| 1. |
- Ausmees, Kristiina
(författare)
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Efficient computational methods for applications in genomics
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- During the last two decades, advances in molecular technology have facilitated the sequencing and analysis of ancient DNA recovered from archaeological finds, contributing to novel insights into human evolutionary history. As more ancient genetic information has become available, the need for specialized methods of analysis has also increased. In this thesis, we investigate statistical and computational models for analysis of genetic data, with a particular focus on the context of ancient DNA.The main focus is on imputation, or the inference of missing genotypes based on observed sequence data. We present results from a systematic evaluation of a common imputation pipeline on empirical ancient samples, and show that imputed data can constitute a realistic option for population-genetic analyses. We also discuss preliminary results from a simulation study comparing two methods of phasing and imputation, which suggest that the parametric Li and Stephens framework may be more robust to extremely low levels of sparsity than the parsimonious Browning and Browning model.An evaluation of methods to handle missing data in the application of PCA for dimensionality reduction of genotype data is also presented. We illustrate that non-overlapping sequence data can lead to artifacts in projected scores, and evaluate different methods for handling unobserved genotypes.In genomics, as in other fields of research, increasing sizes of data sets are placing larger demands on efficient data management and compute infrastructures. The last part of this thesis addresses the use of cloud resources for facilitating such analysis. We present two different cloud-based solutions, and exemplify them on applications from genomics.
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| 2. |
- Nettelblad, Carl
(författare)
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Using Markov models and a stochastic Lipschitz condition for genetic analyses
- 2010
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A proper understanding of biological processes requires an understanding of genetics and evolutionary mechanisms. The vast amounts of genetical information that can routinely be extracted with modern technology have so far not been accompanied by an equally extended understanding of the corresponding processes.The relationship between a single gene and the resulting properties, phenotype of an individual is rarely clear. This thesis addresses several computational challenges regarding identifying and assessing the effects of quantitative trait loci (QTL), genomic positions where variation is affecting a trait. The genetic information available for each individual is rarely complete, meaning that the unknown variable of the genotype in the loci modelled also needs to be addressed. This thesis contains the presentation of new tools for employing the information that is available in a way that maximizes the information used, by using hidden Markov models (HMMs), resulting in a change in algorithm runtime complexity from exponential to log-linear, in terms of the number of markers. It also proposes the introduction of inferred haplotypes to further increase the power to assess these unknown variables for pedigrees of related genetically diverse individuals. Modelling consequences of partial genetic information are also treated.Furthermore, genes are not directly affecting traits, but are rather expressed in the environment of and in concordance with other genes. Therefore, significant interactions can be expected within genes, where some combination of genetic variation gives a pronounced, or even opposite, effect, compared to when occurring separately. This thesis addresses how to perform efficient scans for multiple interacting loci, as well as how to derive highly accurate empirical significance tests in these settings. This is done by analyzing the mathematical properties of the objective function describing the quality of model fits, and reformulating it through a simple transformation. Combined with the presented prototype of a problem-solving environment, these developments can make multi-dimensional searches for QTL routine, allowing the pursuit of new biological insight.
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| 3. |
- Pietrini, Alberto
(författare)
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Statistical processing of Flash X-ray Imaging of protein complexes
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Flash X-ray Imaging (FXI) at X-ray Free Electron Lasers (XFELs) is a promising technique that permits the investigation of the 3D structure of molecules without the need for crystallization, by diffracting on single individual sample particles.In the past few years, some success has been achieved by using FXI on quite large biological complexes (40 nm-1 μm in diameter size). Still, the desired dream-goal of imaging a single individual of a molecule or a protein complex (<15 nm in diameter size) has not been reached yet. The main issue that prevented us from a complete success has been the low signal strength, almost comparable to background noise. That is particularly true for experiments performed at the Coherent X-ray Imaging (CXI) instrument at the Linac Coherent Light Source (LCLS).In this thesis, we provide a brief review of the CXI instrument (focusing on experiments there performed) and present a statistical method to deal with low signal-to-noise ratios. We take into account a variety of biological particles, showing the benefits of estimating a background model from sample data and using that for processing said data. Moreover, we present the results of some computer simulations in order to explore the limits and potentials of the proposed approach.Last, we show another method (named COACS) that, being fed with the previous findings from the background model, helps obtaining clearer results in the phase retrieval problem.
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