology</subject><subject>Proteins</subject><subject>protein–protein interactions</subject><subject>Proteomics</subject><subject>Science & Technology</subject><subject>Transcriptomics</subject><issn>0960-7412</issn><issn>1365-313X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>EIF</sourceid><recordid>eNqNkE1LI0EQhhtx0Zjdg39AAl4UGdM9Xf0x3kJYdSWgBxf21kzP1EiHyXTsnkH899sx0YMgWJeqw1NVLw8hx4xeslTTfr28ZEKA2iMjxqXIOOP_9smIFpJmClh-SI5iXFLKFJdwQA45yELkio4InYXSutqvo4sT67zrGh9WZe-qeDXpvW_jpOzqSexD2eOTw_iT_GjKNuKvXR-Tv9e_H-e32eL-5s98tsgqrrXKEJtaSCutspYD0yzlksCZLGpgnOaUQQMacm5BgWWVRcgFK0qFtELkBR-Ts-3ddfDPA8berFyssG3LDv0QTS60BCi4gISefkKXfghdSmdymfxo2Lwfk_MtVQUfY8DGrINbleHVMGo2Gk3SaN40JvZkd3GwK6w_yHdvCbjYAi9ofRMrh12FHxilVDFRaK7TRDev9ffpueuTft_N_dD1aXW6W3Utvn4d2Tw-3G2z_wdugZpc</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>Cantó‐Pastor, Alex</creator><creator>Mason, G. Alex</creator><creator>Brady, Siobhan M.</creator><creator>Provart, Nicholas J.</creator><general>Wiley</general><general>Blackwell Publishing Ltd</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</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>7QO</scope><scope>7QP</scope><scope>7QR</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9424-8055</orcidid><orcidid>https://orcid.org/0000-0001-5551-7232</orcidid></search><sort><creationdate>202112</creationdate><title>Arabidopsis bioinformatics: tools and strategies</title><author>Cantó‐Pastor, Alex ; Mason, G. Alex ; Brady, Siobhan M. ; Provart, Nicholas J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3887-eefd56b6b7bb34181136643169d41302014f48423b474b1cbe42519a7e0cee393</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Arabidopsis - genetics</topic><topic>Arabidopsis - metabolism</topic><topic>Arabidopsis Proteins - genetics</topic><topic>Arabidopsis Proteins - metabolism</topic><topic>Bioinformatics</topic><topic>Computational Biology - methods</topic><topic>co‐expression</topic><topic>Databases, Genetic</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>Epigenomics - methods</topic><topic>functional genomics</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene Ontology</topic><topic>Genomics</topic><topic>hypothesis generation</topic><topic>Life Sciences & Biomedicine</topic><topic>Plant Sciences</topic><topic>Promoter Regions, Genetic</topic><topic>Protein Interaction Maps - physiology</topic><topic>Proteins</topic><topic>protein–protein interactions</topic><topic>Proteomics</topic><topic>Science & Technology</topic><topic>Transcriptomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cantó‐Pastor, Alex</creatorcontrib><creatorcontrib>Mason, G. Alex</creatorcontrib><creatorcontrib>Brady, Siobhan M.</creatorcontrib><creatorcontrib>Provart, Nicholas J.</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>The Plant journal : for cell and molecular biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cantó‐Pastor, Alex</au><au>Mason, G. Alex</au><au>Brady, Siobhan M.</au><au>Provart, Nicholas J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Arabidopsis bioinformatics: tools and strategies</atitle><jtitle>The Plant journal : for cell and molecular biology</jtitle><stitle>PLANT J</stitle><addtitle>Plant J</addtitle><date>2021-12</date><risdate>2021</risdate><volume>108</volume><issue>6</issue><spage>1585</spage><epage>1596</epage><pages>1585-1596</pages><issn>0960-7412</issn><eissn>1365-313X</eissn><abstract>Summary
The sequencing of the Arabidopsis thaliana genome 21 years ago ushered in the genomics era for plant research. Since then, an incredible variety of bioinformatic tools permit easy access to large repositories of genomic, transcriptomic, proteomic, epigenomic and other ‘‐omic’ data. In this review, we cover some more recent tools (and highlight the ‘classics’) for exploring such data in order to help formulate quality, testable hypotheses, often without having to generate new experimental data. We cover tools for examining gene expression and co‐expression patterns, undertaking promoter analyses and gene set enrichment analyses, and exploring protein–protein and protein–DNA interactions. We will touch on tools that integrate different data sets at the end of the article.
Significance Statement
Bioinformatic tools have become an essential part of a researcher’s toolbox. We review how dozens of such tools can be used for hypothesis generation in Arabidopsis research.</abstract><cop>HOBOKEN</cop><pub>Wiley</pub><pmid>34695270</pmid><doi>10.1111/tpj.15547</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-9424-8055</orcidid><orcidid>https://orcid.org/0000-0001-5551-7232</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Arabidopsis - genetics Arabidopsis - metabolism Arabidopsis Proteins - genetics Arabidopsis Proteins - metabolism Bioinformatics Computational Biology - methods co‐expression Databases, Genetic Deoxyribonucleic acid DNA Epigenomics - methods functional genomics Gene expression Gene Expression Profiling Gene Ontology Genomics hypothesis generation Life Sciences & Biomedicine Plant Sciences Promoter Regions, Genetic Protein Interaction Maps - physiology Proteins protein–protein interactions Proteomics Science & Technology Transcriptomics |
title | Arabidopsis bioinformatics: tools and strategies |
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