Reduce manual curation by combining gene predictions from multiple annotation engines, a case study of start codon prediction.

ABSTRACT: Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF) calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35-52%. We present a case study which demonstrates a novel way o

SUBMITTER: Ederveen TH 

PROVIDER: S-EPMC3651085 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature