Assessment of quality control approaches for metagenomic data analysis

Abstract

Currently there is an explosive increase of the next-generation sequencing (NGS) projects and related datasets, which have to be processed by Quality Control (QC) procedures before they could be utilized for omics analysis. QC procedure usually includes identification and filtration of sequencing artifacts such as low-quality reads and contaminating reads, which would significantly affect and sometimes mislead downstream analysis. Quality control of NGS data for microbial communities is especially challenging. In this work, we have evaluated and compared the performance and effects of various QC pipelines on different types of metagenomic NGS data and from different angles, based on which general principles of using QC pipelines were proposed. Results based on both simulated and real metagenomic datasets have shown that: firstly, QC-Chain is superior in its ability for contamination identification for metagenomic NGS datasets with different complexities with high sensitivity and specificity. Secondly, the high performance computing engine enabled QC-Chain to achieve a significant reduction in processing time compared to other pipelines based on serial computing. Thirdly, QC-Chain could outperform other tools in benefiting downstream metagenomic data analysis.

YAMP: a containerised workflow enabling reproducibility in metagenomics research

“…Therefore, we generated five metagenomics samples simulating a human oral community with 13 bacterial species, and including a variable amount of human reads as contaminants. The relative proportion of the bacterial genomes followed that suggested by Zhou et al [48] ( Supplementary Table S1). The percentage of contaminant reads was of 1, 5, 25, 50, and 80%, in line with the amounts observed in the literature for human samples [26,49,50].…”

Challenges of the Unknown: Clinical Application of Microbial Metagenomics

“…The pipeline has three common stages, quality control, read trimming, and a host screen. Together these make up the preprocessing stages which increase the power of downstream analyses [43]. This starts by using FastQC [36] to calculate general quality control (QC) metrics, such as per base qualities, followed by Trimmomatic [37], which removes poor quality base calls and potential adapter sequences, and finally host removal by reference-based mapping using the Bowtie2 [44] aligner against a panel of expected host sequences, such as human or porcine.…”

DNA Sequencing as a Tool to Monitor Marine Ecological Status

“…Work initiated in the area of standardizing laboratory procedures includes much-needed development of reference materials and improved DNA extraction methods, as exemplified by establishment of the International Metagenomics and Microbiome Standards Alliance (IMMSA) (NIST, 2016). Controls used to assess artifacts and bias introduced by sample preparation, PCR, and sequencing are reviewed elsewhere (Pinto and Raskin, 2012;Elbrecht and Leese, 2015;Pedersen et al, 2015;Tan et al, 2015;Zhou et al, 2015;Aylagas et al, 2016;Danovaro et al, 2016) and should be used in conjunction with standard quality control pipelines for sequence quality, including chimeric removal (Smyth et al, 2010;Teeling and Glöckner, 2012;Zhou et al, 2014;Escobar-Zepeda et al, 2015;Jeon et al, 2015). Including mock communities in DNA sequencing efforts can assess technical issues such as incomplete DNA extraction or library preparation, PCR, and sequencing errors (Schirmer et al, 2015) and provide correction factors (Tan et al, 2015;Aylagas et al, 2016).…”