Mapinsights: deep exploration of quality issues and error profiles in high-throughput sequence data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-throughput sequencing (HTS) enables rapid genomic variant detection at base-pair resolution.
• Identifying technical artifacts in HTS data is crucial for distinguishing true variants from false positives.

Purpose of the Study:

• To develop Mapinsights, a toolkit for advanced quality control (QC) of sequence alignment files.
• To enhance the detection of sequencing artifacts and outliers in HTS data with greater resolution than existing methods.

Main Methods:

• Mapinsights employs cluster analysis using novel and existing QC features from sequence alignments.
• The toolkit analyzes technical errors related to sequencing cycles, chemistry, libraries, and platforms.
• It also identifies anomalies associated with sequencing depth.

Main Results:

• Mapinsights successfully identified various quality issues in community standard datasets.
• A logistic regression model based on Mapinsights features demonstrated high accuracy in detecting low-confidence variant sites.
• The toolkit provides quantitative estimates and probabilistic arguments for error and bias identification.

Conclusions:

• Mapinsights offers a deeper resolution for detecting sequencing artifacts compared to current methods.
• The toolkit aids in identifying errors, bias, and outlier samples, thereby improving variant call authenticity.
• Mapinsights is a valuable tool for enhancing the reliability of HTS data analysis.