Contamination detection in sequencing studies using the mitochondrial phylogeny.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Within-species contamination poses a significant challenge in genetic sequencing, particularly impacting mitochondrial DNA (mtDNA) studies.
• Current methods for detecting contamination often rely on nuclear genome analysis, which is computationally intensive and unsuitable for large-scale mtDNA datasets.

Purpose of the Study:

• To introduce haplocheck, a novel computational tool designed for detecting sample contamination using only mtDNA.
• To evaluate haplocheck's accuracy and efficiency in identifying contamination in both targeted mtDNA and whole-genome sequencing data.

Main Methods:

• Development of haplocheck, a tool requiring only mtDNA for contamination detection.
• In silico simulations and experimental validation using amplicon mixtures.
• Application of haplocheck to The 1000 Genomes Project Consortium data.

Main Results:

• Haplocheck accurately detects mtDNA contamination, independent of phylogenetic distance within mixtures.
• The tool functions effectively for both targeted mtDNA and whole-genome sequencing studies.
• Analysis of The 1000 Genomes data demonstrated haplocheck's utility as a rapid proxy for nuclear DNA-based contamination detection, highlighting mitochondrial copy number as a key factor.

Conclusions:

• Haplocheck provides an accurate and efficient method for detecting mtDNA contamination using only the mitochondrial genome.
• The tool is valuable for large-scale sequencing projects and can serve as a fast proxy for nuclear DNA-based contamination assessment.
• Haplocheck is accessible as a command-line tool and a cloud web service with interactive reporting features.