Dataset Information

FIDBAC: A Platform for Fast Bacterial Genome Identification and Typing.

ABSTRACT: To study the contamination of microorganisms in the food industry, pharmaceutical industry, clinical diagnosis, or bacterial taxonomy, accurate identification of species is a key starting point of further investigation. The conventional method of identification by the 16S rDNA gene or other marker gene comparison is not accurate, because it uses a tiny part of the genomic information. The average nucleotide identity calculated between two whole bacterial genomes was proven to be consistent with DNA-DNA hybridization and adopted as the gold standard of bacterial species delineation. Furthermore, there are more bacterial genomes available in public databases recently. All of those contribute to a genome era of bacterial species identification. However, wrongly labeled and low-quality bacterial genome assemblies, especially from type strains, greatly affect accurate identification. In this study, we employed a multi-step strategy to create a type-strain genome database, by removing the wrongly labeled and low-quality genome assemblies. Based on the curated database, a fast bacterial genome identification platform (fIDBAC) was developed (http://fbac.dmicrobe.cn/). The fIDBAC is aimed to provide a single, coherent, and automated workflow for species identification, strain typing, and downstream analysis, such as CDS prediction, drug resistance genes, virulence gene annotation, and phylogenetic analysis.

SUBMITTER: Liang Q

PROVIDER: S-EPMC8558511 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

fIDBAC: A Platform for Fast Bacterial Genome Identification and Typing.

Liang Qian Q Liu Chengzhi C Xu Rong R Song Minghui M Zhou Zhihui Z Li Hong H Dai Weiyou W Yang Meicheng M Yu Yunsong Y Chen Huan H

Frontiers in microbiology 20211018

To study the contamination of microorganisms in the food industry, pharmaceutical industry, clinical diagnosis, or bacterial taxonomy, accurate identification of species is a key starting point of further investigation. The conventional method of identification by the 16S rDNA gene or other marker gene comparison is not accurate, because it uses a tiny part of the genomic information. The average nucleotide identity calculated between two whole bacterial genomes was proven to be consistent with ...[more]

PMID: 34733246

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Project description:BackgroundBacteria carry a wide array of genes, some of which have multiple alleles. These different alleles are often responsible for distinct types of virulence and can determine the classification at the subspecies levels (e.g., housekeeping genes for Multi Locus Sequence Typing, MLST). Therefore, it is important to rapidly detect not only the gene of interest, but also the relevant allele. Current sequencing-based methods are limited to mapping reads to each of the known allele reference, which is a time-consuming procedure.ResultsTo address this limitation, we developed BacTag - a pipeline that rapidly and accurately detects which genes are present in a sequencing dataset and reports the allele of each of the identified genes. We exploit the fact that different alleles of the same gene have a high similarity. Instead of mapping the reads to each of the allele reference sequences, we preprocess the database prior to the analysis, which makes the subsequent gene and allele identification efficient. During the preprocessing, we determine a representative reference sequence for each gene and store the differences between all alleles and this chosen reference. Throughout the analysis we estimate whether the gene is present in the sequencing data by mapping the reads to this reference sequence; if the gene is found, we compare the variants to those in the preprocessed database. This allows to detect which specific allele is present in the sequencing data. Our pipeline was successfully tested on artificial WGS E. coli, S. pseudintermedius, P. gingivalis, M. bovis, Borrelia spp. and Streptomyces spp. data and real WGS E. coli and K. pneumoniae data in order to report alleles of MLST house-keeping genes.ConclusionsWe developed a new pipeline for fast and accurate gene and allele recognition based on database preprocessing and parallel computing and performed better or comparable to the current popular tools. We believe that our approach can be useful for a wide range of projects, including bacterial subspecies classification, clinical diagnostics of bacterial infections, and epidemiological studies.

Dataset Information

FIDBAC: A Platform for Fast Bacterial Genome Identification and Typing.

Publications

fIDBAC: A Platform for Fast Bacterial Genome Identification and Typing.

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