AGeS: a software system for microbial genome sequence annotation

• Published in: PloS one 2011-03, Vol.6 (3), p.e17469-e17469
• Main Authors: Kumar, Kamal, Desai, Valmik, Cheng, Li, Khitrov, Maxim, Grover, Deepak, Satya, Ravi Vijaya, Yu, Chenggang, Zavaljevski, Nela, Reifman, Jaques
• Format: Article
• Language: English
• Subjects: Acids; Age; Annotations; Armed forces; Automation; Bacteria; Base Sequence; Bioinformatics; Biology; Biotechnology; Computer programs; Computer Science; Data processing; Gene sequencing; Genes; Genes, Bacterial - genetics; Genome, Bacterial - genetics; Genomes; Genomics; Integrated software; Linux; Localization; Medical research; Microorganisms; Molecular Sequence Annotation - methods; Next-generation sequencing; Nucleotide sequence; Ontology; Proteins; Reproducibility of Results; Ribonucleic acid; RNA; Software; Software development tools; Software upgrading; Telemedicine; Web applications
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0017469

The annotation of genomes from next-generation sequencing platforms needs to be rapid, high-throughput, and fully integrated and automated. Although a few Web-based annotation services have recently become available, they may not be the best solution for researchers that need to annotate a large number of genomes, possibly including proprietary data, and store them locally for further analysis. To address this need, we developed a standalone software application, the Annotation of microbial Genome Sequences (AGeS) system, which incorporates publicly available and in-house-developed bioinformatics tools and databases, many of which are parallelized for high-throughput performance. The AGeS system supports three main capabilities. The first is the storage of input contig sequences and the resulting annotation data in a central, customized database. The second is the annotation of microbial genomes using an integrated software pipeline, which first analyzes contigs from high-throughput sequencing by locating genomic regions that code for proteins, RNA, and other genomic elements through the Do-It-Yourself Annotation (DIYA) framework. The identified protein-coding regions are then functionally annotated using the in-house-developed Pipeline for Protein Annotation (PIPA). The third capability is the visualization of annotated sequences using GBrowse. To date, we have implemented these capabilities for bacterial genomes. AGeS was evaluated by comparing its genome annotations with those provided by three other methods. Our results indicate that the software tools integrated into AGeS provide annotations that are in general agreement with those provided by the compared methods. This is demonstrated by a >94% overlap in the number of identified genes, a significant number of identical annotated features, and a >90% agreement in enzyme function predictions.