Prevention, diagnosis and treatment of high‐throughput sequencing data pathologies

Abstract

High-throughput sequencing (HTS) technologies generate millions of sequence reads from DNA/RNA molecules rapidly and cost-effectively, enabling single investigator laboratories to address a variety of 'omics' questions in nonmodel organisms, fundamentally changing the way genomic approaches are used to advance biological research. One major challenge posed by HTS is the complexity and difficulty of data quality control (QC). While QC issues associated with sample isolation, library preparation and sequencing are well known and protocols for their handling are widely available, the QC of the actual sequence reads generated by HTS is often overlooked. HTS-generated sequence reads can contain various errors, biases and artefacts whose identification and amelioration can greatly impact subsequent data analysis. However, a systematic survey on QC procedures for HTS data is still lacking. In this review, we begin by presenting standard 'health check-up' QC procedures recommended for HTS data sets and establishing what 'healthy' HTS data look like. We next proceed by classifying errors, biases and artefacts present in HTS data into three major types of 'pathologies', discussing their causes and symptoms and illustrating with examples their diagnosis and impact on downstream analyses. We conclude this review by offering examples of successful 'treatment' protocols and recommendations on standard practices and treatment options. Notwithstanding the speed with which HTS technologies -and consequently their pathologies -change, we argue that careful QC of HTS data is an important -yet often neglected -aspect of their application in molecular ecology, and lay the groundwork for developing a HTS data QC 'best practices' guide.

In Silico Whole Genome Sequencer and Analyzer (iWGS): a Computational Pipeline to Guide the Design and Analysis of de novo Genome Sequencing Studies

“…HTS data generated by all technologies contain errors and artifacts, which may sometimes substantially compromise the quality of the assembly (Zhou and Rokas 2014). Therefore, iWGS includes an optional step to perform preprocessing of the data, including trimming of low-quality bases, removal of adapter contaminations, and correction of sequencing errors.…”

SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data

“…However, the presence of low-quality bases, sequence artifacts, and sequence contamination can introduce serious negative impact on downstream analyses. Thus, QC and preprocessing of raw data serve as the critical steps to initiate analysis pipelines [4, 5]. QC investigates several statistics of datasets to ensure data quality, and preprocessing trims off undesirable terminal fragments and filters out substandard reads [6].…”

Removing duplicate reads using graphics processing units

“…However, this situation occurs typically during the analysis of single-end reads. In fact, as for paired-end reads, the probability of finding independent molecules identical at both ends being very low [5]. …”