Abstract
Next-generation sequencing (NGS) technologies have been widely used in life sciences. However, several kinds of sequencing artifacts, including low-quality reads and contaminating reads, were found to be quite common in raw sequencing data, which compromise downstream analysis. Therefore, quality control (QC) is essential for raw NGS data. However, although a few NGS data quality control tools are publicly available, there are two limitations: First, the processing speed could not cope with the rapid increase of large data volume. Second, with respect to removing the contaminating reads, none of them could identify contaminating sources de novo, and they rely heavily on prior information of the contaminating species, which is usually not available in advance. Here we report QC-Chain, a fast, accurate and holistic NGS data quality-control method. The tool synergeticly comprised of user-friendly tools for (1) quality assessment and trimming of raw reads using Parallel-QC, a fast read processing tool; (2) identification, quantification and filtration of unknown contamination to get high-quality clean reads. It was optimized based on parallel computation, so the processing speed is significantly higher than other QC methods. Experiments on simulated and real NGS data have shown that reads with low sequencing quality could be identified and filtered. Possible contaminating sources could be identified and quantified de novo, accurately and quickly. Comparison between raw reads and processed reads also showed that subsequent analyses (genome assembly, gene prediction, gene annotation, etc.) results based on processed reads improved significantly in completeness and accuracy. As regard to processing speed, QC-Chain achieves 7–8 time speed-up based on parallel computation as compared to traditional methods. Therefore, QC-Chain is a fast and useful quality control tool for read quality process and de novo contamination filtration of NGS reads, which could significantly facilitate downstream analysis. QC-Chain is publicly available at: http://www.computationalbioenergy.org/qc-chain.html.
Highlights
Next-generation sequencing (NGS) technologies, which could produce numerous sequences in a single experiment in a relatively short time, have been widely applied in life sciences
When applying quality control (QC)-Chain on the dataset, the results showed a significant improvement in the downstream analysis (Table 3), but when other tools such as FastQC, FASTX-Toolkit, PRINSEQ or NGS QC were applied, since the simulated data were designed to be of high-quality reads, few reads were filtered because of low sequencing-quality and the analysis result is equivalent to that obtained from total reads
Read quality process module (Parallel-QC), together with rRNA identification module and in-house scripts were used in this method to accomplish the comprehensive quality control process
Summary
Next-generation sequencing (NGS) technologies, which could produce numerous sequences (reads) in a single experiment in a relatively short time, have been widely applied in life sciences. Several kinds of sequencing artifacts, which could introduce serious negative impact on downstream analyses, commonly exist in raw reads, regardless of the sequencing platform. These sequence artifacts could be classified into two groups:. For the sequencing quality problem, other than the QC pipeline supplied by the sequencing instrument manufactures, a few online/standalone tools are publicly available, such as PRINSEQ [2], FASTXToolkit (http://hannonlab.cshl.edu/fastx_toolkit/) and NGSQC Toolkit [3] These tools have specific features and were developed based on different concepts and algorithms, yet are not sufficiently optimized on their own
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