Benchmarking the Effectiveness and Accuracy of Multiple Mitochondrial DNA Variant Callers: Practical Implications for Clinical Application

mtDNA-Server 2: advancing mitochondrial DNA analysis through highly parallelized data processing and interactive analytics

Over the past decade, mtDNA-Server established itself as one of the most widely used variant calling web-services for human mitochondrial genomes. The service accepts sequencing data in BAM format and returns an annotated variant analysis report for both homoplasmic and heteroplasmic variants. In this work we present mtDNA-Server 2, which includes several new features highly requested by the community. Most importantly, it includes (a) the integration of a novel variant calling mode that accurately call insertions, deletions and single nucleotide variants at once, (b) the integration of additional quality control and input validation modules, (c) a method to estimate the required coverage to minimize false positives and (d) an interactive analytics dashboard. Furthermore, we migrated the complete analysis workflow to the Nextflow workflow manager for improved parallelization, reproducibility and local execution. Recognizing the importance of insertions and deletions as well as offering novel quality control, validation and reporting features, mtDNA-Server 2 provides researchers and clinicians a new state-of-the-art analysis platform for interpreting mitochondrial genomes. mtDNA-Server 2 is available via mitoverse, our analysis platform that offers a centralized place for mtDNA analysis in the cloud. The web-service, source code and its documentation are freely accessible at https://mitoverse.i-med.ac.at.

mtDNA analysis using Mitopore

Mitochondrial DNA (mtDNA) analysis is crucial for the diagnosis of mitochondrial disorders, forensic investigations, and basic research. Existing pipelines are complex, expensive, and require specialized personnel. In many cases, including the diagnosis of detrimental single nucleotide variants (SNVs), mtDNA analysis is still carried out using Sanger sequencing. Here, we developed a simple workflow and a publicly available webserver named Mitopore that allows the detection of mtDNA SNVs, indels, and haplogroups. To simplify mtDNA analysis, we tailored our workflow to process noisy long-read sequencing data for mtDNA analysis, focusing on sequence alignment and parameter optimization. We implemented Mitopore with eliBQ (eliminate bad quality reads), an innovative quality enhancement that permits the increase of per-base quality of over 20% for low-quality data. The whole Mitopore workflow and webserver were validated using patient-derived and induced pluripotent stem cells harboring mtDNA mutations. Mitopore streamlines mtDNA analysis as an easy-to-use fast, reliable, and cost-effective analysis method for both long- and short-read sequencing data. This significantly enhances the accessibility of mtDNA analysis and reduces the cost per sample, contributing to the progress of mtDNA-related research and diagnosis.

A clinical approach to diagnosis and management of mitochondrial myopathies

This paper provides an overview of the different types of mitochondrial myopathies (MM), associated phenotypes, genotypes as well as a practical clinical approach towards disease diagnosis, surveillance, and management. nDNA-related MM are more common in pediatric-onset disease whilst mtDNA-related MMs are more frequent in adults. Genotype-phenotype correlation in MM is challenging due to clinical and genetic heterogeneity. The multisystemic nature of many MMs adds to the diagnostic challenge. Diagnostic approaches utilizing genetic sequencing with next generation sequencing approaches such as gene panel, exome and genome sequencing are available. This aids molecular diagnosis, heteroplasmy detection in MM patients and furthers knowledge of known mitochondrial genes. Precise disease diagnosis can end the diagnostic odyssey for patients, avoid unnecessary testing, provide prognosis, facilitate anticipatory management, and enable access to available therapies or clinical trials. Adjunctive tests such as functional and exercise testing could aid surveillance of MM patients. Management requires a multi-disciplinary approach, systemic screening for comorbidities, cofactor supplementation, avoidance of substances that inhibit the respiratory chain and exercise training. This update of the current understanding on MMs provides practical perspectives on current diagnostic and management approaches for this complex group of disorders.

A systematic comparison of human mitochondrial genome assembly tools

BACKGROUND

Mitochondria are the cell organelles that produce most of the chemical energy required to power the cell's biochemical reactions. Despite being a part of a eukaryotic host cell, the mitochondria contain a separate genome whose origin is linked with the endosymbiosis of a prokaryotic cell by the host cell and encode independent genomic information throughout their genomes. Mitochondrial genomes accommodate essential genes and are regularly utilized in biotechnology and phylogenetics. Various assemblers capable of generating complete mitochondrial genomes are being continuously developed. These tools often use whole-genome sequencing data as an input containing reads from the mitochondrial genome. Till now, no published work has explored the systematic comparison of all the available tools for assembling human mitochondrial genomes using short-read sequencing data. This evaluation is required to identify the best tool that can be well-optimized for small-scale projects or even national-level research.

RESULTS

In this study, we have tested the mitochondrial genome assemblers for both simulated datasets and whole genome sequencing (WGS) datasets of humans. For the highest computational setting of 16 computational threads with the simulated dataset having 1000X read depth, MitoFlex took the least execution time of 69 s, and IOGA took the longest execution time of 1278 s. NOVOPlasty utilized the least computational memory of approximately 0.098 GB for the same setting, whereas IOGA utilized the highest computational memory of 11.858 GB. In the case of WGS datasets for humans, GetOrganelle and MitoFlex performed the best in capturing the SNPs information with a mean F1-score of 0.919 at the sequencing depth of 10X. MToolBox and NOVOPlasty performed consistently across all sequencing depths with a mean F1 score of 0.897 and 0.890, respectively.

CONCLUSIONS

Based on the overall performance metrics and consistency in assembly quality for all sequencing data, MToolBox performed the best. However, NOVOPlasty was the second fastest tool in execution time despite being single-threaded, and it utilized the least computational resources among all the assemblers when tested on simulated datasets. Therefore, NOVOPlasty may be more practical when there is a significant sample size and a lack of computational resources. Besides, as long-read sequencing gains popularity, mitochondrial genome assemblers must be developed to use long-read sequencing data.

Next-generation sequencing reveals mitogenome diversity in plasma extracellular vesicles from colorectal cancer patients

BACKGROUND

Recent reports have demonstrated that the entire mitochondrial genome can be secreted in extracellular vesicles (EVs), but the biological attributes of this cell-free mitochondrial DNA (mtDNA) remain insufficiently understood. We used next-generation sequencing to compare plasma EV-derived mtDNA to that of whole blood (WB), peripheral blood mononuclear cells (PBMCs), and formalin-fixed paraffin-embedded (FFPE) tumor tissue from eight rectal cancer patients and WB and fresh-frozen (FF) tumor tissue from eight colon cancer patients.

METHODS

Total DNA was isolated before the mtDNA was enriched by PCR with either two primer sets generating two long products or multiple primer sets (for the FFPE tumors), prior to the sequencing. mtDNA diversity was assessed as the total variant number, level of heteroplasmy (mutant mtDNA copies mixed with wild-type copies), variant distribution within the protein-coding genes, and the predicted functional effect of the variants in the different sample types. Differences between groups were compared by paired Student's t-test or ANOVA with Dunnett's multiple comparison tests when comparing matched samples from patients. Mann-Whitney U test was used when comparing differences between the cancer types and patient groups. Pearson correlation analysis was performed.

RESULTS

In both cancer types, EV mtDNA presented twice as many variants and had significantly more low-level heteroplasmy than WB mtDNA. The EV mtDNA variants were clustered in the coding regions, and the proportion of EV mtDNA variants that were missense mutations (i.e., estimated to moderately affect the mitochondrial protein function) was significantly higher than in WB and tumor tissues. Nonsense mutations (i.e., estimated to highly affect the mitochondrial protein function) were only observed in the tumor tissues and EVs.

CONCLUSION

Taken together, plasma EV mtDNA in CRC patients exhibits a high degree of diversity.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT01816607 . Registered 22 March 2013.

Laboratory testing for mitochondrial diseases: biomarkers for diagnosis and follow-up

The currently available biomarkers generally lack the specificity and sensitivity needed for the diagnosis and follow-up of patients with mitochondrial diseases (MDs). In this group of rare genetic disorders (mutations in approximately 350 genes associated with MDs), all clinical presentations, ages of disease onset and inheritance types are possible. Blood, urine, and cerebrospinal fluid surrogates are well-established biomarkers that are used in clinical practice to assess MD. One of the main challenges is validating specific and sensitive biomarkers for the diagnosis of disease and prediction of disease progression. Profiling of lactate, amino acids, organic acids, and acylcarnitine species is routinely conducted to assess MD patients. New biomarkers, including some proteins and circulating cell-free mitochondrial DNA, with increased diagnostic specificity have been identified in the last decade and have been proposed as potentially useful in the assessment of clinical outcomes. Despite these advances, even these new biomarkers are not sufficiently specific and sensitive to assess MD progression, and new biomarkers that indicate MD progression are urgently needed to monitor the success of novel therapeutic strategies. In this report, we review the mitochondrial biomarkers that are currently analyzed in clinical laboratories, new biomarkers, an overview of the most common laboratory diagnostic techniques, and future directions regarding targeted versus untargeted metabolomic and genomic approaches in the clinical laboratory setting. Brief descriptions of the current methodologies are also provided.

Benchmarking Low-Frequency Variant Calling With Long-Read Data on Mitochondrial DNA

Background: Sequencing quality has improved over the last decade for long-reads, allowing for more accurate detection of somatic low-frequency variants. In this study, we used mixtures of mitochondrial samples with different haplogroups (i.e., a specific set of mitochondrial variants) to investigate the applicability of nanopore sequencing for low-frequency single nucleotide variant detection.

Methods: We investigated the impact of base-calling, alignment/mapping, quality control steps, and variant calling by comparing the results to a previously derived short-read gold standard generated on the Illumina NextSeq. For nanopore sequencing, six mixtures of four different haplotypes were prepared, allowing us to reliably check for expected variants at the predefined 5%, 2%, and 1% mixture levels. We used two different versions of Guppy for base-calling, two aligners (i.e., Minimap2 and Ngmlr), and three variant callers (i.e., Mutserve2, Freebayes, and Nanopanel2) to compare low-frequency variants. We used F₁ score measurements to assess the performance of variant calling.

Results: We observed a mean read length of 11 kb and a mean overall read quality of 15. Ngmlr showed not only higher F₁ scores but also higher allele frequencies (AF) of false-positive calls across the mixtures (mean F₁ score = 0.83; false-positive allele frequencies < 0.17) compared to Minimap2 (mean F₁ score = 0.82; false-positive AF < 0.06). Mutserve2 had the highest F₁ scores (5% level: F₁ score >0.99, 2% level: F₁ score >0.54, and 1% level: F₁ score >0.70) across all callers and mixture levels.

Conclusion: We here present the benchmarking for low-frequency variant calling with nanopore sequencing by identifying current limitations.