Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Background: With the advent of next-generation sequencing in genetic testing, predicting the pathogenicity of missense variants represents a major challenge potentially leading to misdiagnoses in the clinical setting. In neurofibromatosis type 1 (NF1), where clinical criteria for diagnosis may not be fully present until late infancy, correct assessment of variant pathogenicity is fundamental for appropriate patients' management. Methods: Here, we analyzed three different computational methods, VEST3, REVEL and ClinPred, and after extracting predictions scores for 1585 NF1 missense variants listed in ClinVar, evaluated their performances and the score distribution throughout the neurofibromin protein. Results: For all the three methods, no significant differences were present between the scores of "likely benign", "benign", and "likely pathogenic", "pathogenic" variants that were consequently collapsed into a single category. The cutoff values for pathogenicity were significantly different for the three methods and among benign and pathogenic variants for all methods. After training five different models with a subset of benign and pathogenic variants, we could reclassify variants in three sharply separated categories. Conclusions: The recently developed metapredictors, which integrate information from multiple components, after gene-specific fine-tuning, could represent useful tools for variant interpretation, particularly in genetic diseases where a clinical diagnosis can be difficult.
Pubmed ID: 31979111
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
View all literature mentionsArchive of aggregated information about sequence variation and its relationship to human health. Provides reports of relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. Collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about submitter, and other supporting data. Alleles described in submissions are mapped to reference sequences, and reported according to HGVS standard.
View all literature mentions