Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes

A - Papers appearing in refereed journals

Hassani-Pak, K. and Rawlings, C. J. 2017. Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes. Journal of Integrative Bioinformatics. 14 (1), pp. 1-9.

AuthorsHassani-Pak, K. and Rawlings, C. J.
Abstract

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. 

KeywordsRRES175; 175_Bioinformatics; 175_Genetics
Year of Publication2017
JournalJournal of Integrative Bioinformatics
Journal citation14 (1), pp. 1-9
Digital Object Identifier (DOI)doi:10.1515/jib-2016-0002
Open accessPublished as ‘gold’ (paid) open access
Funder project or codeWheat
Directors Award
Publication dates
Online13 Jun 2017
ISSN16134516
PublisherDe Gruyter

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