HemoMIPs

HemoMIPs analyzes multiplexed Molecular Inversion Probe (MIP) targeted next-generation sequencing (NGS) data to detect genetic variants, assess coverage, and support cohort-level screening such as hemophilia A and B studies.


Key Features:

  • Workflow orchestration: Uses Snakemake to run sample demultiplexing, overlap paired-end merging, alignment, MIP-arm trimming, variant calling, coverage analysis, and report generation.
  • Variant calling flexibility: Supports GATK v4 and GATK v3 for variant discovery.
  • Reporting: Produces an HTML report summarizing covered regions, incomplete or missing areas, called variants, predicted effects, and performance statistics.
  • Structural variant probe analysis: Supports analysis of probes designed to capture specific structural variants.
  • Sex assignment: Assigns sex using Y-chromosome-unique probes for sample interpretation.
  • Imbalanced NGS handling: Processes highly imbalanced targeted NGS datasets typical of MIP experiments.

Scientific Applications:

  • Targeted sequencing analysis: Variant detection and coverage assessment in MIP-based targeted NGS panels.
  • Cohort screening for hemophilia: Screening patient cohorts for hemophilia A and B to identify benign and likely pathogenic variants.
  • Structural variant detection: Detection and interpretation of targeted structural variants captured by specialized probes.
  • Sex assignment in genetic studies: Inferring sample sex using Y-chromosome-unique probes for quality control and interpretation.

Methodology:

Snakemake workflow executing sample demultiplexing, overlap paired-end merging, alignment, MIP-arm trimming, variant calling, coverage analysis, and HTML report generation.

Topics

Details

License:
MIT
Tool Type:
command-line tool
Programming Languages:
Python
Added:
1/18/2021
Last Updated:
1/30/2021

Operations

Publications

Kleinert P, Martin B, Kircher M. HemoMIPs—Automated analysis and result reporting pipeline for targeted sequencing data. PLOS Computational Biology. 2020;16(6):e1007956. doi:10.1371/journal.pcbi.1007956. PMID:32497118. PMCID:PMC7297380.