MEGA

MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences. The Molecular Evolutionary Genetics Analysis (MEGA) software is a desktop application designed for comparative analysis of homologous gene sequences either from multigene families or from different species with a special emphasis on inferring evolutionary relationships and patterns of DNA and protein evolution. In addition to the tools for statistical analysis of data, MEGA provides many convenient facilities for the assembly of sequence data sets from files or web-based repositories, and it includes tools for visual presentation of the results obtained in the form of interactive phylogenetic trees and evolutionary distance matrices. Here we discuss the motivation, design principles and priorities that have shaped the development of MEGA. We also discuss how MEGA might evolve in the future to assist researchers in their growing need to analyze large data set using new computational methods.


References in zbMATH (referenced in 34 articles )

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  1. Collienne, Lena; Gavryushkin, Alex: Computing nearest neighbour interchange distances between ranked phylogenetic trees (2021)
  2. Ostash, Bohdan; Anisimova, Maria: Visualizing codon usage within and across genomes: concepts and tools (2020)
  3. Rout, Subhashree; Mahapatra, Rajani Kanta: \textitInsilico analysis of \textitplasmodiumfalciparum CDPK5 protein through molecular modeling, docking and dynamics (2019)
  4. Warnow, Tandy (ed.): Bioinformatics and phylogenetics. Seminal contributions of Bernard Moret (2019)
  5. Wu, Chengyuan; Ren, Shiquan; Wu, Jie; Xia, Kelin: Magnus representation of genome sequences (2019)
  6. Liu, Yong; Chen, Jingan; Qian, Jieying; Lin, Hao; Sun, Ning; Huang, Zunnan: Evolutionary analysis and structural characterization of \textitAquilariasinensis sesquiterpene synthase in agarwood formation: a computational study (2018)
  7. Davidson, Ruth; Rusinko, Joseph; Vernon, Zoe; Xi, Jing: Modeling the distribution of distance data in Euclidean space (2017)
  8. Drellich, Elizabeth; Gainer-Dewar, Andrew; Harrington, Heather A.; He, Qijun; Heitsch, Christine; Poznanović, Svetlana: Geometric combinatorics and computational molecular biology: branching polytopes for RNA sequences (2017)
  9. Erciyes, K.: Distributed and sequential algorithms for bioinformatics (2015)
  10. RoyChoudhury, Arindam; Willis, Amy; Bunge, John: Consistency of a phylogenetic tree maximum likelihood estimator (2015)
  11. Podder, Avijit; Jatana, Nidhi; Latha, N.: Human dopamine receptors interaction network (DRIN): a systems biology perspective on topology, stability and functionality of the network (2014)
  12. Yin, Changchuan; Chen, Ying; Yau, Stephen S.-T.: A measure of DNA sequence similarity by Fourier transform with applications on hierarchical clustering (2014)
  13. Yu, Chenglong; Lucy He, Rong; Yau, Stephen S.-T.: Viral genome phylogeny based on Lempel-Ziv complexity and Hausdorff distance (2014)
  14. de Farias, Sávio Torres: Suggested phylogeny of tRNAs based on the construction of ancestral sequences (2013)
  15. Yang, Xiwu; Wang, Tianming: Linear regression model of short (k)-word: a similarity distance suitable for biological sequences with various lengths (2013)
  16. Namboodiri, Saritha; Giuliani, Alessandro; Nair, Achuthsankar S.; Dhar, Pawan K.: Looking for a sequence based allostery definition: a statistical journey at different resolution scales (2012)
  17. Song, Lei; Pan, Yuting; Chen, Sihong; Zhang, Xuehong: Structural characteristics of genomic islands associated with GMP synthases as integration hotspot among sequenced microbial genomes (2012)
  18. Wang, Hsiuying; Hung, Shan-Lin: Phylogenetic tree selection by the adjusted (k)-means approach (2012)
  19. Blazewicz, Jacek; Formanowicz, Piotr; Kedziora, Pawel; Marciniak, Pawel; Taront, Przemyslaw: Adaptive memory programming: local search parallel algorithms for phylogenetic tree construction (2011)
  20. Dai, Qi; Liu, Xiaoqing; Yao, Yuhua; Zhao, Fukun: Numerical characteristics of word frequencies and their application to dissimilarity measure for sequence comparison (2011)

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Further publications can be found at: http://www.megasoftware.net/citations