In silico methods for predicting functional synonymous variants
Abstract Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be “silent,” but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improv...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-05-01
|
| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-023-02966-1 |
| _version_ | 1797817981149904896 |
|---|---|
| author | Brian C. Lin Upendra Katneni Katarzyna I. Jankowska Douglas Meyer Chava Kimchi-Sarfaty |
| author_facet | Brian C. Lin Upendra Katneni Katarzyna I. Jankowska Douglas Meyer Chava Kimchi-Sarfaty |
| author_sort | Brian C. Lin |
| collection | DOAJ |
| description | Abstract Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be “silent,” but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs. |
| first_indexed | 2024-03-13T09:01:32Z |
| format | Article |
| id | doaj.art-572a8e7434a04c1887b2b7b511abdcc2 |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-03-13T09:01:32Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-572a8e7434a04c1887b2b7b511abdcc22023-05-28T11:18:13ZengBMCGenome Biology1474-760X2023-05-0124112510.1186/s13059-023-02966-1In silico methods for predicting functional synonymous variantsBrian C. Lin0Upendra Katneni1Katarzyna I. Jankowska2Douglas Meyer3Chava Kimchi-Sarfaty4Hemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDAHemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDAHemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDAHemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDAHemostasis Branch 1, Division of Hemostasis, Office of Plasma Protein Therapeutics CMC, Office of Therapeutic Products, Center for Biologics Evaluation and Research, US FDAAbstract Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be “silent,” but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.https://doi.org/10.1186/s13059-023-02966-1 |
| spellingShingle | Brian C. Lin Upendra Katneni Katarzyna I. Jankowska Douglas Meyer Chava Kimchi-Sarfaty In silico methods for predicting functional synonymous variants Genome Biology |
| title | In silico methods for predicting functional synonymous variants |
| title_full | In silico methods for predicting functional synonymous variants |
| title_fullStr | In silico methods for predicting functional synonymous variants |
| title_full_unstemmed | In silico methods for predicting functional synonymous variants |
| title_short | In silico methods for predicting functional synonymous variants |
| title_sort | in silico methods for predicting functional synonymous variants |
| url | https://doi.org/10.1186/s13059-023-02966-1 |
| work_keys_str_mv | AT brianclin insilicomethodsforpredictingfunctionalsynonymousvariants AT upendrakatneni insilicomethodsforpredictingfunctionalsynonymousvariants AT katarzynaijankowska insilicomethodsforpredictingfunctionalsynonymousvariants AT douglasmeyer insilicomethodsforpredictingfunctionalsynonymousvariants AT chavakimchisarfaty insilicomethodsforpredictingfunctionalsynonymousvariants |