Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data
Abstract Background Whole exome sequencing (WES) is a cost-effective method that identifies clinical variants but it demands accurate variant caller tools. Currently available tools have variable accuracy in predicting specific clinical variants. But it may be possible to find the best combination o...
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| Format: | Article |
| Language: | English |
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BMC
2019-06-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-019-2928-9 |
| _version_ | 1818228943594979328 |
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| author | Manojkumar Kumaran Umadevi Subramanian Bharanidharan Devarajan |
| author_facet | Manojkumar Kumaran Umadevi Subramanian Bharanidharan Devarajan |
| author_sort | Manojkumar Kumaran |
| collection | DOAJ |
| description | Abstract Background Whole exome sequencing (WES) is a cost-effective method that identifies clinical variants but it demands accurate variant caller tools. Currently available tools have variable accuracy in predicting specific clinical variants. But it may be possible to find the best combination of aligner-variant caller tools for detecting accurate single nucleotide variants (SNVs) and small insertion and deletion (InDels) separately. Moreover, many important aspects of InDel detection are overlooked while comparing the performance of tools, particularly its base pair length. Results We assessed the performance of variant calling pipelines using the combinations of four variant callers and five aligners on human NA12878 and simulated exome data. We used high confidence variant calls from Genome in a Bottle (GiaB) consortium for validation, and GRCh37 and GRCh38 as the human reference genome. Based on the performance metrics, both BWA and Novoalign aligners performed better with DeepVariant and SAMtools callers for detecting SNVs, and with DeepVariant and GATK for InDels. Furthermore, we obtained similar results on human NA24385 and NA24631 exome data from GiaB. Conclusion In this study, DeepVariant with BWA and Novoalign performed best for detecting accurate SNVs and InDels. The accuracy of variant calling was improved by merging the top performing pipelines. The results of our study provide useful recommendations for analysis of WES data in clinical genomics. |
| first_indexed | 2024-12-12T10:10:44Z |
| format | Article |
| id | doaj.art-f594c2bd79884f0890f236e2fc58e2db |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-12T10:10:44Z |
| publishDate | 2019-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-f594c2bd79884f0890f236e2fc58e2db2022-12-22T00:27:49ZengBMCBMC Bioinformatics1471-21052019-06-0120111110.1186/s12859-019-2928-9Performance assessment of variant calling pipelines using human whole exome sequencing and simulated dataManojkumar Kumaran0Umadevi Subramanian1Bharanidharan Devarajan2Department of Bioinformatics, Aravind Medical Research FoundationDepartment of Bioinformatics, Aravind Medical Research FoundationDepartment of Bioinformatics, Aravind Medical Research FoundationAbstract Background Whole exome sequencing (WES) is a cost-effective method that identifies clinical variants but it demands accurate variant caller tools. Currently available tools have variable accuracy in predicting specific clinical variants. But it may be possible to find the best combination of aligner-variant caller tools for detecting accurate single nucleotide variants (SNVs) and small insertion and deletion (InDels) separately. Moreover, many important aspects of InDel detection are overlooked while comparing the performance of tools, particularly its base pair length. Results We assessed the performance of variant calling pipelines using the combinations of four variant callers and five aligners on human NA12878 and simulated exome data. We used high confidence variant calls from Genome in a Bottle (GiaB) consortium for validation, and GRCh37 and GRCh38 as the human reference genome. Based on the performance metrics, both BWA and Novoalign aligners performed better with DeepVariant and SAMtools callers for detecting SNVs, and with DeepVariant and GATK for InDels. Furthermore, we obtained similar results on human NA24385 and NA24631 exome data from GiaB. Conclusion In this study, DeepVariant with BWA and Novoalign performed best for detecting accurate SNVs and InDels. The accuracy of variant calling was improved by merging the top performing pipelines. The results of our study provide useful recommendations for analysis of WES data in clinical genomics.http://link.springer.com/article/10.1186/s12859-019-2928-9Whole exome sequencingSimulated exome dataHuman reference genomeVariant calling pipelinesSNVs and InDels |
| spellingShingle | Manojkumar Kumaran Umadevi Subramanian Bharanidharan Devarajan Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data BMC Bioinformatics Whole exome sequencing Simulated exome data Human reference genome Variant calling pipelines SNVs and InDels |
| title | Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data |
| title_full | Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data |
| title_fullStr | Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data |
| title_full_unstemmed | Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data |
| title_short | Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data |
| title_sort | performance assessment of variant calling pipelines using human whole exome sequencing and simulated data |
| topic | Whole exome sequencing Simulated exome data Human reference genome Variant calling pipelines SNVs and InDels |
| url | http://link.springer.com/article/10.1186/s12859-019-2928-9 |
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