Heinz Wiesinger 63daf9f79a All: Support $PRINT_PACKAGE_NAME env var 3 years ago
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README ec11283d0b academic/muscle: Update script and added manual page. 4 years ago
References 9b575230bb academic/muscle: Added (A multiple sequence alignments program) 13 years ago
muscle.1 ec11283d0b academic/muscle: Update script and added manual page. 4 years ago
muscle.SlackBuild 63daf9f79a All: Support $PRINT_PACKAGE_NAME env var 3 years ago
muscle.info ec11283d0b academic/muscle: Update script and added manual page. 4 years ago
slack-desc ec11283d0b academic/muscle: Update script and added manual page. 4 years ago

README

MUSCLE is a program for creating multiple alignments of amino acid or
nucleotide sequences. A range of options is provided that give you the
choice of optimizing accuracy, speed, or some compromise between the
two.

Fast, accurate and easy to use
MUSCLE is one of the best-performing multiple alignment programs
according to published benchmark tests, with accuracy and speed that
are consistently better than CLUSTALW. MUSCLE can align hundreds of
sequences in seconds. Most users learn everything they need to know
about MUSCLE in a few minutes—only a handful of command-line options
are needed to perform common alignment tasks.

NOTE about the "-stable" option no longer being supported:
The "-stable" option had a bug, which sometimes resulted in incorrect
alignments to be produced. The author has created a python script to
be used as a workaround. The SlackBuild includes it and its usage is:

python muscle-stable.py input.fasta aligned.fasta > stable.fasta

Papers
There are two papers. The first (NAR) introduced the algorithm, and is
the primary citation if you use the program. The second (in BMC Bio-
informatics) gives more technical details, including descriptions of
non-default options.

Edgar, R.C. (2004) MUSCLE: multiple sequence alignment with high
accuracy and high throughput. Nucleic Acids Res. 32(5):1792-1797

Edgar, R.C. (2004) MUSCLE: a multiple sequence alignment method with
reduced time and space complexity BMC Bioinformatics, (5) 113