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.
|Authors||Hassani-Pak, K. and Rawlings, C. J.|
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.
|Keywords||RRES175; 175_Bioinformatics; 175_Genetics|
|Year of Publication||2017|
|Journal||Journal of Integrative Bioinformatics|
|Journal citation||14 (1), pp. 1-9|
|Digital Object Identifier (DOI)||doi:10.1515/jib-2016-0002|
|Open access||Published as ‘gold’ (paid) open access|
|Funder project or code||Wheat|
|Online||13 Jun 2017|
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