SIRAC: Supervised Identification of Regions of Aberration in aCGH datasets.
BACKGROUND
Array comparative genome hybridization (aCGH) provides information about genomic aberrations. Alterations in the DNA copy number may cause the cell to malfunction, leading to cancer. Therefore, the identification of DNA amplifications or deletions across tumors may reveal key genes involved in cancer and improve our understanding of the underlying biological processes associated with the disease.


RESULTS
We propose a supervised algorithm for the analysis of aCGH data and the identification of regions of chromosomal alteration (SIRAC). We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion. SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters.


CONCLUSIONS
We illustrate the features of the algorithm with the use of a simple artificial dataset. The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.
Lai
Carmen
C
Bioinformatics group, Delft University, Delft, The Netherlands. c.lai@tudelft.nl
Horlings
Hugo M
HM
van de Vijver
Marc J
MJ
van Beers
Eric H
EH
Nederlof
Petra M
PM
Wessels
Lodewyk F A
LF
Reinders
Marcel J T
MJ
D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D030541:Databases, Genetic; D006801:Humans; D017403:In Situ Hybridization / Q000379:methods*; D009363:Neoplasm Proteins / Q000235:genetics; D010363:Pattern Recognition, Automated / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
D009363:Neoplasm Proteins