Efficient computational methods for applications in genomics

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.eSSENC

Methodology and Infrastructure for Statistical Computing in Genomics : Applications for Ancient DNA

This thesis concerns the development and evaluation of computational methods for analysis of genetic data. A particular focus is on ancient DNA recovered from archaeological finds, the analysis of which has contributed to novel insights into human evolutionary and demographic history, while also introducing new challenges and the demand for specialized methods. A main topic is that of 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 develop a tool for genotype imputation that is based on the full probabilistic Li and Stephens model for haplotype frequencies and show that it can yield improved accuracy on particularly challenging data.   Another central subject in genomics and population genetics is that of data characterization methods that allow for visualization and exploratory analysis of complex information. We discuss challenges associated with performing dimensionality reduction of genetic data, demonstrating how the use of principal component analysis is sensitive to incomplete information and performing an evaluation of methods to handle unobserved genotypes. We also discuss the use of deep learning models as an alternative to traditional methods of data characterization in genomics and propose a framework based on convolutional autoencoders that we exemplify on the applications of dimensionality reduction and genetic clustering. 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 final part of this thesis addresses the use of cloud resources for facilitating data analysis in scientific applications. We present two different cloud-based solutions, and exemplify them on applications from genomics.eSSENC

Methodology and Infrastructure for Statistical Computing in Genomics : Applications for Ancient DNA

This thesis concerns the development and evaluation of computational methods for analysis of genetic data. A particular focus is on ancient DNA recovered from archaeological finds, the analysis of which has contributed to novel insights into human evolutionary and demographic history, while also introducing new challenges and the demand for specialized methods. A main topic is that of 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 develop a tool for genotype imputation that is based on the full probabilistic Li and Stephens model for haplotype frequencies and show that it can yield improved accuracy on particularly challenging data.   Another central subject in genomics and population genetics is that of data characterization methods that allow for visualization and exploratory analysis of complex information. We discuss challenges associated with performing dimensionality reduction of genetic data, demonstrating how the use of principal component analysis is sensitive to incomplete information and performing an evaluation of methods to handle unobserved genotypes. We also discuss the use of deep learning models as an alternative to traditional methods of data characterization in genomics and propose a framework based on convolutional autoencoders that we exemplify on the applications of dimensionality reduction and genetic clustering. 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 final part of this thesis addresses the use of cloud resources for facilitating data analysis in scientific applications. We present two different cloud-based solutions, and exemplify them on applications from genomics.eSSENC

Evaluation of methods handling missing data in PCA on genotype data: applications for ancient DNA

Principal Component Analysis (PCA) is a method of projecting data onto a basis that maximizes its variance, possibly revealing previously unseen patterns or features. PCA can be used to reduce the dimensionality of multivariate data, and is widely applied in visualization of genetic information. In the field of ancient DNA, it is common to use PCA to show genetic affinities of ancient samples in the context of modern variation. Due to the low quality and sequence coverage often exhibited by ancient samples, such analysis is not straightforward, particularly when performing joint visualization of multiple individuals with non-overlapping sequence data. The PCA transform is based on variances of allele frequencies among pairs of individuals, and  discrepancies in overlap may therefore have large effects on scores. As the relative distances between scores are used to infer genetic similarity, it is important to distinguish between the effects of the particular set of markers used and actual genetic affinities. This work addresses the problem of using an existing PCA model to estimate scores of new observations with missing data. We address the particular application of visualizing genotype data, and evaluate approaches commonly used in population genetic analyses as well as other methods from the literature. The methods considered are that of trimmed scores, projection to the model plane, performing PCA individually on samples and subsequently merging them using Procrustes transformation, as well as the two least-squares based methods trimmed score regression and known data regression. Using empirical ancient data, we demonstrate the use of the different methods, and show that discrepancies in the set of loci considered for different samples can have pronounced effects on estimated scores. We also present an evaluation of the methods based on modern data with varying levels of simulated sparsity, concluding that their relative performance is highly data-dependent

Efficient computational methods for applications in genomics

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.eSSENC

Evaluation of methods handling missing data in PCA on genotype data : Applications for ancient DNA

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Zone-Based Reachability Analysis of Dense-Timed Pushdown Automata

Proving that programs behave correctly is a matter of both great theoretical interest as well as practical use. One way to do this is by analyzing a model of the system in question in order to determine if it meets a given specification. Real-time recursive systems can be modeled by dense-timed pushdown automata, a model which combines the behaviours of classical timed automata and pushdown automata. The problem of reachability has been proven to be decidable for this model. The algorithm that solves this problem relies on constructing a classical pushdown automaton that mimics the behaviour of a given timed pushdown automaton by means of an abstraction that uses regions as a symbolic representation  of states. The drawback of this approach is that the untimed automaton produced generally contains a very large number of states. This report  proposes a method of generalizing this abstraction by using zones instead of regions, in order to minimize the number of states in the untimed automaton