A complete pipeline for fitting and testing Fixed Local Clock (FLC) molecular clock models for episodic evolution.
Project description
Episodic 
A complete pipeline for fitting and testing Fixed Local Clock (FLC) molecular clock models for episodic evolution.
About
Episodic is a tool for fitting and testing Fixed Local Clock (FLC) molecular clock models for episodic evolution. The package is built on top of SNK, and provides a complete pipeline for fitting and testing models of episodic evolution using BEAST.
Episodic implements the ideas of Tay et al. (2022 and 2023) and detects episodic evolution through Bayesian inference of molecular clock models.
Given a multiple sequence alignment and a list of groups to test for episodic evolution, episodic will:
- Configure BEAST analyses for strict, relaxed (UCGD) and fixed local clock models.
- Configure marginal likelihood analyses for each clock model.
- Run all the BEAST and marginal likelihood analyses.
- Plot and summarise the results.
- Compute and plot Bayes factors for the marginal likelihood analyses.
- Produce maximum clade credibility (MCC) trees for each clock model.
- Compute bayes factor on effect size for the FLC models (foreground vs background).
- Run rank and quantile tests on the relaxed clock models.
- Handel the execution of the pipeline on a HPC cluster via snakemake profiles.
- Produce a report of the results (TBD).
Features
- Complete pipeline -
episodicprovides a complete pipeline for fitting and testing FLC models of episodic evolution. - Flexible -
episodicis built on top of SNK, and provides a flexible framework for fitting and testing FLC models of episodic evolution. - Easy to use -
episodicis easy to use, and provides a simple interface for fitting and testing FLC models of episodic evolution. - robust -
episodicis robust, and provides a robust framework for fitting and testing FLC models of episodic evolution.
Installation
pip install episodic
CLI
Multiple alignment partitions can be supplied by repeating --alignment.
episodic run \
--alignment partition1.fasta \
--alignment partition2.fasta \
--group BA.2.86
Outputs
Episodic produces BEAST logs, trees, summary tables and plots under output.dir (optionally timestamped when output.dated: true).
For the complete output file reference (all targets, side-effect files, and optional branches), see:
Main output categories include:
-
BEAST log files -
episodicwill produce a BEAST log file for each clock model. These files can be analysed with Beastiary. -
BEAST trees -
episodicwill produce a BEAST tree file for each clock model. -
MCC trees -
episodicwill produce a MCC tree for each clock model. -
MCC tree plots -
episodicwill produce a MCC tree plot for each clock model. -
Marginal likelihoods -
episodicwill produce a marginal likelihood plot for each clock model. -
Bayes factors on effect size -
episodicwill calculate a Bayes factors on effect size for each local clock model.Rate Column p_p p_odds pos_p pos_odds bf BA.2.86.rate 0.5034996111543162 1.0140971134481986 1.0 inf inf -
Rank and quantile tests -
episodicwill produce a rank and quantile test plot for each relaxed clock model. -
Partition local-rate posterior plots (FLC clocks) - overlaid background vs local posterior densities per partition.
-
Clock rate plots -
episodicwill produce a rate plot for each clock model.
DAG
License
episodic is distributed under the terms of the MIT license.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file episodic-0.7.2.tar.gz.
File metadata
- Download URL: episodic-0.7.2.tar.gz
- Upload date:
- Size: 42.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: python-httpx/0.27.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
5076bf459120a8da56a3c633510b68b83a3488f1d5f926096991a2dc52086e21
|
|
| MD5 |
008dc9ad9cc6bb75e382a0775e15315f
|
|
| BLAKE2b-256 |
cdc4ee78178451263044c5619c62141b3bd6074d4fb357bfed61981cace21985
|
File details
Details for the file episodic-0.7.2-py3-none-any.whl.
File metadata
- Download URL: episodic-0.7.2-py3-none-any.whl
- Upload date:
- Size: 50.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: python-httpx/0.27.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
66c077e497a8a26e2126011e5ee7d3aefd28207d2e7043ef7c5d09ccc4a25960
|
|
| MD5 |
e8aba14b74cb84979a2bf7e5bdfc2c41
|
|
| BLAKE2b-256 |
e65d8250f3923285ab062f61b9441acefc2d7087c2f0cf2a9d01bbbf18abdf42
|