Graph-based sequence annotation using a data integration approach

Abstract

SummaryThe automated annotation of data from high throughput sequencing and genomics experiments is a significant challenge for bioinformatics. Most current approaches rely on sequential pipelines of gene finding and gene function prediction methods that annotate a gene with information from different reference data sources. Each function prediction method contributes evidence supporting a functional assignment. Such approaches generally ignore the links between the information in the reference datasets. These links, however, are valuable for assessing the plausibility of a function assignment and can be used to evaluate the confidence in a prediction. We are working towards a novel annotation system that uses the network of information supporting the function assignment to enrich the annotation process for use by expert curators and predicting the function of previously unannotated genes. In this paper we describe our success in the first stages of this development. We present the data integration steps that are needed to create the core database of integrated reference databases (UniProt, PFAM, PDB, GO and the pathway database AraCyc) which has been established in the ONDEX data integration system. We also present a comparison between different methods for integration of GO terms as part of the function assignment pipeline and discuss the consequences of this analysis for improving the accuracy of gene function annotation. The methods and algorithms presented in this publication are an integral part of the ON-DEX system which is freely available from http://ondex.sf.net/.

Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks

“…The integration of biological databases is currently one of the main challenges in the data mining community, and many studies have focused on enrichment analyses for driving the integration of multipurpose ‘omics’ or annotated data. Although such database integration enables the user to perform more realistic analysis, few tools integrate visualization approaches [ 74 ], and if so, they are dedicated to specific biological networks [ 75 , 76 ].…”

Genome-wide detection of sRNA targets with rNAV

“…Integration of biological databases is currently one the main challenges in the data mining community, and many works have focused on enrichment analyses for driving the integration of multipurpose omics or [15]). However few of them integrate visualization approaches (for examples see [29]), and if so, they are dedicated to specific biological networks with a single database (e.g. [3,14,24,7,22]).…”