Geometric Approach to Biosequence Analysis

被引:1
|
作者
Brimkov, Boris [1 ]
Brimkov, Valentin E. [2 ]
机构
[1] Rice Univ, Computat & Appl Math, Houston, TX 77005 USA
[2] SUNY Coll Buffalo, Dept Math, Buffalo, NY 14222 USA
关键词
String linearity; deviation from linearity; biosequence comparison; discrete monotone path; PROTEIN SEQUENCES; EVOLUTION;
D O I
10.1007/978-3-319-07581-5_12
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Tools that effectively analyze and compare sequences are of great importance in various areas of applied computational research, especially in the framework of molecular biology. In the present paper, we introduce simple geometric criteria based on the notion of string linearity and use them to compare DNA sequences of various organisms, as well as to distinguish them from random sequences. Our experiments reveal a significant difference between biosequences and random sequences the former having much higher deviation from linearity than the latter as well as a general trend of increasing deviation from linearity between primitive and biologically complex organisms. The proposed approach is potentially applicable to the construction of dendograms representing the evolutionary relationships among species.
引用
收藏
页码:97 / 104
页数:8
相关论文
共 50 条
  • [1] Geometric approach to string analysis for biosequence classification
    Brimkov, Boris
    [J]. JOURNAL OF INTEGRATIVE BIOINFORMATICS, 2014, 11 (03): : 252
  • [2] Biosequence Analysis in PRISM
    Lassen, Ole Torp
    [J]. LOGIC PROGRAMMING, PROCEEDINGS, 2008, 5366 : 809 - 810
  • [3] Nanoarrays for Systolic Biosequence Analysis
    Mehdy, Malik Ashter
    Antidormi, Aleandro
    Graziano, Mariagrazia
    Piccinini, Gianluca
    [J]. JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS, 2018, 27 (12)
  • [4] Genetic approach to biosequence alignment (GABA)
    Rajapakse, JC
    Faleel, I
    [J]. ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING: COMPUTATIONAL INTELLIGENCE FOR THE E-AGE, 2002, : 611 - 615
  • [5] FOURIER METHODS FOR BIOSEQUENCE ANALYSIS
    BENSON, DC
    [J]. NUCLEIC ACIDS RESEARCH, 1990, 18 (21) : 6305 - 6310
  • [6] Effective ambiguity checking in biosequence analysis
    Reeder, J
    Steffen, P
    Giegerich, R
    [J]. BMC BIOINFORMATICS, 2005, 6 (1)
  • [7] A Biosequence-based Approach to Software Characterization
    Oehmen, Christopher S.
    Peterson, Elena S.
    Phillips, Aaron R.
    Curtis, Darren S.
    [J]. 2016 IEEE SYMPOSIUM ON SECURITY AND PRIVACY WORKSHOPS (SPW 2016), 2016, : 118 - 125
  • [8] Effective ambiguity checking in biosequence analysis
    Janina Reeder
    Peter Steffen
    Robert Giegerich
    [J]. BMC Bioinformatics, 6
  • [9] Biosequence Analysis using Intel® Xeon Phi
    Sinha, Pradeep
    Misra, Goldi
    Vikraman, Deepu
    Das, Abhishek
    Desai, Shraddha
    Pawar, Sucheta
    Shewale, Kalyani
    [J]. UKSIM-AMSS SEVENTH EUROPEAN MODELLING SYMPOSIUM ON COMPUTER MODELLING AND SIMULATION (EMS 2013), 2013, : 497 - 499
  • [10] Evolving Turing machines for biosequence recognition and analysis
    Vallejo, EE
    Ramos, F
    [J]. GENETIC PROGRAMMING, PROCEEDINGS, 2001, 2038 : 192 - 203