Artikel ; Online: How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.
IEEE/ACM transactions on computational biology and bioinformatics
2023 Band 20, Heft 2, Seite(n) 1594–1599
Abstract: The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation ... ...
Abstract | The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding. The experimental results not only showed the superiority of deepBICS on ChIP-seq data sets but also suggested deepBICS as a language model could help the classification of disease-related and neutral variants. We then built a language model-based method called deepBICS4SNV to predict the pathogenicity of single nucleotide variants. The good performance of deepBICS4SNV on 2 tests related to Mendelian disorders and viral diseases shows the sequence contextual information derived from language models can improve prediction accuracy and generalization capability. |
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Mesh-Begriff(e) | Humans ; Chromatin Immunoprecipitation Sequencing ; Deep Learning ; Virulence ; Binding Sites/genetics ; DNA/genetics ; Transcription Factors/genetics ; Transcription Factors/metabolism ; Nucleotides |
Chemische Substanzen | DNA (9007-49-2) ; Transcription Factors ; Nucleotides |
Sprache | Englisch |
Erscheinungsdatum | 2023-04-03 |
Erscheinungsland | United States |
Dokumenttyp | Journal Article ; Research Support, Non-U.S. Gov't |
ISSN | 1557-9964 |
ISSN (online) | 1557-9964 |
DOI | 10.1109/TCBB.2022.3170343 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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