ArticleBMC bioinformatics2014
Quantitative prediction of the effect of genetic variation using hidden Markov models.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 16 citations in OpenAlex.
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- Evolution and Diversification ofFrontiers in plant science · 2019Article
- Loss of Janus Associated Kinase 1 Alters Urothelial Cell Function and Facilitates the Development of Bladder Cancer.Frontiers in immunology · 2019Article
- The role of small in-frame insertions/deletions in inherited eye disorders and how structural modelling can help estimate their pathogenicity.Orphanet journal of rare diseases · 2016Article
- HMMvar-func: a new method for predicting the functional outcome of genetic variants.BMC bioinformatics · 2015Article
- Computational approaches to study the effects of small genomic variations.Journal of molecular modeling · 2015Review
- Predicting the combined effect of multiple genetic variants.Human genomics · 2015Article
- The pattern of DNA cleavage intensity around indels.Scientific reports · 2015Article
- The road from next-generation sequencing to personalized medicine.Personalized medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the development of sequencing technologies, more and more sequence variants are available for investigation. Different classes of variants in the human genome have been identified, including single nucleotide substitutions, insertion and deletion, and large structural variations such as duplications and deletions. Insertion and deletion (indel) variants comprise a major proportion of human genetic variation. However, little is known about their effects on humans. The absence of understanding is largely due to the lack of both biological data and computational resources.
resultsThis paper presents a new indel functional prediction method HMMvar based on HMM profiles, which capture the conservation information in sequences. The results demonstrate that a scoring strategy based on HMM profiles can achieve good performance in identifying deleterious or neutral variants for different data sets, and can predict the protein functional effects of both single and multiple mutations.
conclusionsThis paper proposed a quantitative prediction method, HMMvar, to predict the effect of genetic variation using hidden Markov models. The HMM based pipeline program implementing the method HMMvar is freely available at https://bioinformatics.cs.vt.edu/zhanglab/hmm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.