{ "info": { "author": "Aaron Weimann", "author_email": "weimann@hhu.de", "bugtrack_url": null, "classifiers": [], "description": "Traitar \u2013 the microbial trait analyzer\n======================================\n\nTraitar is a software for characterizing microbial samples from\nnucleotide or protein sequences. It can accurately phenotype `67 diverse\ntraits`_.\n\nTable of Contents\n~~~~~~~~~~~~~~~~~\n\n| `Installation`_\n| `Basic usage`_\n| `Results`_\n| `Docker`_\n| `Citing Traitar`_\n\nInstallation\n============\n\nPlease see `INSTALL.md`_ for installation instructions.\n\nBasic usage\n===========\n\n``traitar phenotype from_nucleotides ``\n\nwill trigger the standard `workflow `__ of Traitar, which is to predict open\nreading frames with Prodigal, annotate the coding sequences provided as\nnucleotide FASTAs in the for all samples in with Pfam families using\nHMMer and finally predict phenotypes from the models for the 67 traits.\n\n\nThe sample file has one column for the sample file names and one for the\nnames as specified by the user. You can also specify a grouping of the\nsamples in the third column, which will be shown in the generated plots.\nThe template looks like following - The header row is mandatory; please\nalso take a look at the sample file for the packaged example data:\n\n| sample\\_file\\_name{tab}sample\\_name{tab}category\n| sample1\\_file\\_name{tab}sample1\\_name[{tabl}sample\\_category1]\n sample2\\_file\\_name{tab}sample2\\_name[{tabl}sample\\_category2]\n\n``traitar phenotype from_genes ``\n\nassumes that gene prediction has been conducted already externally. In\nthis case analysis will start with the Pfam annotation. If the output\ndirectory already exists, Traitar will offer to recompute or resume the\nindividual analysis steps. This option is only available if the process\nis run interactively.\n\nParallel usage\n~~~~~~~~~~~~~~\n\nTraitar can benefit from parallel execution. The ``-c`` parameter sets\nthe number of processes used e.g. ``-c 2`` for using two processes\n\n``traitar phenotype from_nucleotides out_dir -c 2``\n\nThis requires installing GNU parallel as noted above.\n\nInspect phenotype classification models\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nTraitar can be used to inspect the protein families in each phenotype\nmodel:\n\n``traitar show 'Glucose fermenter``\n\nwill show the majority features i.e. the Pfam families that contribute\nto the assignment of the trait Glucose fermenter with *phypat*\nclassifier to some genome sequence. Via \u2013predictor the user may specify\nthe classifier (phypat, phypat+PGL).\n\nRun Traitar with packaged sample data\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n``traitar phenotype /data/sample_data /data/sample_data/samples.txt from_genes -c 2``\nwill trigger phenotyping of *Listeria grayi DSM\\_20601* and *Listeria\nivanovii WSLC3009*. Computation should be done within 5 minutes. You can\nfind out ```` by running\n\n\\`\\`\\` python\npython >>> import traitar >>> traitar.\\ **path** \\`\\`\\`\n\nResults\n=======\n\nTraitar provides the gene prediction results in\n``/gene_prediction``, the Pfam annotation in\n``/pfam_annotation`` and the phenotype prediction\nin\\ ``/phenotype prediction``.\n\nHeatmaps\n~~~~~~~~\n\nThe phenotype prediction is summarized in heatmaps individually for the\nphyletic pattern classifier in ``heatmap_phypat.png``, for the\nphylogeny-aware classifier in ``heatmap_phypat_ggl.png`` and for both\nclassifiers `combined `__ in ``heatmap_comb.png`` and provide hierarchical\nclustering dendrograms for phenotypes and the samples.\n\nPhenotype prediction - Tables and flat files\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThese heatmaps are based on tab separated text files e.g.\n``predictions_majority-vote_combined.txt``. A negative prediction is\nencoded as 0, a prediction made only by the pure phyletic classifier as\n1, one made by the phylogeny-aware classifier by 2 and a prediction\nsupported by both algorithms as 3.\n``predictions_flat_majority-votes_combined.txt`` provides a flat version\nof this table with one prediction per row. The expert user might also\nwant to access the individual results for each algorithm in the\nrespective sub folders ``phypat`` and ``phypat+PGL``.\n\nPhenotype-relevant protein families and feature tracks\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nTraitar will link the protein families and predicted phenotypes. The\nresults can be found in ``phypat/feat_gffs`` and\n\\`\\ ``phypat+PGL/feat_gffs``. If the user picked the \u2018from nucleotides\u2019\noption, Traitar will also generate GFF files that link the genes called\nby Prodidgal with the important protein families. The phenotype-specific\nprotein family annotations tracks can be visualized via GFF files in a\ngenome browser of choice.\n\nFeature tracks with *from\\_genes* option (experimental feature)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nIf the *from\\_genes* option is set, the user may specify gene GFF files\nvia an additional column called gene\\_gff in the sample file. As gene\nids are not consistent across gene GFFs from different sources e.g. img,\nRefSeq or Prodigal the user needs to specify the origin of the gene gff\nfile via the -g / \u2013gene\\_gff\\_type parameter. Still there is no\nguarantee that this works currently. Using samples\\_gene\\_gff.txt as the\nsample file in the above example will generate phenotype-specific Pfam\ntracks for the two genomes.\n\n``traitar phenotype . samples_gene_gff.txt from_genes traitar_out -g refseq``\n\nDocker\n======\n\nThere is a Docker container available for Traitar. Pull by\n\n``docker pull aweimann/traitar``\n\nTo run traitar for the sample data execute\n\n``docker run -v /data/sample_data:/mnt 1445e6c01992 bash -c 'traitar phenotype /mnt/ /mnt/samples.txt from_nucleotides /mnt/traitar_out'``,\n\nwhich will take ~30 minutes. Note there is a problem with parallel usage\nso -c option is not guaranteed to work. The output will be owned by\nroot. So currently you still need root access to your machine to inspect\nthe traitar\\_out folder.\n\nCiting Traitar\n==============\n\nIf you use Traitar in your research, please cite our preprint:\n\n| **From genomes to phenotypes: Traitar, the microbial trait analyzer**\n| Aaron Weimann, Jeremy Frank, Phillip B Pope, Andreas Bremges, Alice C\n McHardy\n| *bioRxiv* (2016) doi:\\ `10.1101/043315`_\n\n.. _10.1101/043315: http://dx.doi.org/10.1101/043315\n.. _67 diverse traits: traits.tsv\n.. _Installation: #installation\n.. _Basic usage: #basic-usage\n.. _Results: #results\n.. _Docker: #docker\n.. _Citing Traitar: #citing-traitar\n.. _INSTALL.md: INSTALL.md", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://github.com/aweimann/traitar", "keywords": null, "license": "GNU General Public License, version 3 (GPL-3.0)", "maintainer": null, "maintainer_email": null, "name": "traitar", "package_url": "https://pypi.org/project/traitar/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/traitar/", "project_urls": { "Download": "UNKNOWN", "Homepage": "http://github.com/aweimann/traitar" }, "release_url": "https://pypi.org/project/traitar/1.1.2/", "requires_dist": null, "requires_python": null, "summary": "traitar - The microbial trait analyzer", "version": "1.1.2" }, "last_serial": 2284374, "releases": { "1.0.1": [ { "comment_text": "", "digests": { "md5": "409a84492839847f810ed9386777a0a3", "sha256": "693a454c0b7c69b1abbf477f3119900ef4d2ba0c85d147fca29d68eaf50cc56f" }, "downloads": -1, "filename": "traitar-1.0.1.tar.gz", "has_sig": false, "md5_digest": "409a84492839847f810ed9386777a0a3", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 3724354, "upload_time": "2016-03-11T16:14:33", "url": "https://files.pythonhosted.org/packages/a1/43/bb0263eec2e4df15e2991b912a72fb8f828eaad1aac34bc19e0664ebe54c/traitar-1.0.1.tar.gz" } ], "1.0.2": [], "1.0.3": [ { "comment_text": "", "digests": { "md5": "1b73d0bb3d3545d267c1c7ce53fe42fc", "sha256": "e42eef18dfedf27032e376ae1ce2c76cf3908c019fc1e79ea4909363454f3d93" }, "downloads": -1, "filename": "traitar-1.0.3.tar.gz", "has_sig": false, "md5_digest": "1b73d0bb3d3545d267c1c7ce53fe42fc", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 3724942, "upload_time": "2016-04-10T12:26:41", "url": "https://files.pythonhosted.org/packages/5d/b1/b1d615af6321675f104a69eca8934c966515410697fc31c3cfb07a7d6b21/traitar-1.0.3.tar.gz" } ], "1.0.4": [ { "comment_text": "", "digests": { "md5": "49f19b1378a1dcd51be056b1f27a89ff", "sha256": "c3cc4f14361b51cfcd0d02488f7cf46cfdd1ebdebf02963087a3d3cd386001fe" }, "downloads": -1, "filename": "traitar-1.0.4.tar.gz", "has_sig": false, "md5_digest": "49f19b1378a1dcd51be056b1f27a89ff", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 3724971, "upload_time": "2016-04-26T09:41:20", "url": "https://files.pythonhosted.org/packages/1d/7e/4a87b435ab7a2d307b3a5610a90245ed95dbdc9a438f8090b6f7fbc82e89/traitar-1.0.4.tar.gz" } ], "1.1.1": [ { "comment_text": "", "digests": { "md5": "b03d708bb7d5db298fb2234cfc529113", "sha256": "efd73a55e8db10b5787ffbde46d5965dd29b4253eff1db73755c72a8755e060f" }, "downloads": -1, "filename": "traitar-1.1.1.tar.gz", "has_sig": false, "md5_digest": "b03d708bb7d5db298fb2234cfc529113", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8374719, "upload_time": "2016-07-25T13:09:05", "url": "https://files.pythonhosted.org/packages/58/7b/63d31f80d72af1ad57e9c8bd2bd73d12dfe2838dc0ca0491d9293915cde2/traitar-1.1.1.tar.gz" } ], "1.1.2": [ { "comment_text": "", "digests": { "md5": "cfd558771c0177ca4aa365906e30f5c3", "sha256": "73886d01c7431cb9bd4345192920fad0a430aeae48ba5b4b844f843f809e5387" }, "downloads": -1, "filename": "traitar-1.1.2.tar.gz", "has_sig": false, "md5_digest": "cfd558771c0177ca4aa365906e30f5c3", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8373797, "upload_time": "2016-08-16T14:12:51", "url": "https://files.pythonhosted.org/packages/8a/10/b90d7e3498fbfd314ca1edccf88eb47e3ef5ad8c9acb46ed16465557dd45/traitar-1.1.2.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "cfd558771c0177ca4aa365906e30f5c3", "sha256": "73886d01c7431cb9bd4345192920fad0a430aeae48ba5b4b844f843f809e5387" }, "downloads": -1, "filename": "traitar-1.1.2.tar.gz", "has_sig": false, "md5_digest": "cfd558771c0177ca4aa365906e30f5c3", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 8373797, "upload_time": "2016-08-16T14:12:51", "url": "https://files.pythonhosted.org/packages/8a/10/b90d7e3498fbfd314ca1edccf88eb47e3ef5ad8c9acb46ed16465557dd45/traitar-1.1.2.tar.gz" } ] }