Performance analysis of conventional and AI-based variant callers using short and long reads
Abstract Background The 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 us...
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-023-05596-3 |
| _version_ | 1797388077909409792 |
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| author | Omar Abdelwahab François Belzile Davoud Torkamaneh |
| author_facet | Omar Abdelwahab François Belzile Davoud Torkamaneh |
| author_sort | Omar Abdelwahab |
| collection | DOAJ |
| description | Abstract Background The 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. Results In 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. Conclusion This study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data. |
| first_indexed | 2024-03-08T22:34:31Z |
| format | Article |
| id | doaj.art-a37b96b7a61545c8a3eb55bfdf6f8930 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-08T22:34:31Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-a37b96b7a61545c8a3eb55bfdf6f89302023-12-17T12:31:55ZengBMCBMC Bioinformatics1471-21052023-12-0124111310.1186/s12859-023-05596-3Performance analysis of conventional and AI-based variant callers using short and long readsOmar Abdelwahab0François Belzile1Davoud Torkamaneh2Département de Phytologie, Université LavalDépartement de Phytologie, Université LavalDépartement de Phytologie, Université LavalAbstract Background The 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. Results In 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. Conclusion This study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.https://doi.org/10.1186/s12859-023-05596-3GenomicsSequencingVariant callingNGSArtificial intelligence |
| spellingShingle | Omar Abdelwahab François Belzile Davoud Torkamaneh Performance analysis of conventional and AI-based variant callers using short and long reads BMC Bioinformatics Genomics Sequencing Variant calling NGS Artificial intelligence |
| title | Performance analysis of conventional and AI-based variant callers using short and long reads |
| title_full | Performance analysis of conventional and AI-based variant callers using short and long reads |
| title_fullStr | Performance analysis of conventional and AI-based variant callers using short and long reads |
| title_full_unstemmed | Performance analysis of conventional and AI-based variant callers using short and long reads |
| title_short | Performance analysis of conventional and AI-based variant callers using short and long reads |
| title_sort | performance analysis of conventional and ai based variant callers using short and long reads |
| topic | Genomics Sequencing Variant calling NGS Artificial intelligence |
| url | https://doi.org/10.1186/s12859-023-05596-3 |
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