Benchmarking of variant calling software for whole-exome sequencing using gold standard datasets
Abstract Accurate variant calling from whole-exome sequencing (WES) data is vital for understanding genetic diseases. Recently, commercial variant calling software have emerged that do not require bioinformatics or programming expertise, hence enabling independent analysis of WES data by smaller lab...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97047-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850176927724208128 |
|---|---|
| author | Matthew Wong Bryan Liew Melissa Hum Ning Yuan Lee Ann S. G. Lee |
| author_facet | Matthew Wong Bryan Liew Melissa Hum Ning Yuan Lee Ann S. G. Lee |
| author_sort | Matthew Wong |
| collection | DOAJ |
| description | Abstract Accurate variant calling from whole-exome sequencing (WES) data is vital for understanding genetic diseases. Recently, commercial variant calling software have emerged that do not require bioinformatics or programming expertise, hence enabling independent analysis of WES data by smaller laboratories and clinics and circumventing the need for dedicated and expensive computers and bioinformatics staff. This study benchmarks four non-programming variant calling software namely, Illumina BaseSpace Sequence Hub (Illumina), CLC Genomics Workbench (CLC), Partek Flow, and Varsome Clinical, for the variant calling of three Genome in a Bottle (GIAB) whole-exome sequencing datasets (HG001, HG002 and HG003). Following alignment of sequence reads to the human reference genome GRCh38, variants were compared against high-confidence regions from GIAB datasets and assessed using the Variant Calling Assessment Tool (VCAT). Illumina’s DRAGEN Enrichment achieved the highest precision and recall scores for single nucleotide variant (SNV) and insertions/deletion (indel) calling at over 99% for SNVs and 96% for indels while Partek Flow using unionised variant calls from Freebayes and Samtools had the lowest indel calling performance. Illumina had the highest true positives (TP) variant counts for all samples and all four software shared 98–99% similarity of TP variants. Run times were shortest for CLC and Illumina ranging from 6 to 25 min and 29 to 36 min respectively, while Partek Flow took the longest (3.6 to 29.7 h). This study provides information for clinicians and biologists without programming expertise in their selection of software for variant analysis that balance accuracy, sensitivity, and runtime. |
| format | Article |
| id | doaj-art-b2f08567dec64da3bfa6e6a0ee0616de |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b2f08567dec64da3bfa6e6a0ee0616de2025-08-20T02:19:07ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-97047-7Benchmarking of variant calling software for whole-exome sequencing using gold standard datasetsMatthew Wong0Bryan Liew1Melissa Hum2Ning Yuan Lee3Ann S. G. Lee4Division of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre SingaporeDivision of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre SingaporeDivision of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre SingaporeDivision of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre SingaporeDivision of Cellular and Molecular Research, Humphrey Oei Institute of Cancer Research, National Cancer Centre SingaporeAbstract Accurate variant calling from whole-exome sequencing (WES) data is vital for understanding genetic diseases. Recently, commercial variant calling software have emerged that do not require bioinformatics or programming expertise, hence enabling independent analysis of WES data by smaller laboratories and clinics and circumventing the need for dedicated and expensive computers and bioinformatics staff. This study benchmarks four non-programming variant calling software namely, Illumina BaseSpace Sequence Hub (Illumina), CLC Genomics Workbench (CLC), Partek Flow, and Varsome Clinical, for the variant calling of three Genome in a Bottle (GIAB) whole-exome sequencing datasets (HG001, HG002 and HG003). Following alignment of sequence reads to the human reference genome GRCh38, variants were compared against high-confidence regions from GIAB datasets and assessed using the Variant Calling Assessment Tool (VCAT). Illumina’s DRAGEN Enrichment achieved the highest precision and recall scores for single nucleotide variant (SNV) and insertions/deletion (indel) calling at over 99% for SNVs and 96% for indels while Partek Flow using unionised variant calls from Freebayes and Samtools had the lowest indel calling performance. Illumina had the highest true positives (TP) variant counts for all samples and all four software shared 98–99% similarity of TP variants. Run times were shortest for CLC and Illumina ranging from 6 to 25 min and 29 to 36 min respectively, while Partek Flow took the longest (3.6 to 29.7 h). This study provides information for clinicians and biologists without programming expertise in their selection of software for variant analysis that balance accuracy, sensitivity, and runtime.https://doi.org/10.1038/s41598-025-97047-7BenchmarkingVariant callingWhole-exome sequencingNo-programming softwareGIAB. |
| spellingShingle | Matthew Wong Bryan Liew Melissa Hum Ning Yuan Lee Ann S. G. Lee Benchmarking of variant calling software for whole-exome sequencing using gold standard datasets Scientific Reports Benchmarking Variant calling Whole-exome sequencing No-programming software GIAB. |
| title | Benchmarking of variant calling software for whole-exome sequencing using gold standard datasets |
| title_full | Benchmarking of variant calling software for whole-exome sequencing using gold standard datasets |
| title_fullStr | Benchmarking of variant calling software for whole-exome sequencing using gold standard datasets |
| title_full_unstemmed | Benchmarking of variant calling software for whole-exome sequencing using gold standard datasets |
| title_short | Benchmarking of variant calling software for whole-exome sequencing using gold standard datasets |
| title_sort | benchmarking of variant calling software for whole exome sequencing using gold standard datasets |
| topic | Benchmarking Variant calling Whole-exome sequencing No-programming software GIAB. |
| url | https://doi.org/10.1038/s41598-025-97047-7 |
| work_keys_str_mv | AT matthewwong benchmarkingofvariantcallingsoftwareforwholeexomesequencingusinggoldstandarddatasets AT bryanliew benchmarkingofvariantcallingsoftwareforwholeexomesequencingusinggoldstandarddatasets AT melissahum benchmarkingofvariantcallingsoftwareforwholeexomesequencingusinggoldstandarddatasets AT ningyuanlee benchmarkingofvariantcallingsoftwareforwholeexomesequencingusinggoldstandarddatasets AT annsglee benchmarkingofvariantcallingsoftwareforwholeexomesequencingusinggoldstandarddatasets |