Functional genomics and applied bioinformatics

A bioinformatic approach to study alternative splicing in breast tumors based on microarray data

The biological dogma of a simple genetic flow of information from DNA via RNA to protein expression is on its way to disband. A manifold picture of a comprehensive and interactive regulatory system with multiple regulatory mechanisms is evolving, including non-coding RNAs, infrastructural RNAs and alternative splicing processes. Signaling proteins, RNA processing and RNA binding are frequent signals in the targeting of chromatin complexes and in the regulation of gene expression during differentiation and development. A common feature for all these regulation mechanisms is a partial or complete independence of consistency to gene copy numbers. Such uncommon regulatory features can be identified by utilizing microarray datasets, where microarray comparative genomic hybridization (array CGH) analysis of copy number variation has been performed in parallel with microarray gene expression studies. The growing list of unconventional gene regulation mechanisms is striking and we hypothesize that many of them are present and dysregulated in cancer. This seems to be not only to be an interesting feature for cancer diagnostic and therapeutic issues but also for the biological understanding of the mechanisms of gene regulating as such.

Description in Norwegian.

 

This project resulted in the following publication:

Baumbusch LO, Myhre S, Langerød A, Bergamaschi A, Geisler S, Lønning P-E, Deppert W, Dornreiter I and Børresen-Dale A-L (2006) Expression of wild-type and mutated p53 and its novel isoform Δp53 in breast carcinomas. Molecular Cancer 5:47. doi.org/10.1186/1476-4598-5-47.


A bioinformatic and genetic approach to understand gene regulation and dysregulation in breast tumor development and progression

The aim of this project was to a.) Establish, evaluate and possibly develop new bioinformatical tools to handle the massive data obtained from microarray experiments to compare gene expression- with gene copy number data from classical and various array based CGH analyses b.) Identify and characterize genes that are dysregulated by other mechanisms than copy number alterations c.) Analyze the impact of the above identified genes (or gene clusters) on clinical parameters and patient outcome d.) Analyze experimentally the role of mRNA binding proteins using CRD-BP and MYC genes as a model system, and search for similar protein motifs and investigate their role in breast tumorigenesis. Microarray-based Comparative Genome Hybridization (array CGH) provides the possibility to connect DNA copy-aberrations with genomic map positions. Different types of arrays may be used in such analyses, cDNA, BAC arrays containing genomic DNA or oligoarrays. In close collaboration with the Bioinformatics group, Institute of Informatics (University of Oslo), UCSF and the Stanford University a comparative study of the different methods is performed based on state-of-the-art statistical modeling and analysis methods. This project was supported by FUGE.

 

This project resulted in the following publications:

Baumbusch LO, Aarøe J, Hicks J, Johansen FE, Sun H, Bruhn L, Gunderson K, Naume B, Kristensen VN, Listøl K, Børresen-Dale A-L and Lingjærede OC (2008) Comparison of the Agilent, ROMA/NimbleGen, and Illumina platforms for classification of copy number alterations in human breast tumors. BMC Genomics 9:379. doi.org/10.1186/1471-2164-9-379.

Ben-Dor A, Lipson D, Tsalenko A, Reimers M, Baumbusch LO, Barrett MM, Weinstein J, Børresen-Dale A-L, Yakhini Z (2007) Framework for identifying common aberrations in DNA copy number data. In: Research in Computational Molecular Biology. Springer, Berlin/Heidelberg: 4453:122.

Lingjærde OC, Baumbusch LO, Liestøl K, Glad I and Børresen-Dale AL (2005) CGH-Explorer: a program for visualization and analysis of CGH-array data. Bioinformatics 21(6):821. doi.org/10.1093/bioinformatics/bti113. 

(*These authors should be considered as shared first authors)


 

 
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