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678b09e63c
Signed-off-by: David Spencer <idlemoor@slackbuilds.org>
47 lines
2 KiB
Text
47 lines
2 KiB
Text
MEGA: Molecular Evolutionary Genetics Analysis
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Sophisticated and user-friendly software suite for analyzing DNA and
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protein sequence data from species and populations. MEGA can be used
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with either a graphical user interface, useful for visual exploration
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of data and results or a command-line interface (useful for batch or
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scripted execution).
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MEGA X
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MEGA X is the GUI graphical user interface of MEGA and can be run in
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one of two modes. The first mode is the "Analyze mode" in which all
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GUI tools in MEGA are enabled and visual results explorers are
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available for tasks such as editing sequence alignments and viewing
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phylogenies. This is the mode that most MEGA users are familiar with.
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The second mode is the Prototype mode which is used solely for
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generating MEGA Analysis Options (.mao) files that specify analysis
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settings when using MEGA from a command shell (megacc). In the MEGA
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main form, click the Prototype button and then specify the type of
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input data that will be used for analysis.
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MEGA CC
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The command-line version of MEGA is 'megacc', that implements its core
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analysis functions and is useful for iterative and automated execution.
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The megacc command requires several options, including the path to a
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.mao file and paths to input data file(s) to be analyzed. For example:
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$ megacc -a myMaoFile.mao -d mySequenceAlignment.fas -o myOutput
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Note 1.
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Upon start, MEGA X will create an examples folder with the same name
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in your user's home. This is a good place to start if you are not
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familiar with MEGA.
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Note 2.
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This SlackBuild just repackages the binaries for Debian, provided from
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upstream. The RedHat packages, currently, do not have 32bit version.
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Note 3. IMPORTANT!
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SlackBuilds.org thanks Prof. Kumar for permission to use the direct
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download URL. MEGA is not free software. Your use is subject to the
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licence agreement at:
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http://www.megasoftware.net/show_eua
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Citing:
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MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger
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datasets (Kumar, Stecher, and Tamura 2015).
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