MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis
By Sudhir Kumar, Glen Stecher, Daniel Peterson, Koichiro Tamura. Bioinform., 2012
349 citations recorded by the source corpus.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/96c403bfcd18938055effd7e488fd08ff0f5ced5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- Glen Stecher
- Koichiro Tamura
- Sudhir Kumar
- Daniel Peterson
- Alan J. Filipski
- Michael Li
- Masatoshi Nei
- Paul D. N. Hebert
In Molecular Biology
- Sudhir Kumar
- Koichiro Tamura
- Glen Stecher
- Michael Li
- Daniel Peterson
- Christina Knyaz
- Alan J. Filipski
- Asha J. Dissanayake
Citations in the corpus
- MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. (2016, methodology): “In order to configure analyses in M EGA 7-CC, we have chosen to continue requiring an analysis options file (called .mao file) that specifies all the input parameters to the command-line driven M EGA -CC application; see figure 1 in Kumar et al. (2012).”
- MEGA11: Molecular Evolutionary Genetics Analysis Version 11 (2021, methodology): “MEGA-CC was released for use as a command-line program to address the growing need for batch processing of many data sets and integration into analysis workflows (Kumar et al. 2012;”
- MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. (2013, methodology): “MEGA is currently distributed in two editions: a graphical user interface (GUI) edition with visual tools for exploration of data and analysis results (Tamura et al. 2011) and a command line edition (MEGA-CC), which is optimized for iterative and integrated pipeline analyses (Kumar et al. 2012).”
- MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. (2018, methodology): “M EGA X now generates a configuration file specifying analysis options for user-desired analysis in M EGA CC, which many researchers use for high throughput and iterative analysis in Windows, Linux and macOS environments (Kumar et al. 2012).”
- Molecular Evolutionary Genetics Analysis (MEGA) for macOS. (2020, methodology): “For users that wish to run MEGA through a command-line interface (MEGA-CC), MEGA for macOS generates a configuration file specifying analysis options for user-desired analysis by using the computing core of MEGA that is useful for high throughput and iterative analysis (Kumar et al. 2012).”