ngsComposer: an automated pipeline for empirically based NGS data quality filtering

Area of Science:

• Genomics
• Bioinformatics

Background:

• Next-generation sequencing (NGS) generates large-scale omics data but lacks objective quality filtering parameters.
• Platform-generated Phred values often overestimate per-base quality scores, necessitating improved filtering methods.

Purpose of the Study:

• To develop and validate novel algorithms for objective and empirically based NGS data quality filtering.
• To address limitations in current quality assessment metrics and improve the reliability of omics data.

Main Methods:

• Developed empirically based algorithms leveraging known sequence motifs for error rate estimation.
• Implemented motif-based detection of erroneous base calls and removal of adapter sequences.
• Validated performance using read compression rates and assessed mitigation of barcode swapping.

Main Results:

• Novel algorithms provide empirical estimation of error rates, surpassing Phred value accuracy.
• Effective detection and removal of erroneous base calls and adapter sequences were achieved.
• Mitigation of barcode swapping in pooled libraries and validation of universal applicability across NGS platforms.

Conclusions:

• The ngsComposer pipeline offers a robust solution for NGS data quality filtering using universal algorithmic concepts.
• Empirically derived quality filtering enhances the reliability of large-scale omics data for diverse applications.
• The developed methods improve data integrity and address inherent challenges in pooled library sequencing.