Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits
Summary: Functional impact of noncoding variants can be predicted using computational approaches. Although predictive scores can be insightful, implementing the scores for a custom variant set and associating scores with complex traits require multiple phases of analysis. Here, we present a protocol...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | STAR Protocols |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166725001443 |
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| author | Enrique Mondragon-Estrada Sarah U. Morton |
| author_facet | Enrique Mondragon-Estrada Sarah U. Morton |
| author_sort | Enrique Mondragon-Estrada |
| collection | DOAJ |
| description | Summary: Functional impact of noncoding variants can be predicted using computational approaches. Although predictive scores can be insightful, implementing the scores for a custom variant set and associating scores with complex traits require multiple phases of analysis. Here, we present a protocol for prioritizing variants by generating deep-learning-predicted functional scores and relating them with brain traits. We describe steps for score prediction, statistical comparison, phenotype correlation, and functional enrichment analysis. This protocol can be generalized to different models and phenotypes.For complete details on the use and execution of this protocol, please refer to Mondragon-Estrada et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
| format | Article |
| id | doaj-art-0e2a217c76f44a79989f6ac48f1f76a8 |
| institution | OA Journals |
| issn | 2666-1667 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj-art-0e2a217c76f44a79989f6ac48f1f76a82025-08-20T02:16:29ZengElsevierSTAR Protocols2666-16672025-06-016210373810.1016/j.xpro.2025.103738Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traitsEnrique Mondragon-Estrada0Sarah U. Morton1Division of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA 02115, USADivision of Newborn Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Corresponding authorSummary: Functional impact of noncoding variants can be predicted using computational approaches. Although predictive scores can be insightful, implementing the scores for a custom variant set and associating scores with complex traits require multiple phases of analysis. Here, we present a protocol for prioritizing variants by generating deep-learning-predicted functional scores and relating them with brain traits. We describe steps for score prediction, statistical comparison, phenotype correlation, and functional enrichment analysis. This protocol can be generalized to different models and phenotypes.For complete details on the use and execution of this protocol, please refer to Mondragon-Estrada et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.http://www.sciencedirect.com/science/article/pii/S2666166725001443BioinformaticsComputer sciencesGeneticsGenomicsHealth SciencesNeuroscience |
| spellingShingle | Enrique Mondragon-Estrada Sarah U. Morton Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits STAR Protocols Bioinformatics Computer sciences Genetics Genomics Health Sciences Neuroscience |
| title | Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits |
| title_full | Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits |
| title_fullStr | Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits |
| title_full_unstemmed | Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits |
| title_short | Protocol to analyze deep-learning-predicted functional scores for noncoding de novo variants and their correlation with complex brain traits |
| title_sort | protocol to analyze deep learning predicted functional scores for noncoding de novo variants and their correlation with complex brain traits |
| topic | Bioinformatics Computer sciences Genetics Genomics Health Sciences Neuroscience |
| url | http://www.sciencedirect.com/science/article/pii/S2666166725001443 |
| work_keys_str_mv | AT enriquemondragonestrada protocoltoanalyzedeeplearningpredictedfunctionalscoresfornoncodingdenovovariantsandtheircorrelationwithcomplexbraintraits AT sarahumorton protocoltoanalyzedeeplearningpredictedfunctionalscoresfornoncodingdenovovariantsandtheircorrelationwithcomplexbraintraits |