NgsComposer: an automated pipeline for empirically based NGS data quality filtering.

ABSTRACT: Next-generation sequencing (NGS) enables massively parallel acquisition of large-scale omics data; however, objective data quality filtering parameters are lacking. Although a useful metric, evidence reveals that platform-generated Phred values overestimate per-base quality scores. We have developed novel and empirically based algorithms that streamline NGS data quality filtering. The pipeline leverages known sequence motifs to enable empirical estimation of error rates, detection of erroneous base calls and removal of contaminating adapter sequence. The performance of motif-based error detection and quality filtering were further validated with read compression rates as an unbiased metric. Elevated error rates at read ends, where known motifs lie, tracked with propagation of erroneous bas

SUBMITTER: Kuster RD 

PROVIDER: S-EPMC8425578 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature