{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T09:05:42Z","timestamp":1768035942669,"version":"3.49.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2010,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations (e.g., for identifying genes related to susceptibility to complex diseases or genes playing key roles in development or performance). The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Description<\/jats:title>\n            <jats:p>We introduce an R package called FunctSNP, which is the user interface to custom built species-specific databases. The local relational databases contain SNP data together with functional annotations extracted from online resources. FunctSNP provides a unified bioinformatics resource to link SNPs with functional knowledge (e.g., genes, pathways, ontologies). We also introduce dbAutoMaker, a suite of Perl scripts, which can be scheduled to run periodically to automatically create\/update the customised SNP databases. We illustrate the use of FunctSNP with a livestock example, but the approach and software tools presented here can be applied also to human and other organisms.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Finding the potential functional significance of SNPs is important when further using the outcomes from whole genome association studies. FunctSNP is unique in that it is the only R package that links SNPs to functional annotation. FunctSNP interfaces to local SNP customised databases which can be built for any species contained in the National Center for Biotechnology Information dbSNP database.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-11-311","type":"journal-article","created":{"date-parts":[[2010,6,9]],"date-time":"2010-06-09T18:14:38Z","timestamp":1276107278000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["FunctSNP: an R package to link SNPs to functional knowledge and dbAutoMaker: a suite of Perl scripts to build SNP databases"],"prefix":"10.1186","volume":"11","author":[{"given":"Stephen J","family":"Goodswen","sequence":"first","affiliation":[]},{"given":"Cedric","family":"Gondro","sequence":"additional","affiliation":[]},{"given":"Nathan S","family":"Watson-Haigh","sequence":"additional","affiliation":[]},{"given":"Haja N","family":"Kadarmideen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,6,9]]},"reference":[{"issue":"11","key":"3768_CR1","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1001\/jama.299.11.1335","volume":"299","author":"TA Pearson","year":"2008","unstructured":"Pearson TA, Manolio TA: How to interpret a genome-wide association study. 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