Year: 2012
Type: dissertation
Abstract: 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.
Source: Työväentutkimus Vuosikirja
Author: Marko Laakso
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2234124049