ArticleBioinformatics (Oxford, England)2017
HIPred: an integrative approach to predicting haploinsufficient genes.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
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- Proteome-scale prediction of molecular mechanisms underlying dominant genetic diseases.PloS one · 2024Article
- Nemaline myopathy: reclassification of previously reported variants according to ACMG guidelines, and report of novel genetic variants.European journal of human genetics : EJHG · 2023Article
- Large-scale across species transcriptomic analysis identifies genetic selection signatures associated with longevity in mammals.The EMBO journal · 2023Article
- Distinct longevity mechanisms across and within species and their association with aging.Cell · 2023Article
- Buffering of genetic dominance by allele-specific protein complex assembly.Science advances · 2023Article
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- Faster and more accurate pathogenic combination predictions with VarCoPP2.0.BMC bioinformatics · 2023Article
- Germline loss-of-functionmedRxiv : the preprint server for health sciences · 2023Article
- Germline loss-of-functionFrontiers in endocrinology · 2023Article
- Translational enhancement by base editing of the Kozak sequence rescues haploinsufficiency.Nucleic acids research · 2022Article
- Proteotype coevolution and quantitative diversity across 11 mammalian species.Science advances · 2022Article
- Integration of Data from Liquid-Liquid Phase Separation Databases Highlights Concentration and Dosage Sensitivity of LLPS Drivers.International journal of molecular sciences · 2021Article
- Neurodevelopmental Disorders in Patients With Complex Phenotypes and Potential Complex Genetic Basis Involving Non-Coding Genes, and Double CNVs.Frontiers in genetics · 2021Article
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4 authors.
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Abstract
motivationA major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency incorporate biological networks, e.g. protein-protein interaction networks that have recently been shown to introduce study bias. As a result, these methods tend to perform best on well-studied genes, but underperform on less studied genes. The advent of large genome sequencing consortia, such as the 1000 genomes project, NHLBI Exome Sequencing Project and the Exome Aggregation Consortium creates an urgent need for unbiased haploinsufficiency prediction methods.
resultsHere, we describe a machine learning approach, called HIPred, that integrates genomic and evolutionary information from ENSEMBL, with functional annotations from the Encyclopaedia of DNA Elements consortium and the NIH Roadmap Epigenomics Project to predict haploinsufficiency, without the study bias described earlier. We benchmark HIPred using several datasets and show that our unbiased method performs as well as, and in most cases, outperforms existing biased algorithms. AVAILABILITY AND IMPLEMENTATION: HIPred scores for all gene identifiers are available at: https://github.com/HAShihab/HIPred . CONTACT: h.shihab@bristol.ac.uk or tom.gaunt@bristol.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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