Rapid identification of sequence evolution models
Project description
modelmatcher: Rapid identification of evolutionary models
This tool reads multiple sequence alignments and determines a suitable sequence evolution model for your phylogenetic analysis.
Usage
Example usage:
$ modelmatcher inputfile.fasta
The input file is a multiple sequence alignmnent in one of these common formats:
- FASTA
- Clustal
- NEXUS
- PHYLIP
- STOCKHOLM
Options
Optional options:
-h, --help show this help message and exit
-f {guess,fasta,clustal,nexus,phylip,stockholm}, --format {guess,fasta,clustal,nexus,phylip,stockholm}
Specify what sequence type to assume. Be specific if
the file is not recognized automatically. When reading
from stdin, the format is always guessed to be FASTA.
Default: guess
-m filename, --model filename
Add the model given in the file to the comparisons.
-nf, --no-F-testing Do not try +F models, i.e., do not test with amino
acid frequencies estimated from the MSA.
-of {tabular,json,iqtree,raxml,phyml,mrbayes}, --output_format {tabular,json,iqtree,raxml,phyml,mrbayes}
Choose output format. Tabular format is default. JSON
is for convenient later parsing, with some additional
meta-data added. For one-line output convenient for
immediate use by inference tools, consider raxml and
similar choices. Note that the PhyML and MrBayes
options are restricted to their implemented models.
Although PhyML supports the +F models (using the "-f
e" option), this is not reflected in the output from
"modelmatcher -of phyml ..." at this time.
--verbose Output progress information
Input formats
Input format is detected automatically from the following list, but can also be requested specifically.
- FASTA
- Phylip
- Nexus
- Clustal
- Stockholm
Output
Output is given as a simple text table, or in JSON format for easy parsing by other scripts, ranking possible models in preference order. For example, the command above may yield a table looking like:
WAG 7.972
VT 8.238
BLOSUM62 8.478
JTT 8.864
JTT-DCMUT 8.917
LG 9.984
DCMUT 10.467
Dayhoff 10.495
FLU 11.211
HIVb 12.853
RtREV 14.048
cpREV 14.186
HIVw 17.338
MtZoa 18.476
MtMAM 21.453
mtArt 21.741
MtREV 22.059
Each model is given with its modelmatcher score.
Alternatively, the same analysis can look like:
$ modelmatcher --json inputfile.fasta
{"n_observations": 863692, "infile": "inputfile.fasta", "n_seqs": 66, "model_ranking": [["WAG", 7.972410383355675], ["VT", 8.238362164888876], ["BLOSUM62", 8.478000205922985], ["JTT", 8.863578165338444], ["JTT-DCMUT", 8.917496451351846], ["LG", 9.983874357603963], ["DCMUT", 10.466872509785343], ["Dayhoff", 10.49522598111376], ["FLU", 11.21137482805874], ["HIVb", 12.852877789672046], ["RtREV", 14.047539707772572], ["cpREV", 14.18648653904322], ["HIVw", 17.338193829402], ["MtZoa", 18.475515151949153], ["MtMAM", 21.452528293860837], ["mtArt", 21.740741039472418], ["MtREV", 22.058622800684176]]}
Install
Recommended installation is:
pip install --upgrade pip
pip install modelmatcher
Project details
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