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diff --git a/academic/hyphy/README b/academic/hyphy/README new file mode 100644 index 0000000000..720613c4e4 --- /dev/null +++ b/academic/hyphy/README @@ -0,0 +1,53 @@ +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 |