Profile HMM based Multiple Sequence Alignment for DNA Sequences

被引:1
作者
Mulia, Sudipta [1 ]
Mishra, Debahuti [1 ]
Jena, Tanushree [2 ]
机构
[1] Siksha O Anusandhan Deemed Univ, Inst Tech Educ & Res, Bhubaneswar, Odisha, India
[2] Coll Engn Bhubaneswar, Bhubaneswar, Orissa, India
来源
INTERNATIONAL CONFERENCE ON MODELLING OPTIMIZATION AND COMPUTING | 2012年 / 38卷
关键词
Bioinformatics; Profile Hidden Markov Model; Multiple Sequence Alignment; Protein; DNA; RNA;
D O I
10.1016/j.proeng.2012.06.218
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Sequence alignment is a central tool in molecular biology. A Multiple sequence alignment (MSA) is a sequence alignment of three or more biological sequences, generally protein, DNA or RNA to identify regions of similarity that may be a consequence of functional, structural or evolutionary relationships between the sequences. High sequence similarity between molecules usually implies significant structural and functional similarities in an alignment. Three or more sequences of biologically relevant length can be difficult and are almost always time-consuming to align by hand, computational algorithms are used to produce and analyze the alignments. MSAs require more sophisticated methodologies because they are more computationally complex. A hidden markov model (HMM) is a probabilistic finite state machine which is widely used in biological sequence analysis. Profile HMMs are specific types of HMM used in biological sequence analysis. In this paper, we show how Profile HMMs can be useful for multiple sequence alignment. We test their applicability to the tasks of multiple alignments and find that they work well. (c) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education
引用
收藏
页码:1783 / 1787
页数:5
相关论文
共 3 条
[1]  
Bhargava A, 2009, MULTIPLEWORD ALIGNME, P43
[2]  
Binaey E, 2001, IBM J RES DEV ARCH, V45, P3
[3]  
Sievers F, FAST SCALABLE GENERA