Skip to main content

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

The output is a list of models, in order of fit to data, and their modelmatcher score. The base model (such as JTT, WAG, LG, etc) is predicted, as well as whether one should adapt to the alignments amino acid composition (i.e., JTT+F, WAG+F, etc).

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

modelmatcher-1.1.3.tar.gz (33.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

modelmatcher-1.1.3-py3-none-any.whl (32.8 kB view details)

Uploaded Python 3

File details

Details for the file modelmatcher-1.1.3.tar.gz.

File metadata

  • Download URL: modelmatcher-1.1.3.tar.gz
  • Upload date:
  • Size: 33.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/40.0.0 requests-toolbelt/0.8.0 tqdm/4.24.0 CPython/3.6.3

File hashes

Hashes for modelmatcher-1.1.3.tar.gz
Algorithm Hash digest
SHA256 fb49a95b122d9e2d2192e7d5598314f41a9e3559a576fca518cda3232ceb3058
MD5 d9f3cec84e887970d7618f162913a606
BLAKE2b-256 c64e83bbfe310034b8c3f2ecc0ec5d07f5407e1cf30d5fb3e41e1521bff8d4e0

See more details on using hashes here.

File details

Details for the file modelmatcher-1.1.3-py3-none-any.whl.

File metadata

  • Download URL: modelmatcher-1.1.3-py3-none-any.whl
  • Upload date:
  • Size: 32.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/40.0.0 requests-toolbelt/0.8.0 tqdm/4.24.0 CPython/3.6.3

File hashes

Hashes for modelmatcher-1.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 086803e65041258390bf3615f3595796257531d6a5d3832849c979929677641d
MD5 71ef3b400bdfb2480b6c5839ccdf83ef
BLAKE2b-256 8719eb75ae88231c73ec1192627507751b5d820ed4f95a3cab20abe96fe02e9d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page