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The use of open source bioinformatics tools to dissect transcriptomic data.

This is a discussion on The use of open source bioinformatics tools to dissect transcriptomic data. within the Analytic News Feeds forums, part of the Analytics category; The use of open source bioinformatics tools to dissect transcriptomic data. Methods Mol Biol. 2012;835:311-31 Authors: Nitsche BM, Ram AF, Meyer V Abstract Microarrays are a valuable technology to study ...


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Old 21st December 2011, 09:34 PM   #1
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Default The use of open source bioinformatics tools to dissect transcriptomic data.

The use of open source bioinformatics tools to dissect transcriptomic data.

Methods Mol Biol. 2012;835:311-31

Authors: Nitsche BM, Ram AF, Meyer V

Abstract
Microarrays are a valuable technology to study fungal physiology on a transcriptomic level. Various microarray platforms are available comprising both single and two channel arrays. Despite different technologies, preprocessing of microarray data generally includes quality control, background correction, normalization, and summarization of probe level data. Subsequently, depending on the experimental design, diverse statistical analysis can be performed, including the identification of differentially expressed genes and the construction of gene coexpression networks.We describe how Bioconductor, a collection of open source and open development packages for the statistical programming language R, can be used for dissecting microarray data. We provide fundamental details that facilitate the process of getting started with R and Bioconductor. Using two publicly available microarray datasets from Aspergillus niger, we give detailed protocols on how to identify differentially expressed genes and how to construct gene coexpression networks.


PMID: 22183662 [PubMed - in process]



PubMed comprises more than 19 million citations for biomedical articles from MEDLINE and life science journals. This RSS feed searches for mentions of Bioconductor - the open source and open development software project for the analysis and comprehension of genomic data.
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