Non-Homogeneous Hidden Markov Model Qingyuan Liu Introduction (Why - - PowerPoint PPT Presentation

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Dec 07, 2022 198 likes •294 views

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)

SLIDE 1

Non-Homogeneous Hidden Markov Model

Qingyuan Liu

SLIDE 2

Introduction (Why Homogeneous HMM)

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

SLIDE 3

Introduction (Building a HMM)

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

Phylogeny-aware Profiles (UPP)

Parameter

– Emission Probability – Transition Probability

SLIDE 4

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

SLIDE 5

Significance

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

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

SLIDE 6

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

SLIDE 7

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

SLIDE 8

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