source: orange-bioinformatics/README.rst @ 1714:cdad13f0d034

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1Orange Bioinformatics
2=====================
3
4Orange Bioinformatics extends Orange_, a data mining software
5package, with common functionality for bioinformatics. The provided
6functionality can be accessed as a Python library or through a visual
7programming interface (Orange Canvas). The latter is also suitable for
8non-programmers.
9
10In Orange Canvas the analyst connects basic computational units, called
11widgets, into data flow analytics schemas. Two units-widgets can be
12connected if they share a data type. Compared to other popular tools like
13Taverna, Orange widgets are high-level, integrated potentially complex
14tasks, but are specific enough to be used independently. Even elaborate
15analyses rarely consist of more than ten widgets; while tasks such as
16clustering and enrichment analysis could be executed with up to five
17widgets. While building the schema each widget is independently controlled
18with settings, the settings do not conceptually burden the analyst.
19
20Orange Bioinformatics provides access to publicly available data,
21like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx
22database. As for the analytics, there is gene selection, quality control,
23scoring distances between experiments with multiple factors. All features
24can be combined with powerful visualization, network exploration and
25data mining techniques from the Orange data mining framework.
26
27.. _Orange: http://orange.biolab.si/
28
29Documentation: http://orange-bioinformatics.readthedocs.org/
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