mirror of
https://github.com/Ponce/slackbuilds
synced 2024-11-14 21:56:41 +01:00
53 lines
2.6 KiB
Text
53 lines
2.6 KiB
Text
HyPhy: Hypothesis testing using Phylogenies
|
|
|
|
HyPhy is an open-source software package for the analysis of genetic
|
|
sequences (in particular the inference of natural selection) using
|
|
techniques in phylogenetics, molecular evolution, and machine learning.
|
|
It features a rich scripting language for limitless customization of
|
|
analyses. Additionally, HyPhy features support for parallel computing
|
|
environments (via message passing interface).
|
|
|
|
HyPhy was designed to allow the specification and fitting of a broad
|
|
class of continuous-time discrete-space Markov models of sequence
|
|
evolution. To implement these models, HyPhy provides its own scripting
|
|
language - HBL, or HyPhy Batch Language, which can be used to develop
|
|
custom analyses or modify existing ones. Importantly, it is not
|
|
necessary to learn (or even be aware of) HBL in order to use HyPhy, as
|
|
most common models and analyses have been implemented for user
|
|
convenience. Once a model is defined, it can be fitted to data (using a
|
|
fixed topology tree), its parameters can be constrained in user-defined
|
|
ways to test various hypotheses (e.g. is rate1 > rate2), and simulate
|
|
data from. HyPhy primarily implements maximum likelihood methods, but
|
|
it can also be used to perform some forms of Bayesian inference (e.g.
|
|
FUBAR), fit Bayesian graphical models to data, run genetic algorithms to
|
|
perform complex model selection.
|
|
|
|
Features
|
|
- Support for arbitrary sequence data, including nucleotide, amino-acid,
|
|
codon, binary, count (microsattelite) data, including multiple
|
|
partitions mixing differen data types.
|
|
- Complex models of rate variation, including site-to-site, branch-to-
|
|
branch, hidden markov model (autocorrelated rates), between/within
|
|
partitions, and co-varion type models.
|
|
- Fast numerical fitting routines, supporting parallel and distributed
|
|
execution.
|
|
- A broad collection of pre-defined evolutionary models.
|
|
- The ability to specify flexible constraints on model parameters and
|
|
estimate confidence intervals on MLEs.
|
|
- Ancestral sequence reconstruction and sampling.
|
|
- Simulate data from any model that can be defined and fitted in the
|
|
language.
|
|
- Apply unique (for this domain) machine learning methods to discover
|
|
patterns in the data, e.g. genetic algorithms, stochastic context free
|
|
grammars, Bayesian graphical models.
|
|
- Script analyses completely in HBL including flow control, I/O,
|
|
parallelization, etc.
|
|
|
|
Registration
|
|
you are highly advised to fill the registration form found at:
|
|
https://veg.github.io/hyphy-site/register/
|
|
|
|
Citing
|
|
Sergei L. Kosakovsky Pond, Simon D. W. Frost and Spencer V. Muse (2005)
|
|
HyPhy: hypothesis testing using phylogenies.
|
|
Bioinformatics 21(5): 676-679
|