Abstract
Next-generation sequencing is not yet commonly used in clinical laboratories because of a lack of simple and intuitive tools. We developed a software tool (TreeSeq) with a quaternary tree search structure for the analysis of sequence data. This permits rapid searches for sequences of interest in large datasets. We used TreeSeq to screen a gut microbiota metagenomic dataset and a whole genome sequencing (WGS) dataset of a strain of Klebsiella pneumoniae for antibiotic resistance genes and compared the results with BLAST and phenotypic resistance determination. TreeSeq was more than thirty times faster than BLAST and accurately detected resistance gene sequences in complex metagenomic data and resistance genes corresponding with the phenotypic resistance pattern of the Klebsiella strain. Resistance genes found by TreeSeq were visualized as a gene coverage heat map, aiding in the interpretation of results. TreeSeq brings analysis of metagenomic and WGS data within reach of clinical diagnostics.
Highlights
Introduction of new genotypic methods in clinical diagnostics often leads to new diagnostic strategies
For details on BLAST, TreeSeq and Antibiotic Resistance Genes Database (ARDB) we refer to the methods section
BLAST found 492 genes, comprising 64205 (1–1934, Avg. 76) search hits from the ARDB that corresponded to 34 resistance gene classes in the Human Microbiome Project (HMP) metagenomic stool data
Summary
Introduction of new genotypic methods in clinical diagnostics often leads to new diagnostic strategies. One of the most promising techniques at this moment, which is still not widely available in clinical laboratories, is next-generation sequencing (NGS). It has dramatically improved sequencing throughput and has made the field of shotgun metagenomics possible, where the genetic content of whole bacterial communities can be analysed without prior culture [1,2]. Unlike whole-genome shotgun sequencing (WGSS) of individual organisms, in which the end product is typically an assembled genome, metagenomics handles multiple genomes This is why metagenomic data files are often large and highly complex; analysis frequently requires high-end computers, which are not part of the routine equipment of clinical laboratories. To introduce NGS to these laboratories, we need a rapid and user-friendly tool
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