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Bioinformatics Advance Access published online on May 17, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm263
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Computational Identification of Candidate Loci for Recessively Inherited Mutation Using High Throughput SNP Arrays

Marko Laakso a,c, Sari Tuupanen b,c, Auli Karhu b,c, Rainer Lehtonen b,c, Lauri Aaltonen b,c and Sampsa Hautaniemi a,c,*

aComputational Systems Biology Laboratory, Institute of Biomedicine, University of Helsinki, bDepartment of Medical Genetics, University of Helsinki, cGenome-Scale Biology Research Program, Biomedicum Helsinki

*To whom correspondence should be addressed. Dr. Sampsa Hautaniemi, E-mail: sampsa.hautaniemi{at}helsinki.fi


  Abstract

Motivation: Single nucleic polymorphisms (SNPs) are one of the most abundant genetic variations in the human genome. Recently, several platforms for high-throughput SNP analysis have become available, capable of measuring thousands of SNPs across the genome. Tools for analysing and visualising these large genetic datasets in biologically relevant manner are rare. This hinders effective use of the SNP-array data in research on complex diseases, such as cancer.

Results: We describe a computational framework to analyse and visualise SNP-array data, and link the results in relevant databases. Our major objective is to develop methods for identifying DNA regions that likely harbour recessive mutations. Thus, the algorithms are designed to have high sensitivity and the identified regions are ranked using a scoring algorithm. We have also developed annotation tools that automatically query gene IDs, exon counts, microarray probe IDs etc. In our case study we apply the methods for identifying candidate regions for recessively inherited colorectal cancer predisposition and suggest directions for wet-lab experiments.

Availability: R-package implementation is available at http://www.ltdk.helsinki.fi/sysbio/csb/downloads/CohortComparator/

Supplementary information: Supplementary information is available at Bioinformatics online

Associate Editor: Prof. Keith Crandall

Received on November 3, 2006; revised on April 22, 2007; accepted on May 10, 2007

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