Evidence map›Paper›PMID 34654896›Full record

ArticleScientific reports2021

A benchmarking of human mitochondrial DNA haplogroup classifiers from whole-genome and whole-exome sequence data.

Víctor García-Olivares, Adrián Muñoz-Barrera, José M Lorenzo-Salazar, Carlos Zaragoza-Trello, Luis A Rubio-Rodríguez, Ana Díaz-de Usera, David Jáspez, Antonio Iñigo-Campos, Rafaela González-Montelongo, Carlos Flores

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In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Benchmarking of bioinformatics tools for the hybridComputational and structural biotechnology journal · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Víctor García-OlivaresGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
Adrián Muñoz-BarreraGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
José M Lorenzo-SalazarGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
Carlos Zaragoza-TrelloEstación Biológica de Doñana (EBD-CSIC), Integrative Ecology, 41092, Sevilla, Spain.
Luis A Rubio-RodríguezGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
Ana Díaz-de UseraGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
David JáspezGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
Antonio Iñigo-CamposGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
Rafaela González-MontelongoGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain.
Carlos FloresGenomics Division, Instituto Tecnológico Y de Energías Renovables (ITER), Santa Cruz de Tenerife, Spain. cflores@ull.edu.es.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mitochondrial genome (mtDNA) is of interest for a range of fields including evolutionary, forensic, and medical genetics. Human mitogenomes can be classified into evolutionary related haplogroups that provide ancestral information and pedigree relationships. Because of this and the advent of high-throughput sequencing (HTS) technology, there is a diversity of bioinformatic tools for haplogroup classification. We present a benchmarking of the 11 most salient tools for human mtDNA classification using empirical whole-genome (WGS) and whole-exome (WES) short-read sequencing data from 36 unrelated donors. We also assessed the best performing tool in third-generation long noisy read WGS data obtained with nanopore technology for a subset of the donors. We found that, for short-read WGS, most of the tools exhibit high accuracy for haplogroup classification irrespective of the input file used for the analysis. However, for short-read WES, Haplocheck and MixEmt were the most accurate tools. Based on the performance shown for WGS and WES, and the accompanying qualitative assessment, Haplocheck stands out as the most complete tool. For third-generation HTS data, we also showed that Haplocheck was able to accurately retrieve mtDNA haplogroups for all samples assessed, although only after following assembly-based approaches (either based on a referenced-based assembly or a hybrid de novo assembly). Taken together, our results provide guidance for researchers to select the most suitable tool to conduct the mtDNA analyses from HTS data.

Indexed as

BenchmarkingComputational BiologyDNA, MitochondrialGenome, MitochondrialHaplotypesHumansWhole Genome SequencingDNA, Mitochondrial

Identifiers

PMID34654896
PMCPMC8519921

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.