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