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author | Petar Petrov <slackalaxy@gmail.com> | 2022-10-08 18:13:17 +0100 |
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committer | Willy Sudiarto Raharjo <willysr@slackbuilds.org> | 2022-10-15 10:47:28 +0700 |
commit | 1f43c07f3ce63f69512eda9410131fad0d266ea5 (patch) | |
tree | 5ed4ffaf5afc8a991dfa015786554799d23483ef /academic/muscle5/README | |
parent | d5622d171bc7a0a532386d1d82232d3c0b384275 (diff) | |
download | slackbuilds-1f43c07f3ce63f69512eda9410131fad0d266ea5.tar.xz |
academic/muscle5: Added (MUSCLE 5: Next-generation MUSCLE)
Signed-off-by: Willy Sudiarto Raharjo <willysr@slackbuilds.org>
Diffstat (limited to 'academic/muscle5/README')
-rw-r--r-- | academic/muscle5/README | 28 |
1 files changed, 28 insertions, 0 deletions
diff --git a/academic/muscle5/README b/academic/muscle5/README new file mode 100644 index 000000000000..bdea0f68e6d3 --- /dev/null +++ b/academic/muscle5/README @@ -0,0 +1,28 @@ +MUSCLE 5: Next-generation MUSCLE + +Muscle v5 is a major re-write of MUSCLE based on new algorithms. + +* Highest accuracy, scalable to thousands of sequences: +Compared to previous versions, Muscle v5 is much more accurate, is often +faster, and scales to much larger datasets. At the time of writing (late +2021), Muscle v5 has the highest scores on multiple alignment benchmarks +including Balibase, Bralibase, Prefab and Balifam. It can align tens of +thousands of sequences with high accuracy on a low-cost commodity +computer (say, an 8-core Intel CPU with 32 Gb RAM). On large datasets, +Muscle v5 is 20-30% more accurate than MAFFT and Clustal-Omega. + +* Alignment ensembles: +Muscle v5 can generate ensembles of high-accuracy alternative +alignments. All replicates have equal average accuracy on benchmark +test, including the MSA made with default parameters. By comparing +results of downstream analysis (trees, structure prediction...) on +different replicates, you can assess the effects of alignment errors on +your study. + +* Manual: +https://drive5.com/muscle5/manual/ + +* Reference (included in the package) +R.C. Edgar (2021) "MUSCLE v5 enables improved estimates of phylogenetic +tree confidence by ensemble bootstrapping" +https://www.biorxiv.org/content/10.1101/2021.06.20.449169v1.full.pdf |