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

Abstract

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.

Advances in Integrating Genomics and Bioinformatics in the Plant Breeding Pipeline

“…The task of creating an integrative crop database by combining annotated genome sequences, gene functions, interaction networks and trait phenotypes is challenging as the relevant data are dispersed in numerous databases with various data formats and different quality and coverage. Intelligent mining of large-scale crop databases is required to merge complex data resources and allow gene discovery and crop improvement [56]. To integrate biological data from different resources, a web-based intelligent mining tool, KnetMiner (Knowledge Network Miner) has been developed to search for links and concepts in biological knowledge networks, which enables the discovery of novel connections between traits and genes [56].…”

“…Intelligent mining of large-scale crop databases is required to merge complex data resources and allow gene discovery and crop improvement [56]. To integrate biological data from different resources, a web-based intelligent mining tool, KnetMiner (Knowledge Network Miner) has been developed to search for links and concepts in biological knowledge networks, which enables the discovery of novel connections between traits and genes [56]. There are four main steps in the KnetMiner approach: (1) integrating diverse biological data into a knowledge graph, (2) improving the knowledge graph with text-mining of the literature, (3) identifying the link between genes and evidence nodes, (4) applying the evidence-based gene ranking algorithm and visualising the integrated data.…”

Computational miRNomics – Integrative Approaches

“…Such methods represent basic tools for computational miRNomics today and in the future [4]. Their discourse does not include miRNAs, but Suluyayla et al [5] describe a new database containing miRNAs and their target interactions which can be queried using VANESA [6] to enrich gene regulatory pathways with miRNA interactions.…”

Bottlenecks for genome-edited crops on the road from lab to farm

“…Closing the gaps between phenotypic and genotypic data and among the data for different crops and crop relatives will provide important information that will facilitate the widespread implementation of crop genome editing. Network analyses could then help to interpret this deluge of data to find agronomically relevant target genes [ 11 ].…”