Non-Homogeneous Hidden Markov Model Qingyuan Liu

Introduction (Why Homogeneous HMM)

•  Classify new sequences into new family •  Add related sequences into MSA •  Compute MSA for groups of related sequence

Introduction (Building a HMM)

•  Seed sequences for HMM building •  Ultra-large multiple sequence alignment using

Phylogeny-aware Profiles (UPP) •  Parameter

–  Emission Probability –  Transition Probability

Background (Long Indels) •  HMM can not deal with long indels. •  Example: 10 consecutive residue loss

•  Assume 0.5 for each deletion transition probability •  0.510 is extremely small

Significance •  Cause: Emission probability is fixed •  Do HMM non-homogeneously instead

–  Emission probability is not fixed –  Different parameters for different cases

Project •  Literature review for how to build a non-

homogeneous HMM. •  Propose ideas for how to build an non-homogeneous

HMM for MSA

•  Literature review for other possible MSA methods to deal with long indels

•  Combined tree- and profile-based alignment •  Simulation based approach •  Group-to-group sequence alignment

Literature: •  Sarkar, Abhra, Anindya Bhadra, and Bani K. Mallick. "Nonparametric Bayesian

Approaches to Non-homogeneous Hidden Markov Models.” (n.d.): n. pag. 8 May 2012. Web. 4 Apr. 2017.

•  Ghavidel, Fatemeh Zamanzad, Jargen Claesen, and Tomasz Burzykowski. "A Nonhomogeneous Hidden Markov Model for Gene Mapping Based on Next-Generation Sequencing Data." Journal of Computational Biology 22.2 (2015): 178-88. Web.

•  Grzegorczyk, Marco. "A Non-homogeneous Dynamic Bayesian Network with a Hidden Markov Model Dependency Structure among the Temporal Data Points." Machine Learning 102.2 (2015): 155-207. Web.

•  Gowri-Shankar, V., & Rattray, M. (2007). A Reversible Jump Method for Bayesian Phylogenetic Inference with a Nonhomogeneous Substitution Model. Molecular Biology and Evolution,24(6), 1286-1299. doi:10.1093/molbev/msm046.

•  Aalen, O., & Johansen, S. (1978). An Empirical Transition Matrix for Non-Homogeneous Markov Chains Based on Censored Observations. Scandinavian Journal of Statistics, 5(3), 141-150. Retrieved from http://www.jstor.org/stable/4615704

Literature: •  Vassiliou, P. G. (1997). The evolution of the theory of non‐homogeneous

Markov systems. Applied Stochastic Models and Data Analysis,13(34), 159-176. doi:10.1002/(sici)1099-0747(199709/12)13:3/4<159::aid-asm309>3.3.co;2-h

•  Loytynoja, A., & Goldman, N. (2008). A model of evolution and structure for multiple sequence alignment. Philosophical Transactions of the Royal Society B: Biological Sciences,363(1512), 3913-3919. doi:10.1098/rstb.2008.0170

•  Karin, E. L., Rabin, A., Ashkenazy, H., Shkedy, D., Avram, O., Cartwright, R. A., & Pupko, T. (2015). Inferring Indel Parameters using a Simulation-based Approach. Genome Biology and Evolution,7(12), 3226-3238. doi:10.1093/gbe/evv212

•  Yamada, S., Gotoh, O., & Yamana, H. (2006). BMC Bioinformatics,7(1), 524. doi:10.1186/1471-2105-7-524