Mapinsights

Mapinsights analyzes sequence alignment data from high-throughput sequencing (HTS) to detect technical artifacts, characterize non-random error patterns, and flag low-confidence variant sites to improve variant call reliability.

Key Features:

• Quality Control Analysis: Performs in-depth QC on sequence alignment files using novel and established QC features derived from alignments to detect outlier behavior arising from sequencing artifacts.
• Outlier Detection: Identifies technical errors associated with sequencing cycles, chemistry, library preparation, sequencing platforms, and sequencing depth.
• High-Resolution Analysis: Offers deeper resolution in detecting sequencing artifacts compared to existing methods to better discriminate true variants from false positives.
• Logistic Regression Model: Applies a logistic regression-based model to combine features and probabilistically classify 'low-confidence' variant sites.
• Quantitative Estimates and Probabilistic Arguments: Provides quantitative estimates and probabilistic assessments to recognize errors, biases, and outlier samples within sequencing datasets.

Scientific Applications:

• Genomic Variant Detection: Improves confidence in variant calls by distinguishing genuine variants from sequencing-induced artifacts.
• Cross-Platform Comparisons: Facilitates comparison of sequencing platforms by identifying platform-specific biases and errors.
• Library Preparation Analysis: Assesses the impact of library preparation on data quality and variant detection accuracy.

Methodology:

Integrates cluster analysis with a logistic regression model using novel and established QC features extracted directly from sequence alignments.