ArticleBMC bioinformatics2023
Performance analysis of conventional and AI-based variant callers using short and long reads.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- AI in variant analysis: fast track to genetic diagnoses.Human genetics · 2026Review
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- An rRNA-depleted full-length transcriptome strategy using nanopore sequencing for identification of novel lncRNA isoforms.Communications biology · 2026Article
- Assessing the Genetic Health and Conservation Value of an Introduced Urban Population of a Critically Endangered Parrot.Evolutionary applications · 2026Article
- Decoding rare inherited metabolic disorders: advancing precision in screening and diagnosis.Orphanet journal of rare diseases · 2026Review
- Modern approaches and future prospects in lettuce breeding for controlled environment agriculture.Frontiers in plant science · 2026Review
- Metagenome-assembled genomes enhance bacterial read decontamination and variant calling in oral samples.iScience · 2025Article
- Entering the era of precision medicine to treat amyotrophic lateral sclerosis.Molecular neurodegeneration · 2025Review
- Circulating Tumour DNA for Ovarian Cancer Diagnosis and Treatment Monitoring: What Perspectives for Clinical Use?International journal of molecular sciences · 2025Review
- Review
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Artificial intelligence in variant calling: a review.Frontiers in bioinformatics · 2025Review
- A method to comprehensively identify germline SNVs, INDELs and CNVs from whole exome sequencing data of BRCA1/2 negative breast cancer patients.NAR genomics and bioinformatics · 2024Article
- Article
- Current Applications and Challenges of Next-Generation Sequencing in Plasma Circulating Tumour DNA of Ovarian Cancer.Biology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundThe accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when various major genome projects have chosen to use different tools. Thus far, most of the existing tools were mainly developed to work on short-read data (i.e., Illumina); however, other sequencing technologies (e.g. PacBio, and Oxford Nanopore) have recently shown that they can also be used for variant calling. In addition, with the emergence of artificial intelligence (AI)-based variant calling tools, there is a pressing need to compare these tools in terms of efficiency, accuracy, computational power, and ease of use.
resultsIn this study, we evaluated five of the most widely used conventional and AI-based variant calling tools (BCFTools, GATK4, Platypus, DNAscope, and DeepVariant) in terms of accuracy and computational cost using both short-read and long-read data derived from three different sequencing technologies (Illumina, PacBio HiFi, and ONT) for the same set of samples from the Genome In A Bottle project. The analysis showed that AI-based variant calling tools supersede conventional ones for calling SNVs and INDELs using both long and short reads in most aspects. In addition, we demonstrate the advantages and drawbacks of each tool while ranking them in each aspect of these comparisons.
conclusionThis study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.
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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.