{"id":25612526,"url":"https://github.com/carlobaldassi/gaussdca.jl","last_synced_at":"2025-04-13T17:51:00.070Z","repository":{"id":12330533,"uuid":"14969350","full_name":"carlobaldassi/GaussDCA.jl","owner":"carlobaldassi","description":"Multivariate Gaussian Direct Coupling Analysis for residue contact prediction in protein families - Julia module","archived":false,"fork":false,"pushed_at":"2022-01-27T10:25:03.000Z","size":675,"stargazers_count":22,"open_issues_count":1,"forks_count":12,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-27T08:45:20.610Z","etag":null,"topics":["bioinformatics","direct-coupling-analysis","julia","predicted-contacts","protein-contact-prediction"],"latest_commit_sha":null,"homepage":null,"language":"Julia","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":"veracrypt/VeraCrypt","license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/carlobaldassi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"COPYING","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2013-12-06T00:06:02.000Z","updated_at":"2024-10-19T15:47:10.000Z","dependencies_parsed_at":"2022-09-12T13:42:46.589Z","dependency_job_id":null,"html_url":"https://github.com/carlobaldassi/GaussDCA.jl","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carlobaldassi%2FGaussDCA.jl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carlobaldassi%2FGaussDCA.jl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carlobaldassi%2FGaussDCA.jl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/carlobaldassi%2FGaussDCA.jl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/carlobaldassi","download_url":"https://codeload.github.com/carlobaldassi/GaussDCA.jl/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248758155,"owners_count":21156957,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","direct-coupling-analysis","julia","predicted-contacts","protein-contact-prediction"],"created_at":"2025-02-22T00:28:18.929Z","updated_at":"2025-04-13T17:51:00.020Z","avatar_url":"https://github.com/carlobaldassi.png","language":"Julia","funding_links":[],"categories":[],"sub_categories":[],"readme":"Gaussian Direct Coupling Analysis for protein contacts predicion\n================================================================\n\n[![CI][CI-img]][CI-url] [![CODECOV][codecov-img]][codecov-url]\n\nOverview\n--------\n\nThis is the code which accompanies the paper [\"Fast and accurate multivariate\nGaussian modeling of protein families: Predicting residue contacts and\nprotein-interaction partners\"][paper]\nby Carlo Baldassi, Marco Zamparo, Christoph Feinauer, Andrea Procaccini,\nRiccardo Zecchina, Martin Weigt and Andrea Pagnani, (2014)\nPLoS ONE 9(3): e92721. doi:10.1371/journal.pone.0092721\n\nSee also [this Wikipedia article][wikiDCA] for a general overview of the Direct\nCoupling Analysis technique.\n\nThis code is released under the GPL version 3 (or later) license; see the\n`LICENSE.md` file for details.\n\nThe code is written in [Julia][julia] and requires julia version\n1.5 or later; it provides a function which reads\na multiple sequence alignment (in FASTA format) and returns a ranking of all\npairs of residue positions in the aligned amino-acid sequences.\n\nSince version 2, most of the internal functions used to parse and manipulate\nthe data have been factored out into the package [DCAUtils.jl][DCAUtils].\nThe code in this module is essentially a wrapper around those utilities.\n\n[paper]: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0092721\n[julia]: https://www.julialang.org\n[wikiDCA]: https://en.wikipedia.org/wiki/Direct_coupling_analysis\n\n[CI-img]: https://github.com/carlobaldassi/GaussDCA.jl/actions/workflows/ci.yml/badge.svg\n[CI-url]: https://github.com/carlobaldassi/GaussDCA.jl/actions/workflows/ci.yml\n\n[codecov-img]: https://codecov.io/gh/carlobaldassi/GaussDCA.jl/branch/master/graph/badge.svg\n[codecov-url]: https://codecov.io/gh/carlobaldassi/GaussDCA.jl\n\n[DCAUtils]: https://github.com/carlobaldassi/DCAUtils.jl\n\nInstallation\n------------\n\nTo install the package, enter in Pkg mode by pressing the \u003ckbd\u003e]\u003c/kbd\u003e key,\nthen in the pkg prompt enter\n\n```\n(@v1.5) pkg\u003e add \"https://github.com/carlobaldassi/GaussDCA.jl\"\n```\n\nUsage\n-----\n\nTo load the code, just type `using GaussDCA`.\n\nThis software provides one main function, `gDCA(filname::String, ...)`. This\nfunction takes the name of a (possibly gzipped) FASTA file, and returns a\npredicted contact ranking, in the form of a Vector of triples, each triple\ncontaining two indices `i` and `j` (with `i` \u0026lt; `j`) and a score. The indices\nstart counting from 1, and denote pair of residue positions in the given\nalignment; pairs which are separated by less than a given number of residues\n(by default 5) are filtered out. The triples are sorted by score in descending\norder, such that predicted contacts should come up on top.\n\nFor convenience, a utility function is also provided, `printrank(output, R)`,\nwhich prints the result of `gDCA` either in a file or to a stream, given as\nfirst argument.  If the first argument `output` is omitted, the standard\nterminal output will be used.\n\nThe `gDCA` function takes some additional, optional keyword arguments:\n\n * `pseudocount`: the value of the pseudo-count parameter, between `0` and `1`.\n                  the default is `0.8`, which gives good results when the\n                  Frobenius norm score is used (see below); a good value for the\n                  Direct Information score is `0.2`.\n * `θ`: the value of the similarity threshold. By default it is `:auto`,\n      which means it will be automatically computed (this takes additional\n      time); otherwise, a real value between `0` and `1` can be given.\n * `max_gap_fraction`: maximum fraction of gap symbols in a sequence; sequences\n                       that exceed this threshold are discarded. The default\n                       value is `0.9`.\n * `score`: the scoring function to use. There are two possibilities, `:DI` for\n            the Direct Information, and `:frob` for the Frobenius norm. The\n            default is `:frob`. (Note the leading colon: this argument is passed\n            as a symbol).\n * `min_separation`: the minimum separation between residues in the output\n                     ranking. Must be ≥ `1`. The default\n                     is `5`.\n\nThe code is multi-threaded: if you start julia with the `-t` option, for example\nas `julia -t 8`, the computations will run in parallel on the given number of\nthreads.\n\nExamples\n--------\n\nHere is a basic usage example, assuming an alignment in FASTA format is found\nin the file \"alignment.fasta.gz\":\n\n```\njulia\u003e using GaussDCA\n\njulia\u003e FNR = gDCA(\"alignment.fasta.gz\");\n\njulia\u003e printrank(\"results_FN.txt\", FNR)\n```\n\nThe above uses the Frobenius norm ranking with default parameters.\nThis is how to get the Direct Information ranking instead:\n\n```\njulia\u003e DIR = gDCA(\"alignment.fasta.gz\", pseudocount = 0.2, score = :DI);\n\njulia\u003e printrank(\"results_DI.txt\", DIR)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarlobaldassi%2Fgaussdca.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcarlobaldassi%2Fgaussdca.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcarlobaldassi%2Fgaussdca.jl/lists"}