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

Abstract

BackgroundIllumina 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.ResultsIn 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.ConclusionsADEPT 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0967-z) contains supplementary material, which is available to authorized users.

A random forest classifier for detecting rare variants in NGS data from viral populations

“…With the availability of large amounts of data from NGS technologies, data driven classifiers have also been used for detection of sequencing errors [21] and for variant calling [5] , [12] , [22] , [23] . However, identifying the features for classification of rare variants and sequencing errors is still a challenge, due to their similar characteristics in the NGS data.…”