AGeNNT: annotation of enzyme families by means of refined neighborhood networks

BMC Bioinformatics

Methodology

Rainer Merkl¹  Maximilian G. Plach¹  Florian Kandlinger² 

[1] Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040, Regensburg, Germany;Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040, Regensburg, Germany;Faculty of Mathematics and Computer Science, University of Hagen, D-58084, Hagen, Germany;

关键词: Sequence similarity network;    SSN;    Genome neighborhood network;    GNN;    Genome content;    Enzyme function;    Homology-free annotation;   

DOI  :  10.1186/s12859-017-1689-6

 received in 2016-12-13, accepted in 2017-05-16,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundLarge enzyme families may contain functionally diverse members that give rise to clusters in a sequence similarity network (SSN). In prokaryotes, the genome neighborhood of a gene-product is indicative of its function and thus, a genome neighborhood network (GNN) deduced for an SSN provides strong clues to the specific function of enzymes constituting the different clusters. The Enzyme Function Initiative (http://enzymefunction.org/) offers services that compute SSNs and GNNs.ResultsWe have implemented AGeNNT that utilizes these services, albeit with datasets purged with respect to unspecific protein functions and overrepresented species. AGeNNT generates refined GNNs (rGNNs) that consist of cluster-nodes representing the sequences under study and Pfam-nodes representing enzyme functions encoded in the respective neighborhoods. For cluster-nodes, AGeNNT summarizes the phylogenetic relationships of the contributing species and a statistic indicates how unique nodes and GNs are within this rGNN. Pfam-nodes are annotated with additional features like GO terms describing protein function. For edges, the coverage is given, which is the relative number of neighborhoods containing the considered enzyme function (Pfam-node). AGeNNT is available at https://github.com/kandlinf/agennt.ConclusionsAn rGNN is easier to interpret than a conventional GNN, which commonly contains proteins without enzymatic function and overly specific neighborhoods due to phylogenetic bias. The implemented filter routines and the statistic allow the user to identify those neighborhoods that are most indicative of a specific metabolic capacity. Thus, AGeNNT facilitates to distinguish and annotate functionally different members of enzyme families.

【 授权许可】

CC BY   
© The Author(s). 2017