Novel search method for the discovery of functional relationships

Abstract

Motivation: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account.Results: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.Contact: mario.albrecht@mpi-inf.mpg.deSupplementary Information: Supplementary data are available at Bioinformatics online.

Recent approaches to the prioritization of candidate disease genes

“…The first computational approaches in the hunt for disease genes focused on molecular characteristics of disease genes, which discriminate them from non‐disease genes. As described below, researchers developed methods related to individual gene and protein sequence properties75–77 as well as functional annotations of gene products 20–22,26,27,78. In principle, if a candidate satisfies certain characteristics as derived from known disease genes and proteins, its disease relevance is considered to be higher than otherwise.…”

“…Most of them rely on the biological information already available for the disease phenotype of interest and the known, already verified, disease genes as well as for the additional candidate genes. In this context, functional information, particularly, manually curated or automatically derived functional annotation, often provides strong evidence for establishing links between diseases and relevant genes and proteins 20–27. Many prioritization methods use protein interaction data as rich information source for finding relationships between gene products of candidate genes and disease genes 11,16,18,25,28–45.…”

Protein Function Easily Investigated by Genomics Data Mining Using the ProteINSIDE Online Tool

“…Indeed, many tools are dedicated to only one species such as BioMyn for the Human [9] or DroPNet for the Drosophila [7]). Moreover, many tools are dedicated to diseases studies such as NetPath [13] and ToppGene [12].…”

“…Such bioinformatic data processing has to proceed to data gathering and database searching in order to produce a functional interpretation of large datasets. For this purpose, workflows integrating several bioinformatics analyses are now available [5][6][7][8] and were developed to mine dataset from specific species (BioMyn [9] for human, DroPNet [7] for Drosophila, TAIR [10] for Arabidopsis thaliana, EcoCyc [11] for Escherichia coli …) or to identify candidate genes related to diseases as ToppGene [12] or NetPath [13]. The few workflows currently used for the bioinformatics data processing of ruminant datasets are multispecies.…”

NetworkPrioritizer: a versatile tool for network-based prioritization of candidate disease genes or other molecules

“…A network of both protein–protein interactions and functional similarity links was compiled from BioMyn (Ramírez et al , 2012) and FunSimMat (Schlicker et al , 2010), respectively, for proteins encoded by genes in genomic loci associated with Crohn’s disease (Franke et al , 2010). Proteins associated with inflammatory bowel disease (IBD), or Crohn’s disease as a subtype of IBD, were used as seed nodes for the network analysis (see web site).…”