RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci
Background As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds t...
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| Veröffentlicht in: | Gigascience 2024-06, Vol.13 |
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| creator | Shrestha, Anish M S Gonzales, Mark Edward M Ong, Phoebe Clare L Larmande, Pierre Lee, Hyun-Sook Jeung, Ji-Ung Kohli, Ajay Chebotarov, Dmytro Mauleon, Ramil P Lee, Jae-Sung McNally, Kenneth L |
| description | Background
As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.
Results
We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.
Conclusions
RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf. |
| doi_str_mv | 10.1093/gigascience/giae013 |
| format | Article |
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As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.
Results
We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.
Conclusions
RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.</description><identifier>ISSN: 2047-217X</identifier><identifier>EISSN: 2047-217X</identifier><identifier>DOI: 10.1093/gigascience/giae013</identifier><identifier>PMID: 38832465</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Applications programs ; Bioinformatics ; Biological effects ; Chromosome Mapping ; Collating ; Computational Biology - methods ; Data Mining ; Databases, Genetic ; Drought ; Epigenetics ; Epigenomics - methods ; Gene loci ; Genes ; Genetics ; Genome, Plant ; Genome-wide association studies ; Genome-Wide Association Study ; Genomes ; Genomic analysis ; Genomics ; Genomics - methods ; Life Sciences ; Nucleotides ; Ontology ; Oryza - genetics ; Plant breeding ; Plants genetics ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; Rice ; Single-nucleotide polymorphism ; Software ; Source code ; Technical Note ; Vegetal Biology</subject><ispartof>Gigascience, 2024-06, Vol.13</ispartof><rights>The Author(s) 2024. Published by Oxford University Press on behalf of GigaScience. 2024</rights><rights>The Author(s) 2024. Published by Oxford University Press on behalf of GigaScience.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-1959-6209 ; 0000-0001-8512-144X ; 0009-0004-7982-7314 ; 0000-0002-2923-9790 ; 0000-0001-5050-3157 ; 0000-0002-3644-4901 ; 0000-0002-7325-5798 ; 0000-0002-9192-9709 ; 0000-0002-9613-5537 ; 0000-0003-1351-9453 ; 0000-0002-7578-2081</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148593/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148593/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,1604,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38832465$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-04642135$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Shrestha, Anish M S</creatorcontrib><creatorcontrib>Gonzales, Mark Edward M</creatorcontrib><creatorcontrib>Ong, Phoebe Clare L</creatorcontrib><creatorcontrib>Larmande, Pierre</creatorcontrib><creatorcontrib>Lee, Hyun-Sook</creatorcontrib><creatorcontrib>Jeung, Ji-Ung</creatorcontrib><creatorcontrib>Kohli, Ajay</creatorcontrib><creatorcontrib>Chebotarov, Dmytro</creatorcontrib><creatorcontrib>Mauleon, Ramil P</creatorcontrib><creatorcontrib>Lee, Jae-Sung</creatorcontrib><creatorcontrib>McNally, Kenneth L</creatorcontrib><title>RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci</title><title>Gigascience</title><addtitle>Gigascience</addtitle><description>Background
As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.
Results
We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.
Conclusions
RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.</description><subject>Applications programs</subject><subject>Bioinformatics</subject><subject>Biological effects</subject><subject>Chromosome Mapping</subject><subject>Collating</subject><subject>Computational Biology - methods</subject><subject>Data Mining</subject><subject>Databases, Genetic</subject><subject>Drought</subject><subject>Epigenetics</subject><subject>Epigenomics - methods</subject><subject>Gene loci</subject><subject>Genes</subject><subject>Genetics</subject><subject>Genome, Plant</subject><subject>Genome-wide association studies</subject><subject>Genome-Wide Association Study</subject><subject>Genomes</subject><subject>Genomic analysis</subject><subject>Genomics</subject><subject>Genomics - methods</subject><subject>Life Sciences</subject><subject>Nucleotides</subject><subject>Ontology</subject><subject>Oryza - genetics</subject><subject>Plant breeding</subject><subject>Plants genetics</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Quantitative Trait Loci</subject><subject>Rice</subject><subject>Single-nucleotide polymorphism</subject><subject>Software</subject><subject>Source code</subject><subject>Technical Note</subject><subject>Vegetal Biology</subject><issn>2047-217X</issn><issn>2047-217X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNpdkt1u1DAQhSMEotXSJ0BClrgBadPacX683KBVBS3SSvwVwV00cSaJq8QOtrO0L8kz4bDLaqlvPJr5fM6RNVH0nNFzRlf8olUtOKlQSww1IGX8UXSa0LSIE1b8eHxUn0Rnzt3ScIpCiII_jU64EDxJ8-w0-v1FSfykemjeECCjcT6--r7-evH5ZkNqcF1lwNbEG6K0x9aCRzKCblGbQcklkQbvRovOKaOXxGI79eCNvV8SHNWBMtqb3rShO4LvfkEoQAdVvPPxoLTSbZBvjB3AB53ZbbRmq2okzaTl3IM-EE61nXdzEkNsiE1CSPdXaY5MeiPVs-hJA73Ds_29iL69f3dzeR1vPl59uFxv4o6n3McVVGkKTDLRVMBzloqsqKu8QYZ1IRtI6SrPAURdwUqwVCYNp7IWlEqBAnnGF9Hbne44VQPWErW30JejVQPY-9KAKv-faNWVrdmWjM1mKx4UXu8Uugfvrtebcu7RNE8TxrMtC-yrvZs1Pyd0vhyUk9j3oNFMruQ0TzPBaUYD-vIBemsmG_4vUIyznGWr4L6IXhzHP_j_24sAnO8AM42HKaPlvHvl0e6V-93jfwDurNKr</recordid><startdate>20240604</startdate><enddate>20240604</enddate><creator>Shrestha, Anish M S</creator><creator>Gonzales, Mark Edward M</creator><creator>Ong, Phoebe Clare L</creator><creator>Larmande, Pierre</creator><creator>Lee, Hyun-Sook</creator><creator>Jeung, Ji-Ung</creator><creator>Kohli, Ajay</creator><creator>Chebotarov, Dmytro</creator><creator>Mauleon, Ramil P</creator><creator>Lee, Jae-Sung</creator><creator>McNally, Kenneth L</creator><general>Oxford University Press</general><scope>TOX</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>JQ2</scope><scope>K9.</scope><scope>7X8</scope><scope>1XC</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1959-6209</orcidid><orcidid>https://orcid.org/0000-0001-8512-144X</orcidid><orcidid>https://orcid.org/0009-0004-7982-7314</orcidid><orcidid>https://orcid.org/0000-0002-2923-9790</orcidid><orcidid>https://orcid.org/0000-0001-5050-3157</orcidid><orcidid>https://orcid.org/0000-0002-3644-4901</orcidid><orcidid>https://orcid.org/0000-0002-7325-5798</orcidid><orcidid>https://orcid.org/0000-0002-9192-9709</orcidid><orcidid>https://orcid.org/0000-0002-9613-5537</orcidid><orcidid>https://orcid.org/0000-0003-1351-9453</orcidid><orcidid>https://orcid.org/0000-0002-7578-2081</orcidid></search><sort><creationdate>20240604</creationdate><title>RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci</title><author>Shrestha, Anish M S ; Gonzales, Mark Edward M ; Ong, Phoebe Clare L ; Larmande, Pierre ; Lee, Hyun-Sook ; Jeung, Ji-Ung ; Kohli, Ajay ; Chebotarov, Dmytro ; Mauleon, Ramil P ; Lee, Jae-Sung ; McNally, Kenneth L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-h343t-bab44a1c18fba3614857db6fe1ed7cfa40966aa8dba9814c2f30cd800c8e8e353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Applications programs</topic><topic>Bioinformatics</topic><topic>Biological effects</topic><topic>Chromosome Mapping</topic><topic>Collating</topic><topic>Computational Biology - methods</topic><topic>Data Mining</topic><topic>Databases, Genetic</topic><topic>Drought</topic><topic>Epigenetics</topic><topic>Epigenomics - methods</topic><topic>Gene loci</topic><topic>Genes</topic><topic>Genetics</topic><topic>Genome, Plant</topic><topic>Genome-wide association studies</topic><topic>Genome-Wide Association Study</topic><topic>Genomes</topic><topic>Genomic analysis</topic><topic>Genomics</topic><topic>Genomics - methods</topic><topic>Life Sciences</topic><topic>Nucleotides</topic><topic>Ontology</topic><topic>Oryza - genetics</topic><topic>Plant breeding</topic><topic>Plants genetics</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Quantitative Trait Loci</topic><topic>Rice</topic><topic>Single-nucleotide polymorphism</topic><topic>Software</topic><topic>Source code</topic><topic>Technical Note</topic><topic>Vegetal Biology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shrestha, Anish M S</creatorcontrib><creatorcontrib>Gonzales, Mark Edward M</creatorcontrib><creatorcontrib>Ong, Phoebe Clare L</creatorcontrib><creatorcontrib>Larmande, Pierre</creatorcontrib><creatorcontrib>Lee, Hyun-Sook</creatorcontrib><creatorcontrib>Jeung, Ji-Ung</creatorcontrib><creatorcontrib>Kohli, Ajay</creatorcontrib><creatorcontrib>Chebotarov, Dmytro</creatorcontrib><creatorcontrib>Mauleon, Ramil P</creatorcontrib><creatorcontrib>Lee, Jae-Sung</creatorcontrib><creatorcontrib>McNally, Kenneth L</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Gigascience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shrestha, Anish M S</au><au>Gonzales, Mark Edward M</au><au>Ong, Phoebe Clare L</au><au>Larmande, Pierre</au><au>Lee, Hyun-Sook</au><au>Jeung, Ji-Ung</au><au>Kohli, Ajay</au><au>Chebotarov, Dmytro</au><au>Mauleon, Ramil P</au><au>Lee, Jae-Sung</au><au>McNally, Kenneth L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci</atitle><jtitle>Gigascience</jtitle><addtitle>Gigascience</addtitle><date>2024-06-04</date><risdate>2024</risdate><volume>13</volume><issn>2047-217X</issn><eissn>2047-217X</eissn><abstract>Background
As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.
Results
We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.
Conclusions
RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>38832465</pmid><doi>10.1093/gigascience/giae013</doi><orcidid>https://orcid.org/0000-0002-1959-6209</orcidid><orcidid>https://orcid.org/0000-0001-8512-144X</orcidid><orcidid>https://orcid.org/0009-0004-7982-7314</orcidid><orcidid>https://orcid.org/0000-0002-2923-9790</orcidid><orcidid>https://orcid.org/0000-0001-5050-3157</orcidid><orcidid>https://orcid.org/0000-0002-3644-4901</orcidid><orcidid>https://orcid.org/0000-0002-7325-5798</orcidid><orcidid>https://orcid.org/0000-0002-9192-9709</orcidid><orcidid>https://orcid.org/0000-0002-9613-5537</orcidid><orcidid>https://orcid.org/0000-0003-1351-9453</orcidid><orcidid>https://orcid.org/0000-0002-7578-2081</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Oxford Journals Open Access Collection; PubMed Central |
| subjects | Applications programs Bioinformatics Biological effects Chromosome Mapping Collating Computational Biology - methods Data Mining Databases, Genetic Drought Epigenetics Epigenomics - methods Gene loci Genes Genetics Genome, Plant Genome-wide association studies Genome-Wide Association Study Genomes Genomic analysis Genomics Genomics - methods Life Sciences Nucleotides Ontology Oryza - genetics Plant breeding Plants genetics Polymorphism, Single Nucleotide Quantitative Trait Loci Rice Single-nucleotide polymorphism Software Source code Technical Note Vegetal Biology |
| title | RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci |
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