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MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data.


ABSTRACT:

Background

Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms.

Results

To address this issue, we have developed an integrative genomic analysis platform MD-SeeGH, a software tool that allows users to rapidly and directly analyze genomic datasets spanning multiple genomic experiments. With MD-SeeGH, users have the flexibility to easily update datasets in accordance with new genomic builds, make a quality assessment of data using the filtering features, and identify genetic alterations within single or across multiple experiments. Multiple sample analysis in MD-SeeGH allows users to compare profiles from many experiments alongside tracks containing detailed localized gene information, microRNA, CpG islands, and copy number variations.

Conclusion

MD-SeeGH is a new platform for the integrative analysis of diverse microarray data, facilitating multiple profile analyses and group comparisons.

SUBMITTER: Chi B 

PROVIDER: S-EPMC2408605 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

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Publications

MD-SeeGH: a platform for integrative analysis of multi-dimensional genomic data.

Chi Bryan B   deLeeuw Ronald J RJ   Coe Bradley P BP   Ng Raymond T RT   MacAulay Calum C   Lam Wan L WL  

BMC bioinformatics 20080520


<h4>Background</h4>Recent advances in global genomic profiling methodologies have enabled multi-dimensional characterization of biological systems. Complete analysis of these genomic profiles require an in depth look at parallel profiles of segmental DNA copy number status, DNA methylation state, single nucleotide polymorphisms, as well as gene expression profiles. Due to the differences in data types it is difficult to conduct parallel analysis of multiple datasets from diverse platforms.<h4>Re  ...[more]

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