Skip to main content

Program for estimating amino acid replacement rates

Project description

modelestimator --- Infer sequence evolution rate matrices from a MSA

Example usage

    modelestimator -t 0.001 file1.fa file2.fa file3.fa

Infer a rate matrix (written to stdout) from three alignment files in Fasta format. The output is PAML format by default, and therefore applicable in a number of available phylogenetic softwares.

    modelestimator -b 200 file.fa

Try the experimental bootstrapping feature (200 replicates) on a Fasta multialignment.

Syntax

modelestimator <options> infiles

<format> should be either FASTA, STOCKHOLM or PHYLIP format.

Output is a rate matrix and residue distribution vector.

Options

  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -a {iqtree,matlab,mrbayes,octave,paml,phyml}, --application {iqtree,matlab,mrbayes,octave,paml,phyml}
                        Choose output format to suit the application you want
                        to use for inference. The 'iqtree', 'paml' and 'phyml'
                        options are identical. The 'matlab' and 'octave'
                        optins are for import into MatLab-compatible programs
                        and are presenting the actual Q matrix rather than the
                        R matrix used by PAML/PhyML, etc. Default: paml
  -f {fasta,clustal,nexus,phylip,stockholm}, --format {fasta,clustal,nexus,phylip,stockholm}
                        Specify sequence format of input files. Default: fasta
  -t T, --threshold T   Stop when consecutive iterations do not change by more
                        than T. Default: 0.001
  -b N, --bootstrap N   Estimate the rate matrix using bootstrapping by
                        computing N resampled replicates of the input
                        multialignment. For each replicate, a rate matrix is
                        computed. The mean matrix the elementwise standard
                        deviation is returned. Only one infile should be given
                        in this mode.
  -B N, --bootstrapped_quality N
                        Estimate the quality of the rate matrix estimate using
                        a bootstrap procedure. The multialignment is resampled
                        N times and a Q matrix is computed for each replicate.
                        Then the difference (matrix norm) between rate matrix
                        estimated without resampling and each bootstrapped Q
                        is computed and the mean difference is returned. Only
                        one infile should be given in this mode. Returns
                        bootstrap norm.

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

modelestimator-v2-2.1.3.tar.gz (14.4 kB view details)

Uploaded Source

Built Distribution

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

modelestimator_v2-2.1.3-py3-none-any.whl (23.4 kB view details)

Uploaded Python 3

File details

Details for the file modelestimator-v2-2.1.3.tar.gz.

File metadata

  • Download URL: modelestimator-v2-2.1.3.tar.gz
  • Upload date:
  • Size: 14.4 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 modelestimator-v2-2.1.3.tar.gz
Algorithm Hash digest
SHA256 fe28025dd81e29b57000c5aaf5b79eeed205be27f5badfd1c95d5b88fecceb6f
MD5 420243bc30e271007629065931f79543
BLAKE2b-256 3dcf69322b3cb53e7efef3795d22c7fe8b235c368f955a9d43fce0e069a6ad2a

See more details on using hashes here.

File details

Details for the file modelestimator_v2-2.1.3-py3-none-any.whl.

File metadata

  • Download URL: modelestimator_v2-2.1.3-py3-none-any.whl
  • Upload date:
  • Size: 23.4 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 modelestimator_v2-2.1.3-py3-none-any.whl
Algorithm Hash digest
SHA256 c5700150ec90afa4ba0db4f52ee049fdaa2a62c09d59ecdbd7438c111c06b560
MD5 bcd23e94bb987a146a82ee9e3d8613dd
BLAKE2b-256 04b6154b6ed2273f11272be298a3f0aa51bcae9061ff9d1b9292abe26838293c

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