godon

Godon is codon models software written in Go.

Godon development was supported Swiss National Science Foundation (grant numbers CR32I3_143768, IZLRZ3_163872).

What is special about Godon

• Godon supports rate variation (see the manuscript). There are three models which support rate variation: branch-site (model BSG), M8 (model M8) and M0 (model M0G). You need to specify the number of discrete categories. Otherwise, there will be no rate variation in the model. Use --ncat-site-rate or --ncat-codon-rate for site rate variation and codon rate variation respectively.

• Godon supports state aggregation (option --aggregate). See the paper for the details. For the paper, we used v0.5 (39bf774). Since then likelihood computations code were substantially changed.

• A heuristic to avoid LRT statistics overestimation, which often causes false positives in PAML. Also corrects for LRT underestimation. Use godon test to enable.

• A heuristic for fast branch-length estimation via M0 (--m0-tree).

• Multiple optimizers available: L-BFGS-B, downhill simplex, simulated annealing, SQP, and others via NLopt.

• Markov chain Monte Carlo support (Metropolis-Hastings algorithm).

• Export to machine-readable JSON format.

• Multithreading support (unlike PAML).

• Starting point specification (only some parameters in PAML) and randomization (disabled in PAML).

• Testing multiple branches in one run for the branch-site model.

• Wide range of models: M0, M1a, M2a, M7, M8, and branch-site.

• Support for various genetic codes.

• Checkpoints: in case your long computation was interrupted it is possible to continue. You need to specify checkpoint file to use this (--checkpoint). Warning: this might affect reproducibility when it comes to random number generator.

Support

You can ask questions at the bioinformatics stackexchange site. Do not forget to use the [godon] tag. Use issues to report bugs.

Installation

The software was tested on GNU/Linux and Mac OS X.

You can fetch the latest statically compiled binary for GNU/Linux from the downloads section; do not forget to make it executable prior to running (chmod +x godon-master-linux-gnu-x86_64).

Compiling from source

Requirements:

• Go (preferably v1.7 or later)
• Git
• C and Fortran compilers
• NLopt
• BLAS (e.g. OpenBLAS)
• Gonum BLAS C-bindings

Once you have got all of that you can run:

$ bin/install.sh

Ubuntu 16.04 installation

1. Install Go v1.7 or later. You can start by installing Go v1.6 and then updating using godeb.

2. Install dependencies: sudo apt-get install git libnlopt-dev libopenblas-dev build-essentials gfortran

3. Install Gonum BLAS: CGO_LDFLAGS="-lopenblas" go install github.com/gonum/blas/cgo

4. (Optional) If your Go is older than v1.7 install go-lbfsg.

5. Install godon:

   bin/install.sh
   

Mac OS X installation using Homebrew

1. Install Homebrew.
2. Install dependentices: brew install go gcc nlopt (may take more than an hour).
3. If you don't have git, install it as well: brew install git.
4. Install godon: curl -L https://bitbucket.org/Davydov/godon/raw/master/bin/install.sh | CC=gcc-7 bash. You need to use gcc from Homebrew, in this case gcc-7.
5. (Optional) Add the binary directory of Go to the PATH variable. E.g., put export PATH=$PATH:$HOME/go/bin into your ~/.bash_profile.

Mac OS X installation (old)

1. Make sure you have C compiler, build tools and gfortran.

2. Install Go (1.7 or later).

3. Install NLopt.

4. Get Godon source code with go get -d bitbucket.org/Davydov/godon/godon.

5. Install godon. Depending on the installation, you may need to specify paths to nlopt library and include files and to the fortran library libgfortran (on the test system it was /usr/local/Cellar/gcc/6.2.0/lib/gcc/6). Run:

   CGO_CFLAGS="-I/path/to/nlopt/include" CGO_LDFLAGS="-L/path/to/libgfortran -L/path/to/nlopt/lib" $GOPATH/src/bitbucket.org/Davydov/godon/bin/install.sh
   

Running

Don't forget to check out the tutorial.

You can find sample datasets in godon/cmodel/testdata.

You can tell Godon to run a pair of models (M8 vs. M8a or branch-site H1 vs. H0). In this case, if the foreground branch for the branch-site model is not labeled with #1, Godon will test all the branches. To force this behavior even in the presence of #1 labeled branch, use --all-branches. You can exclude terminal branches with --no-leaves. You can use branch lengths estimated with M0 using --m0-tree.

#!bash
$ godon test BS --m0-tree --all-branches EMGT00050000008747.Drosophila.002.fst EMGT00050000008747.Drosophila.002.nwk

Perform likelihood maximization using L-BFGS-B optimizer for the Branch-Site model without optimizing the branch lengths (use only a single CPU).

#!bash
$ godon -p 1 -n BS EMGT00050000000025.Drosophila.001.fst EMGT00050000000025.Drosophila.001.nwk

Run MCMC using M0 model with the downhill simplex optimization.

#!bash
$ godon -m mh M0 EMGT00050000000025.Drosophila.001.fst EMGT00050000000025.Drosophila.001.nwk

repository contents

• bin installation script
• bio reads fasta and translates genetic code
• cmodel codon models
• codon working with codon and transition matrices
• godon is MCMC sampler/maximum likelihood for M0 and branchsite model
• misc various utilities
• optimize is the MCMC & downhill simplex and other algorithms implementation
• dist functions related to discrete distributions, initially ported from PAML
• tree is tree manipulation library

codon

• codon_frequency.go — F0, F3X4
• codon_sequences.go — codon alignment class
• ematrix.go — matrix class which remembers its eigen decomposition
• matrix.go — transition matrix routines

cmodel

• aggregation.go — codon aggregation code
• branch_site.go — branch site model
• M0.go — M0 model
• model.go — tree + alignment model base class
• tools.go — misc helper functions

cmodel tests

• likelihood_test.go — likelihood test (compare with codeml)
• mcmc_test.go — MCMC benchmark
• mcmcpar_test.go — test that likelihood is consistent during chain evaluation

optimize

• adaptive.go — adaptive parameter class
• lbfgsb.go — L-BFGS-B optimizer
• mh.go — metropolis hastings & simulated annealing implementations
• nlopt_callback.go — NLopt callback wrapper
• nlopt.go — NLopt wrapper
• optimizer.go — Optimizer and Optimizable intefaces
• parameter.go — float64 parameter class
• prior.go — prior functions
• proposal.go — proposal functions
• simplex.go — simplex method
• utils.go — helper functions

misc

• brexp exports branch lengths and node labels in various formats
• brmatch matches branch labels between two trees
• norm is a sampler for multiple normal distributions model