Diss. (sammanfattning) Uppsala : Uppsala universitet, 2009. / Härtill 5 uppsatser.

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2009. / Härtill 5 uppsatser.

Thesis (Ph. D.)--University of Texas Health Science Center at Houston, School of Public Health, 2006. / Includes bibliographical references (leaves 104-114).

published_or_final_version / Computer Science / Master / Master of Philosophy

With the new way of sequencing by NGS new tools had to be developed to be able to work with new data formats and to handle the larger data sizes compared to the previous techniques but also to check the accuracy of the data. Coverage analysis is one important quality control for NGS data, the coverage indicates how many times each base pair has been sequenced and thus how trustworthy each base call is. For clinical purposes every base of interest must be quality controlled as one wrong base call could affect the patient negatively. The softwares used for coverage analysis with enough accuracy and detail for clinical applications are sparse. Several softwares like Samtools, are able to calculate coverage values but does not further process this information in a useful way to produce a QC report of each base pair of interest. My master thesis has therefore been to create a new coverage analysis report tool, named CAR tool, that extract the coverage values from Samtools and further uses this data to produce a report consisting of tables, lists and figures. CAR tool is created to replace the currently used tool, ExCID, at the Clinical Genomics facility at SciLifeLab in Uppsala and was developed to meet the needs of the bioinformaticians and clinicians. CAR tool is written in python and launched from a terminal window. The main function of the tool is to display coverage breath values for each region of interest and to extract all sub regions below a chosen coverage depth threshold. The low coverage regions are then reported together with region name, start and stop positions, length and mean coverage value. To make the tool useful to as many as possible several settings are possible by entering different flags when calling the tool. Such settings can be to generate pie charts of each region’s coverage values, filtering of the read and bases by quality or write your own entry that will be used for the coverage calculation by Samtools. The tool has been proved to find these low coverage regions very well. Most low regions found are also found by ExCID, the currently used tool, some differences did however occur and every such region was verified by IGV. The coverage values shown in IGV coincided with those found by CAR tool. CAR tool is written to find all low coverage regions even if they are only one base pair long, while ExCID instead seem to generate larger low regions not taking very short low regions into account. To read more about the functions and how to use CAR tool I refer to User instructions in the appendix and on GitHub at the repository anod6351

Understanding how tumors evolve and drive uncontrolled cellular growth may lead to better prognosis and therapy for individuals suffering from cancer. A key to understanding the paths of progression are to develop computational and experimental methods to dissect clonal heterogeneity and statistically model evolutionary routes. This thesis contains results from analysis of genomic data using computational methods that integrate diverse next generation sequencing data and evolutionary concepts to model tumor evolution and delineate likely routes of genomic alterations. First, I introduce some background and present studies into how tumor genomic sequencing tells us about tumor evolution. This will encompass some of the principles and practices related to tumor heterogeneity within the field of computional biology. Second, I will present a study of longitudinal sampling in Glioblastoma (GBM) in cohort of 114 individuals pre- and post-treatment. We will see how genomic alterations were dissected to uncover a diverse and largely unexpected landscape of recurrence. This details major observations that the recurrent tumor is not likely seeded by the primary lesion. Second, to dissect heterogeneity from clonal evolution, multiple biopsies will be added to extend our longitudinal GBM cohort. This new data will introduce analyses to explicate inter and intra-tumor heterogeneity of GBM. Specifically, we identify a metric of intratumor heterogeneity able to identify multisector biopsies and propose a model of tumor growth in multiple GBM. These results will relate to clinical outcome and are in agreement with previously established hypotheses in truncal mutation targeting. Fourth, I will introduce new models of clonal growth applicable to 2 patient biopsies and then fit these to our GBM cohort. Simulations are used to verify models and a brief proof is presented.

A pharmacometric analysis must be based on correct data to be valid. Source clinical data is rarely ready to be modelled as is, but rather needs to be reprogrammed to fit the format required by the pharmacometric modelling software. The reprogramming steps include selecting the subsets of data relevant for modelling, deriving new information from the source and adjusting units and encoding. Sometimes, the source data may also be flawed, containing vague definitions and missing or confusing values. In either setting, the source data needs to be reprogrammed to remedy this, followed by extensive quality control to capture any errors or inconsistencies produced along the way. The quality control is a lengthy task which is often performed manually, either by the scientists conducting the pharmacometric study or by independent reviewers. This project presents an automatic data quality control with the purpose of aiding the data curation process, as to minimize any potential errors that would otherwise have to be detected by the manual quality control. The automatic quality control is implemented as an R-package and is specifically tailored for the needs of Pharmetheus.

As cancer tumours develop, competition between cells will favour those with some mutations over others, creating a dynamic heterogeneous system made up of different cell populations, called sub-clones. This heterogeneity poses a challenge for treatment, as this variety serves to ensure there is almost always a portion of the cells which are resistant to any one targeted therapy. This can be avoided by combining therapies, but finding viable combinations experimentally is expensive and time-consuming. However, there is also cooperation between sub-clones, and being able to better model and predict these dynamics could allow this interdependence to be exploited. In order to investigate how best to tackle tumour heterogeneity, while avoiding acquired resistance, I have developed the first comprehensive computational model of the gene regulatory network in breast cancer focused on the c-myc oncogene and the differences between sub-clones. I model the system as a discrete, qualitative network, which can reproduce the conditions in heterogeneous tumours, as well as predict the effect of perturbations mimicking mutations or application of therapy. Together with experimental collaborators, I apply my computational model to an in vivo mouse model of MMTV-Wnt1 driven breast cancer, which has high and low c-myc expressing sub-clones. I show that the computational model is able to reproduce the behaviour of this system, and predict how best to target either one sub-clone individually or the tumour as a whole. I show how combination therapies offer more paths to attack the tumour, and how two drugs can work synergistically. For example, I predict how Mek inhibition will preferentially affect one sub-clone, but the addition of COX2 inhibition improves effectiveness across the tumour as a whole. In this thesis, I show how a computational network model can predict treatments in breast cancer, and assess the effects on different clones of different treatment combinations. This model can be easily extended with new data, as well as adapted to different types of cancer. This therefore represents a novel method to find viable combination therapies computationally and speed up the development of new cancer treatments.

Understanding the mechanisms that regulate gene expression is a major challenge in biology. One of the most important tasks in this challenge is to identify the transcription factors binding sites (TFBS) in DNA sequences. The common representation of these binding sites is called “motif” and the discovery of TFBS problem is also referred as motif finding problem in computer science. Despite extensive efforts in the past decade, none of the existing algorithms perform very well. This dissertation focuses on this difficult problem and proposes three new methods (MotifEnumerator, PosMotif, and Enrich) with excellent improvements. An improved pattern-driven algorithm, MotifEnumerator, is first proposed to detect the optimal motif with reduced time complexity compared to the traditional exact pattern-driven approaches. This strategy is further extended to allow arbitrary don’t care positions within a motif without much decrease in solvable values of motif length. The performance of this algorithm is comparable to the best existing motif finding algorithms on a large benchmark set of samples. Another algorithm with further post processing, PosMotif, is proposed to use a string representation that allows arbitrary ignored positions within the non-conserved portion of single motifs, and use Markov chains to model the background distributions of motifs of certain length while skipping these positions within each Markov chain. Two post processing steps considering redundancy information are applied in this algorithm. PosMotif demonstrates an improved performance compared to the best five existing motif finding algorithms on several large benchmark sets of samples. The third method, Enrich, is proposed to improve the performance of general motif finding algorithms by adding more sequences to the samples in the existing benchmark datasets. Five famous motif finding algorithms have been chosen to run on the original datasets and the enriched datasets, and the performance comparisons show a general great improvement on the enriched datasets.

<p>We work on constructing mathematical models of gene regulatory networks for periodic processes, such as the cell cycle in budding yeast, using biological data sets and applying or developing analysis methods in the areas of mathematics, statistics, and computer science. We identify genes with periodic expression and then the interactions between periodic genes, which defines the structure of the network. This network is then translated into a mathematical model, using Ordinary Differential Equations (ODEs), to describe these entities and their interactions. The models currently describe gene regulatory interactions, but we are expanding to capture other events, such as phosphorylation and ubiquitination. To model the behavior, we must then find appropriate parameters for the mathematical model that allow its dynamics to approximate the biological data. </p><p>This pipeline for model construction is not focused on a specific algorithm or data set for each step, but instead on leveraging several sources of data and analysis from several algorithms. For example, we are incorporating data from multiple time series experiments, genome-wide binding experiments, computationally predicted binding, and regulation inference to identify potential regulatory interactions.</p><p>These approaches are designed to be applicable to various periodic processes in different species. While we have worked most extensively on models for the cell cycle in <italic>Saccharomyces cerevisiae</italic>, we have also begun working with data sets for the metabolic cycle in <italic>S. cerevisiae</italic>, and the circadian rhythm in <italic>Mus musculus</italic>.</p> / Dissertation