# muscle 5.1.0


{{< admonition success "Installed" true >}}
This software should be available with no extra configuration.
{{< /admonition >}}

## muscle-5.1.0

MUSCLE is widely-used software for making multiple alignments of biological
sequences. 

Version 5 of MUSCLE achieves highest scores on Balibase, Bralibase and Balifam
benchmark tests and scales to thousands of sequences on a commodity desktop
computer.

This version supports generating an ensemble of alternative alignments with
the same high accuracy obtained with default parameters. By comparing
downstream predictions from different alignments, such as trees, a biologist
can evaluation the robustness of conclusions against alignment errors.

### Manual and usage guide

Visit the [interactive manual](https://drive5.com/muscle5/manual/) for more
information.

-------------------------------------------------------------------------------

## Location and version

*NOTE:* use `muscle5` for this version, and `muscle` for past versions (e.g.
version 3).

```console
$ which muscle5
/local/cluster/bin/muscle5
$ muscle5 --version
muscle 5.1.linux64 [12f0e2]
Built Jan 13 2022 23:17:13
```

## help message

*NOTE:* currently, `muscle5 --help` provides a placeholder message. Use
`muscle5 -h` instead.

```console
$ muscle5 -h

muscle 5.1.linux64 [12f0e2]  264Gb RAM, 64 cores
Built Jan 13 2022 23:17:13
(C) Copyright 2004-2021 Robert C. Edgar.
https://drive5.com

Align FASTA input, write aligned FASTA (AFA) output:
    muscle -align input.fa -output aln.afa

Align large input using Super5 algorithm if -align is too expensive,
typically needed with more than a few hundred sequences:
    muscle -super5 input.fa -output aln.afa

Single replicate alignment:
    muscle -align input.fa -perm PERM -perturb SEED -output aln.afa
    muscle -super5 input.fa -perm PERM -perturb SEED -output aln.afa
        PERM is guide tree permutation none, abc, acb, bca (default none).
        SEED is perturbation seed 0, 1, 2... (default 0 = don't perturb).

Ensemble of replicate alignments, output in Ensemble FASTA (EFA) format,
EFA has one aligned FASTA for each replicate with header line "<PERM.SEED":
    muscle -align input.fa -stratified -output stratified_ensemble.efa
    muscle -align input.fa -diversified -output diversified_ensemble.afa

    -replicates N
        Number of replicates, defaults 4, 100, 100 for stratified,
          diversified, resampled. With -stratified there is one
          replicate per guide tree permutation, total is 4 x N.

Generate resampled ensemble from existing ensemble by sampling columns
with replacement:
    muscle -resample ensemble.efa -output resampled.efa

    -maxgapfract F
       Maximum fraction of gaps in a column (F=0..1, default 0.5).

    -minconf CC
       Minimum column confidence (CC=0..1, default 0.5).

If ensemble output filename has @, then one FASTA file is generated
for each replicate where @ is replaced by perm.s, otherwise all replicates
are written to one EFA file.

Calculate disperson of an ensemble:
    muscle -disperse ensemble.efa

Extract replicate with highest total CC (diversified input recommended):
    muscle -maxcc ensemble.efa -output maxcc.afa

Extract aligned FASTA files from EFA file:
    muscle -efa_explode ensemble.efa

Convert FASTA to EFA, input has one filename per line:
    muscle -fa2efa filenames.txt -output ensemble.efa

Update ensemble by adding two sequences of digits to each replicate, digits
are column confidence (CC) values, e.g. "73" means CC=0.73, "++" is CC=1.0:
    muscle -addconfseqs ensemble.efa -output ensemble_cc.efa

Calculate letter confidence (LC) values, -ref specifies the alignment to
compare against the ensemble (e.g. from -maxcc), output is in aligned
FASTA format with LC values 0, 1 ... 9 instead of letters:
    muscle -letterconf ensemble.efa -ref aln.afa -output letterconf.afa

    -html aln.html
        Alignment colored by LC in HTML format.

    -jalview aln.features
        Jalview feature file with LC values and colors.

More documentation at:
    https://drive5.com/muscle
```

software ref: <https://github.com/rcedgar/muscle>  
software ref: <https://drive5.com/muscle5/manual/>  
research ref: <https://doi.org/10.1038/s41467-022-34630-w>