Nucleic Acids Research · 2018 · 1.9K citations · 21 references
Life SciencesBioinformaticsMicrobiologyComparative GenomicsGenomicsMedical MicrobiologyBacterial Virulence FactorsComparative Microbial GenomicsMicrobial Virulence FactorsPathogen GenomicsPathogen Virulence FactorsSequence Similarity SearchGenomic Characterization Of PathogensPathogen Genome AnnotationPathogen Genomics DatabasesPathogenesis Of Bacterial InfectionsSequence SimilarityVirulence Factor AnnotationVirulence Factor IdentificationVirulence Gene Identification
VFDB offers a comprehensive online resource for deciphering bacterial pathogenesis, yet efficiently characterizing pathogenomic compositions remains challenging for users lacking bioinformatics expertise. The authors introduced VFanalyzer, an integrated automatic pipeline to systematically identify known and potential virulence factors in bacterial genomes. VFanalyzer constructs orthologous groups, performs exhaustive similarity searches, refines results contextually, and presents reports via an optimized interactive web interface, achieving high specificity and sensitivity without manual curation.
The virulence factor database (VFDB, http://www.mgc.ac.cn/VFs/) is devoted to providing the scientific community with a comprehensive warehouse and online platform for deciphering bacterial pathogenesis. The various combinations, organizations and expressions of virulence factors (VFs) are responsible for the diverse clinical symptoms of pathogen infections. Currently, whole-genome sequencing is widely used to decode potential novel or variant pathogens both in emergent outbreaks and in routine clinical practice. However, the efficient characterization of pathogenomic compositions remains a challenge for microbiologists or physicians with limited bioinformatics skills. Therefore, we introduced to VFDB an integrated and automatic pipeline, VFanalyzer, to systematically identify known/potential VFs in complete/draft bacterial genomes. VFanalyzer first constructs orthologous groups within the query genome and preanalyzed reference genomes from VFDB to avoid potential false positives due to paralogs. Then, it conducts iterative and exhaustive sequence similarity searches among the hierarchical prebuilt datasets of VFDB to accurately identify potential untypical/strain-specific VFs. Finally, via a context-based data refinement process for VFs encoded by gene clusters, VFanalyzer can achieve relatively high specificity and sensitivity without manual curation. In addition, a thoroughly optimized interactive web interface is introduced to present VFanalyzer reports in comparative pathogenomic style for easy online analysis.
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