Using QC-Blind for Quality Control and Contamination Screening of Bacteria DNA Sequencing Data Without Reference Genome

Abstract

Quality control for next generation sequencing (NGS) has become increasingly important with the ever increasing importance of sequencing data for omics studies. Tools have been developed for filtering possible contaminants from species with known reference genome. Unfortunately, reference genomes for all the species involved, including the contaminants, are required for these tools to work. This precludes many real-life samples that have no information about the complete genome of the target species, and are contaminated with unknown microbial species. In this work we proposed QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. The pipeline merely requires the information about a few marker genes of the target species. The entire pipeline consists of unsupervised read assembly, contig binning, read clustering, and marker gene assignment. When evaluated on in silico, ab initio and in vivo datasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind could serve well in situations where limited information is available for both target and contamination species.

Genomic Characterization of Salmonella enterica serovar Weltevreden Associated with Human Diarrhea

“…Weltevreden strains. Potential interspecies and cross-species contamination in the whole genome sequence data was checked with ConFindr ( 44 ) and QC-Blind ( 45 ). Raw reads with low quality base pairs at each terminal (Quality-Value < 20), and/or those with a short length (parameter setting at 50 bp), or > 15 bp overlap with Illumina TruSeq adapter sequences (parameter setting at 15 bp) were removed.…”

Microbial Dark Matter: From Discovery to Applications

“…Genomic and metagenomic sequencing data commonly contain possible contamination from various environments, yet identification and removal of these contaminants remain difficult [126] , [127] , [128] . Machine learning-enabled source tracking and sequence clustering methods can act together to identify and remove contaminants, regardless of known or unknown sources [128] . Indeed, the application of machine learning methods to sequencing data can lead to the removal of most known contaminants [129] .…”

Omics sciences potential on bioprospecting of biological control microbial agents: the case of the Mexican agro-biotechnology

“…At present, the robust taxonomic affiliation of microorganisms is based on the extraction, sequencing, and analysis of their genome, partial or total, supported by morphological, physiological, and metabolic traits (Figure 2). For this, sequencing high-quality DNA of the microorganism of interest and validating the obtained data through a quality control process are decisive factors, because reads of DNA without the required quality can be excluded in subsequent analysis (Xi et al, 2019). FastQC is a digital tool designed to perform numerous quality control checks of the sequences that are obtained (Andrews, 2010).…”

“…En la actualidad, la afiliación taxonómica robusta de microorganismos se basa en la extracción, secuenciación y análisis de su genoma, parcial o total, apoyado de rasgos morfológicos, fisiológicos y metabólicos (Figura 2). Para esto, es determinante la secuenciación del ADN de alta calidad del microorganismo de interés y validar los datos obtenidos por un proceso de control de calidad, lo cual permite la exclusión de lecturas de ADN sin la calidad necesaria para análisis posteriores (Xi et al, 2019). FastQC es una herramienta digital que permite realizar numerosas comprobaciones de control de calidad de las secuencias obtenidas (Andrews, 2010).…”