scikit-bio-core-bioinformatics-data-structures-a.json

{
  "alias": "video/2815/scikit-bio-core-bioinformatics-data-structures-a",
  "category": "SciPy 2014",
  "copyright_text": "https://www.youtube.com/t/terms",
  "description": "Python is widely used in computational biology, with many high profile\nbioinformatics software projects, such as\n`Galaxy <http://galaxyproject.org/>`__,\n`Khmer <http://khmer.readthedocs.org/en/latest/>`__ and\n`QIIME <http://www.qiime.org>`__, being largely or entirely written in\nPython. We present `scikit-bio <http://www.scikit-bio.org>`__, a new\nlibrary based on the standard Python scientific computing stack (e.g.,\nnumpy, scipy, and matplotlib) implementing core bioinformatics data\nstructures, algorithms, parsers, and formatters. scikit-bio is the first\nbioinformatics-centric `scikit <https://scikits.appspot.com/>`__, and\narises from over ten years of development efforts on\n`PyCogent <http://www.pycogent.org>`__ and\n`QIIME <http://www.qiime.org>`__, representing an effort to update the\nfunctionality provided by these extensively used tools, and to make that\nfunctionality more accessible. scikit-bio is intended to be useful both\nas a resource for students, who can learn topics such as heuristic-based\nsequence database searching or iterative progressive multiple sequence\nalignment from the source code and accompanying documentation, and as a\npowerful library for 'real-world' bioinformatics developers. To achieve\nthese goals, scikit-bio development is centered around test-driven,\npeer-reviewed software development; C/Cython integration for\ncomputationally expensive algorithms; extensive API documentation and\ndoc-testing based on the `numpy docstring\nstandards <https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt>`__;\nuser documentation and `theoretical discussion of topics in IPython\nNotebooks <http://caporasolab.us/An-Introduction-To-Applied-Bioinformatics/>`__;\nadherence to PEP8; and continuous integration testing. scikit-bio is\navailable free of charge under the BSD license.\n",
  "duration": null,
  "id": 2815,
  "language": "eng",
  "quality_notes": "",
  "recorded": "2014-07-10",
  "related_urls": [
    "http://caporasolab.us/An-Introduction-To-Applied-Bioinformatics/",
    "http://galaxyproject.org/",
    "http://khmer.readthedocs.org/en/latest/",
    "http://www.pycogent.org",
    "http://www.qiime.org",
    "http://www.scikit-bio.org",
    "https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt",
    "https://scikits.appspot.com/"
  ],
  "slug": "scikit-bio-core-bioinformatics-data-structures-a",
  "speakers": [
    "J Gregory Caporaso"
  ],
  "summary": "We present scikit-bio, a library based on the Python scientific\ncomputing stack implementing core bioinformatics data structures,\nalgorithms and parsers. scikit-bio is useful for students in\nbioinformatics, who can learn topics such as iterative progressive\nmultiple sequence alignment from the source code and accompanying\ndocumentation, and for real-world bioinformatics applications\ndevelopers.\n",
  "tags": [],
  "thumbnail_url": "https://i1.ytimg.com/vi/hgBx_DBiPxA/hqdefault.jpg",
  "title": "scikit-bio: core bioinformatics data structures and algorithms in Python",
  "videos": [
    {
      "length": 0,
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=hgBx_DBiPxA"
    }
  ]
}