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Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes
Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the li...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
De Gruyter
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042805/ https://www.ncbi.nlm.nih.gov/pubmed/28609292 http://dx.doi.org/10.1515/jib-2016-0002 |
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author | Hassani-Pak, Keywan Rawlings, Christopher |
author_facet | Hassani-Pak, Keywan Rawlings, Christopher |
author_sort | Hassani-Pak, Keywan |
collection | PubMed |
description | Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future. |
format | Online Article Text |
id | pubmed-6042805 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | De Gruyter |
record_format | MEDLINE/PubMed |
spelling | pubmed-60428052019-01-28 Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes Hassani-Pak, Keywan Rawlings, Christopher J Integr Bioinform Review Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future. De Gruyter 2017-06-13 /pmc/articles/PMC6042805/ /pubmed/28609292 http://dx.doi.org/10.1515/jib-2016-0002 Text en ©2017, Keywan Hassani-Pak, published by De Gruyter, Berlin/Boston http://creativecommons.org/licenses/by-nc-nd/3.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. |
spellingShingle | Review Hassani-Pak, Keywan Rawlings, Christopher Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes |
title | Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes |
title_full | Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes |
title_fullStr | Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes |
title_full_unstemmed | Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes |
title_short | Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes |
title_sort | knowledge discovery in biological databases for revealing candidate genes linked to complex phenotypes |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042805/ https://www.ncbi.nlm.nih.gov/pubmed/28609292 http://dx.doi.org/10.1515/jib-2016-0002 |
work_keys_str_mv | AT hassanipakkeywan knowledgediscoveryinbiologicaldatabasesforrevealingcandidategeneslinkedtocomplexphenotypes AT rawlingschristopher knowledgediscoveryinbiologicaldatabasesforrevealingcandidategeneslinkedtocomplexphenotypes |