Transcript Introduction to BioConductor

Introduction to BioConductor
Friday 23th nov 2007
Ståle Nygård ([email protected])
Course in Statistical methods and
bioinformatics for the analysis of microarray
data
What is BioConductor?
An open source and open development software project
for the analysis and comprehension of genomic data.
Started in 2001. The core team is based primarily at the
Fred Hutchinson Cancer Research Center.
Is primarily based on the R programming language.
There are two releases of Bioconductor every year. In
addition there are a large number of meta-data packages
available, mainly, but not solely oriented towards
different types of microarrays.
Goals of the Bioconductor
Project
Provide access to a wide range of powerful statistical
and graphical methods for the analysis of genomic data.
Facilitate the integration of biological metadata in the
analysis of experimental data: e.g. literature data from
PubMed, annotation data from LocusLink.
Allow the rapid development of extensible, scalable, and
interoperable software.
Promote high-quality documentaion and reproducible
research
Provide training in computational and statistical methods
for the analysis of genomic data.
Main features of the Bioconductor
Project
Use of R
Documentation and reproducible research
Statistical and graphical methods
Annotation
Bioconductor short courses
Open source
Open development
Use of R
R and the R package system are the main vehicles for
designing and releasing software.
Documentation and reproducible
research
Each package contains at least one vignette, which is a
document that provides a textual, task-oriented
description of the package's functionality and that can be
used interactively.
In the future: looking towards vignettes not specifically
tied to a package, but rather demonstrating more
complex concepts.
Bioconductor FAQ:
http://www.bioconductor.org/docs/faq/index.html#Open%20source
Book:
Statistical and graphical
methods
Bioconductor analysis packages
–
–
–
–
Preprosessing Affymetrix and cDNA array data
Identifying differentially expressed genes
Graph theoretical analyses
Plotting genomic data
In addition, R itself provides implementations for a broad range of
state-of-the-art statistical and graphical techniques including
–
–
–
–
–
–
Linear and non-linear modeling
Cluster analysis
Prediction
Resampling
Survival analysis
Time series analysis
(Screenshots: http://www.bioconductor.org/whatisit/screenshots/)
Annotation
Bioconductor project provides software for associating genomic data
in real time to biological metadata from web databases such as
GenBank, Locus Link, and Pubmed (annotate package).
Provides functions for incorporating the results in HTML reports with
links to annotation www resources
Provides software tools for assembling and processing genomic
annotation from databases such as GenBank, the Gene Ontology
Consortium, LocusLink, UniGene, the UCSC Human Genome
Project (AnnBuilder package).
Data packages are distributed to provide mappings between
different probe identifiers (e.g. Affy IDs, LocusLink, PubMed).
Customized annotation libraries can also be assembled.
Bioconductor short courses
The Bioconductor project has developed a program of
short courses on software and statistical methods for the
analysis of genomic data. (course materials etc at:
http://www.bioconductor.org/services/workshops)
Open source
There are many different reasons why open-source software is
beneficial to the analysis of microarray data and to computational
biology in general, because it
– facilitates full access to algorithms and their impementation
– enables to fix bugs and extend and improve the supplied software
– encourages good scientific computing and statistical practice by
providing appropriate tools and instruction
– provides a workbench of tools that allow researchers to explore and
expand the methods used to analyze biological data
– ensures that the international scientific community is the owner of the
software tools needed to carry out research
– leads and encourages commercial support and development of those
tools that are successful
– promotes reproducible research by providing open and accessible tools
with which to carry out that research
Open development
Users are encouraged to become developers, either by
contributing bioconductor compliant packages or
documentation.
Installation of bioconductor
Install R
Install bioconductor packages:
http://www.bioconductor.org/docs/install-howto.html
Installation tailored for this course:
http://sfi.nr.no/sfi/index.php/Click_here
To check if your packages really is installed type library().