{ "info": { "author": "Olga B. Botvinnik", "author_email": "olga.botvinnik@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.3", "Topic :: Scientific/Engineering" ], "description": "sj2psi\n======\n\n[[![Build Status](https://travis-ci.org/olgabot/sj2psi.svg)](https://travis-ci.org/olgabot/sj2psi)](https://travis-ci.org/olgabot/sj2psi)[![Coverage Status](https://coveralls.io/repos/olgabot/sj2psi/badge.svg?branch=master)](https://coveralls.io/r/olgabot/sj2psi?branch=master)\n\nDOI: 10.5281/zenodo.9885\n\nAnnotation-free estimation of percent spliced in of a junction. This\nwill convert [RNA-STAR aligner](http://bioinformatics.oxfordjournals.org/content/29/1/15.long) \"SJ.out.tab\" files to \"Percent spliced-in\"\n(Psi)\nscores. Here's an example of an `SJ.out.tab` file:\n\n```\nchr1 30040 30563 1 1 1 0 131 45\nchr1 30668 30975 1 1 1 0 123 46\nchr1 146510 155766 2 2 1 50 92 46\nchr1 155832 694346 2 2 0 6 14 26\nchr1 317782 322037 1 1 1 0 2 3\nchr1 320939 321031 1 1 1 2 4 31\nchr1 322229 324287 1 1 1 0 3 21\nchr1 322229 324438 1 1 0 0 5 40\nchr1 324346 324438 1 1 1 0 2 13\nchr1 324711 325802 2 4 0 0 1 30\nchr1 663813 664904 2 4 0 0 1 38\nchr1 665185 667396 2 2 0 0 2 37\nchr1 665185 670802 2 2 0 0 4 40\nchr1 667588 682074 2 2 0 0 44 43\nchr1 668594 670802 2 2 0 0 4 40\nchr1 670994 682074 2 2 0 0 47 43\n```\n\nAs described in [Pervouchine et al, Bioinformatics (2013)](http://bioinformatics.oxfordjournals.org/content/29/2/273.long), we will take the approach of asking,\nhow often is this donor site (5' splice site) used with this acceptor\nsite (3' splice site), compared to ALL OTHER acceptors?\n\nSame goes for acceptor sites. How often is this acceptor site, used with\nthis donor site, compared to ALL OTHER donors?\n\nTo illustrate, check out this example. Each \"-\" represents 10 bp\n\n Splice junction fig genome location number of reads\n [ ]--------[ ] chr1:100-180 90\n [ ]----------[ ] chr1:100-200 10\n [ ]-------[ ] chr1:130-200 40\n\nFor the 5' splice site chr1:100, we have 90+10 = 100 total reads. Thus the\n\"psi5\" for chr1:100-180 is 90/100 = 0.9, and 0.1 for chr:100-200.\n\nFor the 3' splice site chr1:200, we have 10+40 = 50 total reads. Thus the\n\"psi3\" for chr1:100-200 is 10/50 = 0.2, and 0.8 for chr:130-200.\n\nWhat's left is the uninteresting splice sites of chr1:180 and chr1:130,\nboth of which didn't have any variance and were always used. Thus psi3\nfor chr1:180 is 1.0, and psi5 for chr1:130 is 1.0 as well.\n\n >>> import pandas as pd\n >>> data = {'chrom': ['chr1', 'chr1', 'chr1'],\n ... 'first_bp_intron':[100, 100, 130], 'last_bp_intron':[180, 200, 200],\n ... 'unique_junction_reads':[90, 10, 40],\n ... 'multimap_junction_reads':[0, 0, 0]}\n >>> sj = pd.DataFrame(data)\n >>> get_psis(sj, min_multimap=0)\n chrom first_bp_intron last_bp_intron multimap_junction_reads \\\n 0 chr1 100 180 0\n 1 chr1 100 200 0\n 2 chr1 130 200 0\n \n unique_junction_reads multimap_junction_reads_filtered \\\n 0 90 0\n 1 10 0\n 2 40 0\n \n unique_junction_reads_filtered total_filtered_reads psi5 psi3\n 0 90 90 0.9 1.0\n 1 10 10 0.1 0.2\n 2 40 40 1.0 0.8\n \n [3 rows x 10 columns]", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://olgabot.github.io/prettyplotlib", "keywords": null, "license": "MIT License", "maintainer": null, "maintainer_email": null, "name": "sj2psi", "package_url": "https://pypi.org/project/sj2psi/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/sj2psi/", "project_urls": { "Download": "UNKNOWN", "Homepage": "http://olgabot.github.io/prettyplotlib" }, "release_url": "https://pypi.org/project/sj2psi/0.1.2/", "requires_dist": null, "requires_python": null, "summary": "Convert RNA-STAR SJ.out.tab files to 5-prime and 3-prime \"percent spliced in\" (\"psi\") scores.", "version": "0.1.2" }, "last_serial": 1667667, "releases": { "0.0.1": [ { "comment_text": "", "digests": { "md5": "c906806dac2a6b77af96ee86a81e6dc0", "sha256": "29a2a2e87ffbe9a0cf2c8fa326b984a4b31cabf728f42d78915a046e5c83e82a" }, "downloads": -1, "filename": "sj2psi-0.0.1.tar.gz", "has_sig": false, "md5_digest": "c906806dac2a6b77af96ee86a81e6dc0", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 3331, "upload_time": "2014-05-01T17:09:28", "url": "https://files.pythonhosted.org/packages/d2/e7/d4d1b2d8d734e3c1153dbcb4fbab4a451d95400dbd9c71108806dd3550a4/sj2psi-0.0.1.tar.gz" } ], "0.1.2": [ { "comment_text": "", "digests": { "md5": "1c4d4a819f5553a881d28ed7c98b41fc", "sha256": "699370aca88613711db62b6684224d3d0c6337e768dee7982b8ba5c303d95a40" }, "downloads": -1, "filename": "sj2psi-0.1.2.tar.gz", "has_sig": false, "md5_digest": "1c4d4a819f5553a881d28ed7c98b41fc", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4190, "upload_time": "2015-08-07T00:34:20", "url": "https://files.pythonhosted.org/packages/bb/65/eb3edfe343d6537665ede1880c6cca19e1ad9e621460373eca767469548f/sj2psi-0.1.2.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "1c4d4a819f5553a881d28ed7c98b41fc", "sha256": "699370aca88613711db62b6684224d3d0c6337e768dee7982b8ba5c303d95a40" }, "downloads": -1, "filename": "sj2psi-0.1.2.tar.gz", "has_sig": false, "md5_digest": "1c4d4a819f5553a881d28ed7c98b41fc", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 4190, "upload_time": "2015-08-07T00:34:20", "url": "https://files.pythonhosted.org/packages/bb/65/eb3edfe343d6537665ede1880c6cca19e1ad9e621460373eca767469548f/sj2psi-0.1.2.tar.gz" } ] }