MOTIVATION: All current mitochondrial haplogroup classification tools require variants to be detected from an alignment with the reference sequence and to be properly named according to the canonical nomenclature standards for describing mitochondrial variants, before they can be compared with the haplogroup determining polymorphisms. With the emergence of high-throughput sequencing technologies and hence greater availability of mitochondrial genome sequences, there is a strong need for an automated haplogroup classification tool that is alignment-free and agnostic to reference sequence. RESULTS: We have developed a novel mitochondrial genome haplogroup-defining algorithm using a k-mer approach namely Phy-Mer. Phy-Mer performs equally well as the leading haplogroup classifier, HaploGrep, while avoiding the errors that may occur when preparing variants to required formats and notations. We have further expanded Phy-Mer functionality such that next-generation sequencing data can be used directly as input. AVAILABILITY AND IMPLEMENTATION: Phy-Mer is publicly available under the GNU Affero General Public License v3.0 on GitHub (https://github.com/danielnavarrogomez/phy-mer). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Mitochondrial genome sequence analysis is critical to the diagnostic evaluation of mitochondrial disease. Existing methodologies differ widely in throughput, complexity, cost efficiency, and sensitivity of heteroplasmy detection. Affymetrix MitoChip v2.0, which uses a sequencing-by-genotyping technology, allows potentially accurate and high-throughput sequencing of the entire human mitochondrial genome to be completed in a cost-effective fashion. However, the relatively low call rate achieved using existing software tools has limited the wide adoption of this platform for either clinical or research applications. Here, we report the design and development of a custom bioinformatics software pipeline that achieves a much improved call rate and accuracy for the Affymetrix MitoChip v2.0 platform. We used this custom pipeline to analyze MitoChip v2.0 data from 24 DNA samples representing a broad range of tissue types (18 whole blood, 3 skeletal muscle, 3 cell lines), mutations (a 5.8 kilobase pair deletion and 6 known heteroplasmic mutations), and haplogroup origins. All results were compared to those obtained by at least one other mitochondrial DNA sequence analysis method, including Sanger sequencing, denaturing HPLC-based heteroduplex analysis, and/or the Illumina Genome Analyzer II next generation sequencing platform. RESULTS: An average call rate of 99.75% was achieved across all samples with our custom pipeline. Comparison of calls for 15 samples characterized previously by Sanger sequencing revealed a total of 29 discordant calls, which translates to an estimated 0.012% for the base call error rate. We successfully identified 4 known heteroplasmic mutations and 24 other potential heteroplasmic mutations across 20 samples that passed quality control. CONCLUSIONS: Affymetrix MitoChip v2.0 analysis using our optimized MitoChip Filtering Protocol (MFP) bioinformatics pipeline now offers the high sensitivity and accuracy needed for reliable, high-throughput and cost-efficient whole mitochondrial genome sequencing. This approach provides a viable alternative of potential utility for both clinical diagnostic and research applications to traditional Sanger and other emerging sequencing technologies for whole mitochondrial genome analysis.