ADEPT, a dynamic next generation sequencing data error-detection program with trimming

Journal Article · Mon Feb 29 00:00:00 EST 2016 · BMC Bioinformatics

DOI:https://doi.org/10.1186/s12859-016-0967-z· OSTI ID:1248578

Feng, Shihai ^[1]; Lo, Chien-Chi ^[1]; Li, Po-E ^[1]; Chain, Patrick S. G. ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Illumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery. In this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the true positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads. We conclude that ADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-05CH11231; AC52-06NA25396; CB10152; Y1-DE-6006-02; HSHQDC08X00790; B104153I; B084531I
• OSTI ID: 1248578
• Report Number(s): LA-UR-14-25592; PII: 967
• Journal Information: BMC Bioinformatics, Vol. 17, Issue 1; ISSN 1471-2105
• Publisher: BioMed Central
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES
Next generation sequencing
Illumina error prediction
Local quality scores
Position-specific quality