ArticleDNA research : an international journal for rapid publication of reports on genes and genomes2018
ARIADNA: machine learning method for ancient DNA variant discovery.
Article in DNA research : an international journal for rapid publication of reports on genes and genomes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed.
- Recovery and analysis of ancient DNA: challenges, methods, and applications in forensic and archaeological science.Journal, genetic engineering & biotechnology · 2026Article
- Genomic analyses correspond with deep persistence of peoples of Blackfoot Confederacy from glacial times.Science advances · 2024Article
- The genomic prehistory of the Indigenous peoples of Uruguay.PNAS nexus · 2022Article
- Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.Frontiers in immunology · 2022Article
- Article
- Exploring the Consistency of the Quality Scores with Machine Learning for Next-Generation Sequencing Experiments.BioMed research international · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and despite showing extreme sensitivity to aDNA quality, these methods have been used in many published studies, sometimes with additions of arbitrary filters or modifications, designed to overcome aDNA degradation and contamination problems. The general lack of best practices for aDNA mutation calling may lead to inaccurate results. To address these problems, we present ARIADNA (ARtificial Intelligence for Ancient DNA), a novel approach based on machine learning techniques, using specific aDNA characteristics as features to yield improved mutation calls. In our comparisons of variant callers across several ancient genomes, ARIADNA consistently detected higher-quality genome variants with fast runtimes, while reducing the false positive rate compared with other approaches.
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Registered trials
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.