pHMM-tree: phylogeny of profile hidden Markov models

被引:11
|
作者
Huo, Luyang [1 ]
Zhang, Han [1 ]
Huo, Xueting [1 ]
Yang, Yasong [2 ]
Li, Xueqiong [2 ]
Yin, Yanbin [2 ]
机构
[1] Nankai Univ, Coll Comp & Control Engn, Tianjin, Peoples R China
[2] Northern Illinois Univ, Dept Biol Sci, De Kalb, IL 60115 USA
基金
美国国家卫生研究院;
关键词
DATABASE;
D O I
10.1093/bioinformatics/btw779
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Protein families are often represented by profile hidden Markov models (pHMMs). Homology between two distant protein families can be determined by comparing the pHMMs. Here we explored the idea of building a phylogeny of protein families using the distance matrix of their pHMMs. We developed a new software and web server (pHMM-tree) to allow four major types of inputs: (i) multiple pHMM files, (ii) multiple aligned protein sequence files, (iii) mixture of pHMM and aligned sequence files and (iv) unaligned protein sequences in a single file. The output will be a pHMM phylogeny of different protein families delineating their relationships. We have applied pHMM-tree to build phylogenies for CAZyme (carbohydrate active enzyme) classes and Pfam clans, which attested its usefulness in the phylogenetic representation of the evolutionary relationship among distant protein families.
引用
收藏
页码:1093 / 1095
页数:3
相关论文
共 50 条
  • [21] Tree-based covariance modeling of hidden Markov models
    Tian, Ye
    Zhou, Jian-Lai
    Lin, Hui
    Jiang, Hui
    IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2006, 14 (06): : 2134 - 2146
  • [22] Image Denoising Using Bandelets and Hidden Markov Tree Models
    Zhang Wenge
    Wang Suang
    Liu Fang
    Gao Xinbo
    Jiao Licheng
    CHINESE JOURNAL OF ELECTRONICS, 2010, 19 (04): : 646 - 650
  • [23] Characterizing activity sequences using profile Hidden Markov Models
    Liu, Feng
    Janssens, Davy
    Cui, JianXun
    Wets, Geert
    Cools, Mario
    EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (13) : 5705 - 5722
  • [24] Fuzzy profile hidden Markov models for protein sequence analysis
    Bidargaddi, NP
    Chetty, M
    Kamruzzaman, J
    PROCEEDINGS OF THE 2005 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 2005, : 427 - 434
  • [25] Identification and classification of conopeptides using profile Hidden Markov Models
    Laht, Silja
    Koua, Dominique
    Kaplinski, Lauris
    Lisacek, Frederique
    Stoecklin, Reto
    Remm, Maido
    BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS, 2012, 1824 (03): : 488 - 492
  • [26] Efficient estimation of emission probabilities in profile hidden Markov models
    Ahola, V
    Aittokallio, T
    Uusipaikka, E
    Vihinen, M
    BIOINFORMATICS, 2003, 19 (18) : 2359 - 2368
  • [27] Bayesian Monte Carlo estimation for profile hidden Markov models
    Lewis, Steven J.
    Raval, Alpan
    Angus, John E.
    MATHEMATICAL AND COMPUTER MODELLING, 2008, 47 (11-12) : 1198 - 1216
  • [28] Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis
    Mutter, Stefan
    Pfahringer, Bernhard
    Holmes, Geoffrey
    AI 2008: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2008, 5360 : 278 - 288
  • [29] aphid: an R package for analysis with profile hidden Markov models
    Wilkinson, Shaun P.
    BIOINFORMATICS, 2019, 35 (19) : 3829 - 3830
  • [30] Hierarchical hidden Markov models for user/process profile learning
    Galassi, Ugo
    Botta, Marco
    Giordana, Attilio
    FUNDAMENTA INFORMATICAE, 2007, 78 (04) : 487 - 505