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Updated: Dec 11, 2025

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Detection of simple and complex de novo mutations with multiple reference sequences.

Kiran V Garimella1,2,3, Zamin Iqbal2,4, Michael A Krause2,5,6

  • 1Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.

Genome Research
|August 21, 2020
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Summary

Identifying de novo mutations in complex genomic regions is difficult. Corticall, a novel graph-based method, integrates diverse data to accurately detect genetic variants, improving mutation discovery.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • Characterizing de novo mutations in diverse genomic regions is challenging due to assembly and mapping limitations with short-read sequencing.
  • Complex structural variants often occur in repetitive or low-complexity regions, hindering accurate genetic variant detection.
  • Long-read sequencing offers potential solutions but is not yet scalable for comprehensive analysis.

Purpose of the Study:

  • To present Corticall, a novel graph-based computational method for detecting arbitrary genetic variants.
  • To overcome limitations of existing methods in analyzing complex genomic regions.
  • To characterize the rate and spectrum of de novo mutations in *Plasmodium falciparum*.

Main Methods:

  • Constructing multisample, colored de Bruijn graphs from short-read data.
  • Integrating long-read haplotypes and multiple reference data sources to enhance graph connectivity.
  • Employing graph path-finding algorithms and a simultaneous alignment/recombination model for variant calling.

Main Results:

  • Corticall successfully detects arbitrary classes of genetic variants, including complex structural variants.
  • The method was validated through extensive simulations.
  • Applied to *Plasmodium falciparum*, Corticall characterized de novo mutation rates and spectra, identifying known and novel nonallelic homologous recombination events.

Conclusions:

  • Corticall offers a robust solution for variant detection in challenging genomic regions by integrating multiple data types.
  • The method enhances the characterization of de novo mutations and structural variations.
  • This approach has significant implications for population genetics and understanding genome evolution.