🍩 Database of Original & Non-Theoretical Uses of Topology

(found 5 matches in 0.001021s)
  1. Fast Estimation of Recombination Rates Using Topological Data Analysis (2019)

    Devon P. Humphreys, Melissa R. McGuirl, Michael Miyagi, Andrew J. Blumberg
    Abstract Accurate estimation of recombination rates is critical for studying the origins and maintenance of genetic diversity. Because the inference of recombination rates under a full evolutionary model is computationally expensive, we developed an alternative approach using topological data analysis (TDA) on genome sequences. We find that this method can analyze datasets larger than what can be handled by any existing recombination inference software, and has accuracy comparable to commonly used model-based methods with significantly less processing time. Previous TDA methods used information contained solely in the first Betti number (\textlessimg class="highwire-embed" alt="Embedded Image" src="http://www.genetics.org/sites/default/files/highwire/genetics/211/4/1191/embed/mml-math-1.gif"/\textgreater) of a set of genomes, which aims to capture the number of loops that can be detected within a genealogy. These explorations have proven difficult to connect to the theory of the underlying biological process of recombination, and, consequently, have unpredictable behavior under perturbations of the data. We introduce a new topological feature, which we call ψ, with a natural connection to coalescent models, and present novel arguments relating \textlessimg class="highwire-embed" alt="Embedded Image" src="http://www.genetics.org/sites/default/files/highwire/genetics/211/4/1191/embed/mml-math-2.gif"/\textgreater to population genetic models. Using simulations, we show that ψ and \textlessimg class="highwire-embed" alt="Embedded Image" src="http://www.genetics.org/sites/default/files/highwire/genetics/211/4/1191/embed/mml-math-3.gif"/\textgreater are differentially affected by missing data, and package our approach as TREE (Topological Recombination Estimator). TREE’s efficiency and accuracy make it well suited as a first-pass estimator of recombination rate heterogeneity or hotspots throughout the genome. Our work empirically and theoretically justifies the use of topological statistics as summaries of genome sequences and describes a new, unintuitive relationship between topological features of the distribution of sequence data and the footprint of recombination on genomes.
  2. Topological Data Analysis Generates High-Resolution, Genome-Wide Maps of Human Recombination (2016)

    Pablo G. Camara, Daniel I. S. Rosenbloom, Kevin J. Emmett, Arnold J. Levine, Raul Rabadan
    Abstract Meiotic recombination is a fundamental evolutionary process driving diversity in eukaryotes. In mammals, recombination is known to occur preferentially at specific genomic regions. Using topological data analysis (TDA), a branch of applied topology that extracts global features from large data sets, we developed an efficient method for mapping recombination at fine scales. When compared to standard linkage-based methods, TDA can deal with a larger number of SNPs and genomes without incurring prohibitive computational costs. We applied TDA to 1,000 Genomes Project data and constructed high-resolution whole-genome recombination maps of seven human populations. Our analysis shows that recombination is generally under-represented within transcription start sites. However, the binding sites of specific transcription factors are enriched for sites of recombination. These include transcription factors that regulate the expression of meiosis- and gametogenesis-specific genes, cell cycle progression, and differentiation blockage. Additionally, our analysis identifies an enrichment for sites of recombination at repeat-derived loci matched by piwi-interacting RNAs.
  3. Characterizing Scales of Genetic Recombination and Antibiotic Resistance in Pathogenic Bacteria Using Topological Data Analysis (2014)

    Kevin J. Emmett, Raul Rabadan
    Abstract Pathogenic bacteria present a large disease burden on human health. Control of these pathogens is hampered by rampant lateral gene transfer, whereby pathogenic strains may acquire genes conferring resistance to common antibiotics. Here we introduce tools from topological data analysis to characterize the frequency and scale of lateral gene transfer in bacteria, focusing on a set of pathogens of significant public health relevance. As a case study, we examine the spread of antibiotic resistance in Staphylococcus aureus. Finally, we consider the possible role of the human microbiome as a reservoir for antibiotic resistance genes.