Performance Analysis of Multiple Sequence Alignment Tools

被引:1
作者
Reddy, Bharath [1 ]
Fields, Richard [2 ]
机构
[1] Schneider Elect Automat R&D, Foxboro, MA 02035 USA
[2] Schneider Elect Automat R&D, Lake Forest, CA USA
来源
PROCEEDINGS OF THE 2024 ACM SOUTHEAST CONFERENCE, ACMSE 2024 | 2024年
关键词
Sequence Alignment; phylogenetic; Computational biology; Bioinformatics; IMPROVEMENT; ACCURACY; SEARCH; ALGORITHM; DATABASE; ACID; DNA;
D O I
10.1145/3603287.3651216
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multiple Sequence Alignment (MSA) is a process of aligning two or more sequences with the aim of finding relation between the sequences or organisms. The sequences could have mutations in ways of insertion, deletion or rearrangement of the portion of the sequences for reasons unknown over time. The sequences used for alignment could be DNA or RNA or Genes. Today, MSA is an important procedure used as an intial step in molecular biology, computational biology and bioinformatics. The outcome in these fields are, phylogenetic tree construction, protein secondary and tertiary structure analysis, and protein function prediction analysis. This paper provides a comprehensive comparative analysis of different multiple sequence alignment tools which are available today. The paper would first focus on different kinds of sequence alignment before moving to multiple sequence alignment, which then talks about the recent development in the algorithms and their techniques. The later sections would provide some of the benchmarks and data parameters used in the comparative analysis. The subsequent section would talk about the performance and the reasons for various algorithms performance and later conclude in which direction multiple sequence alignment would probably go and what we think would be ideal outcome for biologists going forward.
引用
收藏
页码:167 / 174
页数:8
相关论文
共 50 条
  • [11] Progressive Alignment Method Using Genetic Algorithm for Multiple Sequence Alignment
    Naznin, Farhana
    Sarker, Ruhul
    Essam, Daryl
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2012, 16 (05) : 615 - 631
  • [12] The Impact of Multiple Protein Sequence Alignment on Phylogenetic Estimation
    Wang, Li-San
    Leebens-Mack, Jim
    Wall, P. Kerr
    Beckmann, Kevin
    dePamphilis, Claude W.
    Warnow, Tandy
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2011, 8 (04) : 1108 - 1119
  • [13] DECIPHER: harnessing local sequence context to improve protein multiple sequence alignment
    Wright, Erik S.
    BMC BIOINFORMATICS, 2015, 16
  • [14] MULTIPLE SEQUENCE ALIGNMENT A Quick Tour
    Chintapalli, Radha
    Kumar, Amit
    Parayitam, Laxminarayana
    2013 15TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING TECHNOLOGIES (ICACT), 2013,
  • [15] Multiple Sequence Alignment with Genetic Algorithms
    Botta, Marco
    Negro, Guido
    COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, 2010, 6160 : 206 - 214
  • [16] MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability
    Katoh, Kazutaka
    Standley, Daron M.
    MOLECULAR BIOLOGY AND EVOLUTION, 2013, 30 (04) : 772 - 780
  • [17] Heuristics for multiobjective multiple sequence alignment
    Abbasi, Maryam
    Paquete, Luis
    Pereira, Francisco B.
    BIOMEDICAL ENGINEERING ONLINE, 2016, 15
  • [18] Sequence Alignment as Hypothesis Testing
    Meng, Lu
    Sun, Fengzhu
    Zhang, Xuegong
    Waterman, Michael S.
    JOURNAL OF COMPUTATIONAL BIOLOGY, 2011, 18 (05) : 677 - 691
  • [19] Alignment of multiple protein structures based on sequence and structure features
    Madhusudhan, M. S.
    Webb, Benjamin M.
    Marti-Renom, Marc A.
    Eswar, Narayanan
    Sali, Andrej
    PROTEIN ENGINEERING DESIGN & SELECTION, 2009, 22 (09) : 569 - 574
  • [20] Issues in bioinformatics benchmarking: the case study of multiple sequence alignment
    Aniba, Mohamed Radhouene
    Poch, Olivier
    Thompson, Julie D.
    NUCLEIC ACIDS RESEARCH, 2010, 38 (21) : 7353 - 7363