Gaussian Direct Coupling Analysis for protein contacts predicion
This is the code which accompanies the paper "Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and protein-interaction partners" by Carlo Baldassi, Marco Zamparo, Christoph Feinauer, Andrea Procaccini, Riccardo Zecchina, Martin Weigt and Andrea Pagnani, (2014) PLoS ONE 9(3): e92721. doi:10.1371/journal.pone.0092721
See also this Wikipedia article for a general overview of the Direct Coupling Analysis technique.
This code is released under the GPL version 3 (or later) license; see the
LICENSE.md file for details.
The code is written in Julia and requires julia version 0.6 or later; it provides a function which reads a multiple sequence alignment (in FASTA format) and returns a ranking of all pairs of residue positions in the aligned amino-acid sequences.
Install the package by giving these commands:
julia> using Pkg # only in Julia 0.7 or later julia> Pkg.clone("https://github.com/carlobaldassi/GaussDCA.jl")
All dependencies will be downloaded and installed automatically.
In Julia 0.6, the command
Pkg.upadte() will fetch the latest changes from
In Julia 0.7 and later, however, if you want to update you need to do so explicitly from the package directory using git. One way to do that is as such:
julia> using Pkg julia> cd(joinpath(Pkg.devdir(), "GaussDCA")) shell> git pull origin master
Note that the last line is given from the shell prompt, which you can access by pressing the ; key.
To load the code, just type
This software provides one main function,
gDCA(filname::String, ...). This
function takes the name of a (possibly gzipped) FASTA file, and returns a
predicted contact ranking, in the form of a Vector of triples, each triple
containing two indices
j) and a score. The indices
start counting from 1, and denote pair of residue positions in the given
alignment; pairs which are separated by less than a given number of residues
(by default 5) are filtered out. The triples are sorted by score in descending
order, such that predicted contacts should come up on top.
For convenience, a utility function is also provided,
which prints the result of
gDCA either in a file or to a stream, given as
first argument. If the first argument
output is omitted, the standard
terminal output will be used.
gDCA function takes some additional, optional keyword arguments:
pseudocount: the value of the pseudo-count parameter, between
1. the default is
0.8, which gives good results when the Frobenius norm score is used (see below); a good value for the Direct Information score is
theta: the value of the similarity threshold. By default it is
:auto, which means it will be automatically computed (this takes additional time); otherwise, a real value between
1can be given.
max_gap_fraction: maximum fraction of gap symbols in a sequence; sequences which exceed this threshold are discarded. The default value is
score: the scoring function to use. There are two possibilities,
:DIfor the Direct Information, and
:frobfor the Frobenius norm. The default is
:frob. (Note the leading colon: this argument is passed as a symbol).
min_separation: the minimum separation between residues in the output ranking. Must be >=
1. The default is
The code will be parallelized if more than one julia worker (as obtained by the
nworkers() function) is available. Multiple workers can be created either by
launching julia with the
-p option from the command line, or by using the
function (note that since julia 0.7 you will need to execute
using Distributed before
you can call
addprocs). See also the "Additional thechnical notes" section at the
end of this document.
Here is a basic usage example, assuming an alignment in FASTA format is found in the file "alignment.fasta.gz":
julia> using GaussDCA julia> FNR = gDCA("alignment.fasta.gz"); julia> printrank("results_FN.txt", FNR)
The above uses the Frobenius norm ranking with default parameters. This is how to get the Direct Information ranking instead:
julia> DIR = gDCA("alignment.fasta.gz", pseudocount = 0.2, score = :DI); julia> printrank("results_DI.txt", DIR)
Additional technical details
The parallelization can be forcefully disabled even in presence of extra
workers, by setting the environment variable
before loading the
When using workers, and using either OpenBLAS - which is the
default - or MKL as the BLAS backend, the default behaviour is to disable
threading in BLAS libraries. In this case, i.e. when many workers are found and
parallelization is not manually disabled, the
gDCA function overrides the
default julia behaviour and sets the number of threads to match the number of
workers (except when running the parallel portions of the code). It then resets
the number of threads to 1 when finished. The number of cores used in the
non-parallel portions of the code can be explicitly controlled by the user via
OMP_NUM_THREADS environment variable.