Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia
Abstract Background The utilization of genome/exome sequencing for managing cancer patients is rising. However, deciphering the genomic variations and determining their pathogenicity can be intricate. The widely accepted practice of using in silico pathogenicity predictions as evidence when interpre...
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SpringerOpen
2025-06-01
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| Series: | Egyptian Journal of Medical Human Genetics |
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| Online Access: | https://doi.org/10.1186/s43042-025-00734-3 |
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| author | Wardah Qureshi Muhammad Irfan Ishtiaq Ahmad Khan Muhammad Shakeel |
| author_facet | Wardah Qureshi Muhammad Irfan Ishtiaq Ahmad Khan Muhammad Shakeel |
| author_sort | Wardah Qureshi |
| collection | DOAJ |
| description | Abstract Background The utilization of genome/exome sequencing for managing cancer patients is rising. However, deciphering the genomic variations and determining their pathogenicity can be intricate. The widely accepted practice of using in silico pathogenicity predictions as evidence when interpreting genetic variants is an integral part of standard variant classification guidelines. Several algorithms have been developed and evaluated to predict deleterious variants. The objective of this study was to assess the performance of 34 pathogenicity prediction tools (such as BayesDel, CADD, ClinPred, DANN, DEOGEN2, Eigen-PC, FATHMM, GERP++, M-CAP, MetaLR, MutationAssessor, MutationTaster, MutPred, Polyphen2, PROVEAN, REVEL, and SIFT) on the latest version of ClinVar dataset and implement it on the exome sequence data of acute myeloid leukemia (AML) patients to assess the performance to these tools in clinical samples. Results While predicting the pathogenicity of genetic variants, there were 6 in silico tools having specificity > 0.9 and 14 tools having sensitivity > 0.9 on the ClinVar dataset. Further, three tools BayesDel, MetaRNN, and ClinPred demonstrated highest accuracy achieving sensitivity 0.9337–0.9627 and specificity 0.9245–0.9513. By applying these 3 tools on the present study AML exome dataset, 1421, 1235, and 2033 potential deleterious variants in 410 AML-associated genes were observed, respectively. Conclusion This comparison highlighted the in silico tools to predict the potential pathogenicity of the variants which otherwise might have been classified as variants of uncertain significance (VUS). The finding can help in the genetic risk assessment and targeted therapeutic approaches in AML. |
| format | Article |
| id | doaj-art-bef1af49b64e4e34bcb59f83c350108c |
| institution | OA Journals |
| issn | 2090-2441 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Egyptian Journal of Medical Human Genetics |
| spelling | doaj-art-bef1af49b64e4e34bcb59f83c350108c2025-08-20T02:10:32ZengSpringerOpenEgyptian Journal of Medical Human Genetics2090-24412025-06-012611810.1186/s43042-025-00734-3Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemiaWardah Qureshi0Muhammad Irfan1Ishtiaq Ahmad Khan2Muhammad Shakeel3Jamil-ur-Rahman Center for Genome Research, Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of KarachiJamil-ur-Rahman Center for Genome Research, Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of KarachiJamil-ur-Rahman Center for Genome Research, Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of KarachiJamil-ur-Rahman Center for Genome Research, Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of KarachiAbstract Background The utilization of genome/exome sequencing for managing cancer patients is rising. However, deciphering the genomic variations and determining their pathogenicity can be intricate. The widely accepted practice of using in silico pathogenicity predictions as evidence when interpreting genetic variants is an integral part of standard variant classification guidelines. Several algorithms have been developed and evaluated to predict deleterious variants. The objective of this study was to assess the performance of 34 pathogenicity prediction tools (such as BayesDel, CADD, ClinPred, DANN, DEOGEN2, Eigen-PC, FATHMM, GERP++, M-CAP, MetaLR, MutationAssessor, MutationTaster, MutPred, Polyphen2, PROVEAN, REVEL, and SIFT) on the latest version of ClinVar dataset and implement it on the exome sequence data of acute myeloid leukemia (AML) patients to assess the performance to these tools in clinical samples. Results While predicting the pathogenicity of genetic variants, there were 6 in silico tools having specificity > 0.9 and 14 tools having sensitivity > 0.9 on the ClinVar dataset. Further, three tools BayesDel, MetaRNN, and ClinPred demonstrated highest accuracy achieving sensitivity 0.9337–0.9627 and specificity 0.9245–0.9513. By applying these 3 tools on the present study AML exome dataset, 1421, 1235, and 2033 potential deleterious variants in 410 AML-associated genes were observed, respectively. Conclusion This comparison highlighted the in silico tools to predict the potential pathogenicity of the variants which otherwise might have been classified as variants of uncertain significance (VUS). The finding can help in the genetic risk assessment and targeted therapeutic approaches in AML.https://doi.org/10.1186/s43042-025-00734-3AMLDeleteriousIn silicoPathogenicWhole-exome sequencing |
| spellingShingle | Wardah Qureshi Muhammad Irfan Ishtiaq Ahmad Khan Muhammad Shakeel Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia Egyptian Journal of Medical Human Genetics AML Deleterious In silico Pathogenic Whole-exome sequencing |
| title | Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia |
| title_full | Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia |
| title_fullStr | Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia |
| title_full_unstemmed | Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia |
| title_short | Evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia |
| title_sort | evaluation of different in silico tools for the assessment of deleterious variants in acute myeloid leukemia |
| topic | AML Deleterious In silico Pathogenic Whole-exome sequencing |
| url | https://doi.org/10.1186/s43042-025-00734-3 |
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