Browsing by Author "Laakso, Marko"

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  • Laakso, Marko (Helsingin yliopisto, 2007)
    This thesis presents a highly sensitive genome wide search method for recessive mutations. The method is suitable for distantly related samples that are divided into phenotype positives and negatives. High throughput genotype arrays are used to identify and compare homozygous regions between the cohorts. The method is demonstrated by comparing colorectal cancer patients against unaffected references. The objective is to find homozygous regions and alleles that are more common in cancer patients. We have designed and implemented software tools to automate the data analysis from genotypes to lists of candidate genes and to their properties. The programs have been designed in respect to a pipeline architecture that allows their integration to other programs such as biological databases and copy number analysis tools. The integration of the tools is crucial as the genome wide analysis of the cohort differences produces many candidate regions not related to the studied phenotype. CohortComparator is a genotype comparison tool that detects homozygous regions and compares their loci and allele constitutions between two sets of samples. The data is visualised in chromosome specific graphs illustrating the homozygous regions and alleles of each sample. The genomic regions that may harbour recessive mutations are emphasised with different colours and a scoring scheme is given for these regions. The detection of homozygous regions, cohort comparisons and result annotations are all subjected to presumptions many of which have been parameterized in our programs. The effect of these parameters and the suitable scope of the methods have been evaluated. Samples with different resolutions can be balanced with the genotype estimates of their haplotypes and they can be used within the same study.
  • Laakso, Marko (Helsingin yliopisto, 2012)
    The genetic alterations of cancer cells vary between individuals and during the progression of the disease. The advances in measurement techniques have enabled genome-scale profiling of mutations, transcription, and DNA methylation. These methods can be used to address the complexity of the disease but also raise an acute demand for the analysis of the high dimensional data sets produced. An integrative and scalable computational infrastructure is advantageous in cancer research. First, a multitude of programs and analytic steps are needed when integrating various measurement types. An efficient execution and management of such projects saves time and reduces the probability of mistakes. Second, new information and methods can be utilised with a minor effort of re-executing the workflow. Third, a formal description of the program interfaces and the workflows aids collaboration, testing, and reuse of the work done. Fourth, the number of samples available is often small in comparison with the unknown variables, such as possibly affected genes, of interest. The interpretation of new measurements in the context of existing information may limit the number of false positives when sensitive methods are needed. We have introduced new computational methods for the data integration and for the management of large and heterogeneous data sets. The suitability of the methods has been demonstrated with four cancer studies covering a wide spectrum of data from population genetics to the details of the transcriptional regulation of proteins, such as androgen receptor and forkhead box protein A1. The repeatable workflows established for these colorectal cancer, glioblastoma, and prostate cancer studies have been used to maintain up-to-date registries of results for follow-up studies.