1,721,085 research outputs found
Efficient Algorithms for Large Scale Analysis of Viral Genome Sequencing Data
Full text linkExtracting insights from the vast dataset of viral genome sequences collected throughout the COVID-19 pandemic requires the development of novel algorithms that are tailored to its unique properties. These properties, such as high sampling density, unambiguous knowledge of the phylogenetic root sequence, and completeness with respect to the virus’s evolutionary history in humans, make it distinct among viral genome datasets. This dissertation details the development and application of advanced computational methodologies to analyze the SARS-CoV-2 genomic dataset. We introduce a suite of computational techniques that are tailored to this data, beginning with SPHERE, an algorithm for scalable phylogeny reconstruction that adapts to the high density of the genomic data. The next is (ε, τ )-MSN, which forms genetic relatedness networks by joining all possible minimum spanning trees and sensibly augmenting the network with additional edges, to capture groups of similar sequences. Furthermore, we present an unsupervised learning approach for finding a clustering of genomic sequences that minimizes cluster entropy. We also propose a method for implementing evolutionary jumps within genetic algorithms, simulating the punctuated equilibrium phenomena observed in SARS-CoV-2 sequencing data, which was shown to improve the speed of convergence for hard instances of the 0-1 Knapsack Problem. Collectively, these works detail new, efficient ways in which to consider modeling and extracting information from large scale viral sequencing datasets.Doctor of Philosophy (PhD)Computer Scienc
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Algorithms for Analysis of Heterogeneous Cancer and Viral Populations Using High-Throughput Sequencing Data
Full text linkNext-generation sequencing (NGS) technologies experienced giant leaps in recent years. Short read samples reach millions of reads, and the number of samples has been growing enormously in the wake of the COVID-19 pandemic. This data can expose essential aspects of disease transmission and development and reveal the key to its treatment. At the same time, single-cell sequencing saw the progress of getting from dozens to tens of thousands of cells per sample. These technological advances bring new challenges for computational biology and require the development of scalable, robust methods to deal with a wide range of problems varying from epidemiology to cancer studies.
The first part of this work is focused on processing virus NGS data. It proposes algorithms that can facilitate the initial data analysis steps by filtering genetically related sequencing and the tool investigating intra-host virus diversity vital for biomedical research and epidemiology.
The second part addresses single-cell data in cancer studies. It develops evolutionary cancer models involving new quantitative parameters of cancer subclones to understand the underlying processes of cancer development better.Doctor of Philosophy (PhD)Computer Scienc
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Estimation of alternative splicing isoform frequencies from RNA-Seq data
Full text linkAbstract Background Massively parallel whole transcriptome sequencing, commonly referred as RNA-Seq, is quickly becoming the technology of choice for gene expression profiling. However, due to the short read length delivered by current sequencing technologies, estimation of expression levels for alternative splicing gene isoforms remains challenging. Results In this paper we present a novel expectation-maximization algorithm for inference of isoform- and gene-specific expression levels from RNA-Seq data. Our algorithm, referred to as IsoEM, is based on disambiguating information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand and read pairing information when available. The open source Java implementation of IsoEM is freely available at http://dna.engr.uconn.edu/software/IsoEM/. Conclusions Empirical experiments on both synthetic and real RNA-Seq datasets show that IsoEM has scalable running time and outperforms existing methods of isoform and gene expression level estimation. Simulation experiments confirm previous findings that, for a fixed sequencing cost, using reads longer than 25-36 bases does not necessarily lead to better accuracy for estimating expression levels of annotated isoforms and genes.</p
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
CliqueSNV: GUI and Deployment on Galaxy
No full textNext Generation Sequencing (NGS) is a powerful technology that has enabled sequencing of thousands to millions of DNA molecules simultaneously. NGS can be applied to any species and source of DNA including viral genomes. CliqueSNV is a novel reference-based tool for reconstruction of viral variants from NGS data. In this thesis, we present a graphical user interface for CliqueSNV installed on Galaxy and as a standalone application such that microbiologists can use the tool without having to type any command line instructions and can also run multiple experiments and include it into pipelines.Master of Science (MS)Computer Scienc
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