Abstract
Genomic variations have been intensively studied since the development of high-throughput sequencing technologies. There are numerous tools and databases predicting and annotating the functional impact of genetic variants, such as determining whether a variant is neutral or deleterious to the functions of the corresponding protein. However, there is a need for methods that not only identify neutral or deleterious mutations but also provide fine grained prediction on the outcome resulting from mutations, such as gain, loss, or switch of function. This paper proposes the deployment of multiple hidden Markov models to computationally classify mutations by functional impact type.
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Liu, M., Watson, L.T., Zhang, L. (2014). Classification of Mutations by Functional Impact Type: Gain of Function, Loss of Function, and Switch of Function. In: Basu, M., Pan, Y., Wang, J. (eds) Bioinformatics Research and Applications. ISBRA 2014. Lecture Notes in Computer Science(), vol 8492. Springer, Cham. https://doi.org/10.1007/978-3-319-08171-7_21
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DOI: https://doi.org/10.1007/978-3-319-08171-7_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08170-0
Online ISBN: 978-3-319-08171-7
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