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Preprocessing Algorithms And Software For Genomic Studies With High Throughput Sequencing Data
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Book Synopsis Preprocessing Algorithms and Software for Genomic Studies with High-throughput Sequencing Data by : Ilya Y. Zhbannikov
Download or read book Preprocessing Algorithms and Software for Genomic Studies with High-throughput Sequencing Data written by Ilya Y. Zhbannikov and published by . This book was released on 2015 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: DNA sequencing technologies address problems, the solutions of which were not possible before, such as whole genome sequencing or microbial community characterization without pre-cultivation. Current High-Throughput Sequencing (HTS) techniques allow genomic studies in small labs as well as in large genomic centers. Together with modern computational software, HTS becomes a powerful tool, which allows researchers to answer important biological questions in novel ways. Despite the advantages of modern HTS technologies, large amounts of data and accompanying noise in HTS library confound bioinformatic analysis. Data preprocessing is needed in order to prepare data for subsequent analysis. Data preprocessing includes noise removal as well as techniques such as data reduction. In this dissertation I present a set of software tools that may be used in genomic studies in order to prepare HTS data for subsequent bioinformatic analysis. The first two chapters in this dissertation describe preprocessing tools developed for data denoising. In the last two chapters I explore the use of multiple genomic markers in 16S data analysis with a meta-amplicon analysis algorithm, which facilitates usage of all the information that can be obtained with 16S amplicon sequencing. Meta-amplicon analysis represents improvements on current methods used to characterize bacterial composition and community structure.
Book Synopsis Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine by : Jeea Choi
Download or read book Pre-processing and Statistical Inference Methods for High-throughput Genomic Data with Application to Biomarker Detection and Regenerative Medicine written by Jeea Choi and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome research advances of the last two decades allow us to obtain various forms of data, such as next-generation sequencing, genotyping, phenotyping, as well as clinical information. However, our ability to derive useful information from these data remains to be improved. This motivated me to develop a pipeline with new computational methods. In this dissertation, I develop, implement, evaluate, and apply statistical and computational methods for high-dimensional data analysis to facilitate efforts in regenerative medicine and to uncover novel insights in cancer genomics. The first method is an integrative pathway-index (IPI) model to identify a clinically actionable biomarker of high-risk advanced ovarian cancer patients. Despite improvements in operative management and therapies, overall survival rates in advanced ovarian cancer have remained largely unchanged over the past three decades. The IPI model is applied to messenger RNA expression and survival data collected on ovarian cancer patients as part of the Cancer Genome Atlas project. The approach identifies signatures that are strongly associated with overall and progression-free survival, and also identifies group of patients who may benefit from enhanced adjuvant therapy. The second method is called SCDC for removing increased variability due to oscillating genes in a snapshot scRNA-seq experiment. Single-cell RNA sequencing provides a new avenue for studying oscillatory gene expression. However, in many studies, oscillations (e.g., cell cycle) are not of interest, and the increased variability imposed by them masks the effects of interest. In bulk RNA-seq, the increase in variability caused by oscillatory genes is mitigated by averaging over thousands of cells. However, in typical unsynchronized scRNA-seq, this variability remains. Simulation and case studies demonstrate that by removing increased variability due to oscillations, both the power and accuracy of downstream analysis is increased. Finally, in this thesis, we have extended a data analysis pipeline for both single- cell and bulk RNA-seq data. In this pipeline, we review current standards and resources for (sc)RNA-seq data analysis and provide an extended pipeline that incorporates a quality control scheme and user friendly advanced statistical analysis software for visualization and projected principal component analysis (PCA).
Book Synopsis Computational Genomics with R by : Altuna Akalin
Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.
Book Synopsis Algorithms for Next-Generation Sequencing Data by : Mourad Elloumi
Download or read book Algorithms for Next-Generation Sequencing Data written by Mourad Elloumi and published by Springer. This book was released on 2017-09-18 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 14 contributed chapters in this book survey the most recent developments in high-performance algorithms for NGS data, offering fundamental insights and technical information specifically on indexing, compression and storage; error correction; alignment; and assembly. The book will be of value to researchers, practitioners and students engaged with bioinformatics, computer science, mathematics, statistics and life sciences.
Book Synopsis Genome-Scale Algorithm Design by : Veli Mäkinen
Download or read book Genome-Scale Algorithm Design written by Veli Mäkinen and published by Cambridge University Press. This book was released on 2015-05-07 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows–Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.
Book Synopsis Computational Methods for Next Generation Sequencing Data Analysis by : Ion Mandoiu
Download or read book Computational Methods for Next Generation Sequencing Data Analysis written by Ion Mandoiu and published by John Wiley & Sons. This book was released on 2016-09-12 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.
Book Synopsis Genome Sequencing Technology and Algorithms by : Sun Kim
Download or read book Genome Sequencing Technology and Algorithms written by Sun Kim and published by Artech House Publishers. This book was released on 2008 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 2003 completion of the Human Genome Project was just one step in the evolution of DNA sequencing. This trailblazing work gives researchers unparalleled access to state-of-the-art DNA sequencing technologies, new algorithmic sequence assembly techniques, and emerging methods for both resequencing and genome analysis.
Book Synopsis Efficient Large-Scale Machine Learning Algorithms for Genomic Sequences by : Daniel Quang
Download or read book Efficient Large-Scale Machine Learning Algorithms for Genomic Sequences written by Daniel Quang and published by . This book was released on 2017 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing (HTS) has led to many breakthroughs in basic and translational biology research. With this technology, researchers can interrogate whole genomes at single-nucleotide resolution. The large volume of data generated by HTS experiments necessitates the development of novel algorithms that can efficiently process these data. At the advent of HTS, several rudimentary methods were proposed. Often, these methods applied compromising strategies such as discarding a majority of the data or reducing the complexity of the models. This thesis focuses on the development of machine learning methods for efficiently capturing complex patterns from high volumes of HTS data.First, we focus on on de novo motif discovery, a popular sequence analysis method that predates HTS. Given multiple input sequences, the goal of motif discovery is to identify one or more candidate motifs, which are biopolymer sequence patterns that are conjectured to have biological significance. In the context of transcription factor (TF) binding, motifs may represent the sequence binding preference of proteins. Traditional motif discovery algorithms do not scale well with the number of input sequences, which can make motif discovery intractable for the volume of data generated by HTS experiments. One common solution is to only perform motif discovery on a small fraction of the sequences. Scalable algorithms that simplify the motif models are popular alternatives. Our approach is a stochastic method that is scalable and retains the modeling power of past methods.Second, we leverage deep learning methods to annotate the pathogenicity of genetic variants. Deep learning is a class of machine learning algorithms concerned with deep neural networks (DNNs). DNNs use a cascade of layers of nonlinear processing units for feature extraction and transformation. Each layer uses the output from the previous layer as its input. Similar to our novel motif discovery algorithm, artificial neural networks can be efficiently trained in a stochastic manner. Using a large labeled dataset comprised of tens of millions of pathogenic and benign genetic variants, we trained a deep neural network to discriminate between the two categories. Previous methods either focused only on variants lying in protein coding regions, which cover less than 2% of the human genome, or applied simpler models such as linear support vector machines, which can not usually capture non-linear patterns like deep neural networks can.Finally, we discuss convolutional (CNN) and recurrent (RNN) neural networks, variations of DNNs that are especially well-suited for studying sequential data. Specifically, we stacked a bidirectional recurrent layer on top of a convolutional layer to form a hybrid model. The model accepts raw DNA sequences as inputs and predicts chromatin markers, including histone modifications, open chromatin, and transcription factor binding. In this specific application, the convolutional kernels are analogous to motifs, hence the model learning is essentially also performing motif discovery. Compared to a pure convolutional model, the hybrid model requires fewer free parameters to achieve superior performance. We conjecture that the recurrent layer allows our model spatial and orientation dependencies among motifs better than a pure convolutional model can. With some modifications to this framework, the model can accept cell type-specific features, such as gene expression and open chromatin DNase I cleavage, to accurately predict transcription factor binding across cell types. We submitted our model to the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge, where it was among the top performing models. We implemented several novel heuristics, which significantly reduced the training time and the computational overhead. These heuristics were instrumental to meet the Challenge deadlines and to make the method more accessible for the research community.HTS has already transformed the landscape of basic and translational research, proving itself as a mainstay of modern biological research. As more data are generated and new assays are developed, there will be an increasing need for computational methods to integrate the data to yield new biological insights. We have only begun to scratch the surface of discovering what is possible from both an experimental and a computational perspective. Thus, further development of versatile and efficient statistical models is crucial to maintaining the momentum for new biological discoveries.
Book Synopsis Bioinformatics and Computational Biology Solutions Using R and Bioconductor by : Robert Gentleman
Download or read book Bioinformatics and Computational Biology Solutions Using R and Bioconductor written by Robert Gentleman and published by Springer Science & Business Media. This book was released on 2005-12-29 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
Book Synopsis Toward a More Accurate Genome by : William Jacob Benhardt Biesinger
Download or read book Toward a More Accurate Genome written by William Jacob Benhardt Biesinger and published by . This book was released on 2014 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing enables basic and translational biology to query the mechanics of both life and disease at single-nucleotide resolution and with breadth that spans the genome. This revolutionary technology is a major tool in biomedical research, impacting our understanding of life's most basic mechanics and affecting human health and medicine. Unfortunately, this important technology produces very large, error-prone datasets that require substantial computational processing before experimental conclusions can be made. Since errors and hidden biases in the data may influence empirically-derived conclusions, accurate algorithms and models of the data are critical. This thesis focuses on the development of statistical models for high-throughput sequencing data which are capable of handling errors and which are built to reflect biological realities. First, we focus on increasing the fraction of the genome that can be reliably queried in biological experiments using high-throughput sequencing methods by expanding analysis into repeat regions of the genome. The method allows partial observation of the gene regulatory network topology through identification of transcription factor binding sites using Chromatin Immunoprecipitation followed by high-throughput sequencing (ChIP-seq). Binding site clustering, or "peak-calling", can be frustrated by the complex, repetitive nature of genomes. Traditionally, these regions are censored from any interpretation, but we re-enable their interpretation using a probabilistic method for realigning problematic DNA reads. Second, we leverage high-throughput sequencing data for the empirical discovery of underlying epigenetic cell state, enabled through analysis of combinations of histone marks. We use a novel probabilistic model to perform spatial and temporal clustering of histone marks and capture mark combinations that correlate well with cell activity. A first in epigenetic modeling with high-throughput sequencing data, we not only pool information across cell types, but directly model the relationship between them, improving predictive power across several datasets. Third, we develop a scalable approach to genome assembly using high-throughput sequencing reads. While several assembly solutions exist, most don't scale well to large datasets, requiring computers with copious memory to assemble large genomes. Throughput continues to increase and the large datasets available today and in the near future will require truly scalable methods. We present a promising distributed method for genome assembly which distributes the de Bruijn graph across many computers and seamlessly spills to disk when main memory is insufficient. We also show novel graph cleaning algorithms which should handle increased errors from large datasets better than traditional graph structure-based cleaning. High-throughput sequencing plays an important role in biomedical research, and has already affected human health and medicine. Future experimental procedures will continue to rely on statistical methods to provide crucial error and bias correction, in addition to modeling expected outcomes. Thus, further development of robust statistical models is critical to the future high-throughput sequencing, ensuring a strong foundation for correct biological conclusions.
Book Synopsis Software Development and Analysis of High Throughput Sequencing Data for Genomic Enhancer Prediction by : Juan González-Vallinas Rostes
Download or read book Software Development and Analysis of High Throughput Sequencing Data for Genomic Enhancer Prediction written by Juan González-Vallinas Rostes and published by . This book was released on 2013 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: High Throughput Sequencing technologies (HTS) are becoming the standard in genomic regulation analysis. During my thesis I developed software for the analysis of HTS data. Through collaborations with other research groups, I specialized in the analysis of ChIP-Seq short mapped reads. For instance, I collaborated in the analysis of the effect of Hog1 stress induced response in Yeast and helped in the design of a multiple promoter-alignment method using ChIP-Seq data, among other collaborations. Making use of expertise and the software developed during this time, I analyzed ENCODE datasets in order to detect active genomic enhancers. Genomic enhancers are regions in the genome known to regulate transcription levels of close by or distant genes. Mechanism of activation and silencing of enhancers is still poorly understood. Epigenomic elements, like histone modifications and transcription factors play a critical role in enhancer activity. Modeling epigenomic signals, I predicted active and silenced enhancers in two cell lines and studied their effect in splicing and transcription initiation.
Book Synopsis Functional Comparison of Current Software Tools for Genomic Assembly from High Throughput Sequencing Data by : Lars J. Olsen
Download or read book Functional Comparison of Current Software Tools for Genomic Assembly from High Throughput Sequencing Data written by Lars J. Olsen and published by . This book was released on 2019 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: "De novo genomic sequencing, which is the process of discovering the sequence of a genome which has not previously been elucidated, provides unique challenges, especially for larger genomes. Modern high-throughput sequencing technologies have addressed the issue of covering the entire genome in a reasonable time by fragmenting the genome into portions that can be examined in a massively-parallel approach. While these have saved considerable time and cost for the chemical process of determining the sequence of a genome, they result in sets of many tens of millions of sequence fragments called reads, each of which is typically on the order of just 100 to 300 bases long. Assembling these reads into a genomic sequence is highly computationally complex. A variety of assembly software packages are readily available for this purpose. In this project, a set of genomic assemblers was selected for examination. These programs were then tested with an Illumina data set for the grape species Vitis romanetii. Experimental runs with this dataset were performed to evaluate the run time required as well as the contiguity, completeness, and accuracy of the resulting assemblies. Different approaches to quality control preprocessing of the sequence data were also explored and evaluated. The results strongly recommend the use of the program MaSuRCA, run with data which has not been preprocessed for quality control. The second highest recommendation would be the use of ABySS with data preprocessed via QuorUM error-correction. In the process of these tests, it was also hoped that at least the beginnings of a draft genome for V. romanetii would be produced. The assemblies which came closest to publication quality were produced by MaSuRCA. Examination of these using the assessment software BUSCO suggest that the best of these assemblies may well be approaching publishable quality."--Abstract.
Book Synopsis Evolution of Translational Omics by : Institute of Medicine
Download or read book Evolution of Translational Omics written by Institute of Medicine and published by National Academies Press. This book was released on 2012-09-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.
Book Synopsis Algorithms for Massive Biological Datasets by : Douglas Wesley Bryant
Download or read book Algorithms for Massive Biological Datasets written by Douglas Wesley Bryant and published by . This book was released on 2011 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the past several years the technology of high-throughput sequencing has transformed the study of biology by offering unprecedented access to life's fundamental building block, DNA. With this transformation's potential a host of brand-new challenges have emerged, many of which lend themselves to being solved through computational methods. From de novo and reference-guided genome assembly to gene prediction and identification, from genome annotation to gene expression, a multitude of biological questions are being asked and answered using high-throughput sequencing and computational methods. In this thesis we examine topics relating to high-throughput sequencing. Beginning with de novo assembly we outline current state-of-the-art methods for stitching short reads, the output of high-throughput sequencing experiments, into cohesive genomic contigs and scaffolds. Next we present our own de novo assembly software, QSRA, created in an effort to form longer contigs even through areas of low coverage and high error. We then present an application of short-read assembly and mutation analysis in a discussion of single nucleotide polymorphism discovery in hazelnut, followed by a review of de novo gene finding, the act of identifying genes in anonymous stretches of genomic sequence. Next we outline our supersplat software, built to align short reads generated by RNA-seq experiments, which span splice junctions, followed by the presentation of our gumby software, build to construct putative gene models from purely empirical short-read data. Finally we outline current state-of-the-art methods for discovering and quantifying alternative splicing variants from RNA-seq short-read data. High-throughput sequencing has fundamentally changed the way in which we approach biological questions. While an exceptionally powerful tool, high-throughput sequencing analysis demands equally powerful algorithmic techniques. We examine these issues through the lens of computational biology.
Book Synopsis Bioinformatics and Functional Genomics by : Jonathan Pevsner
Download or read book Bioinformatics and Functional Genomics written by Jonathan Pevsner and published by John Wiley & Sons. This book was released on 2005-03-04 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wiley is proud to announce the publication of the first ever broad-based textbook introduction to Bioinformatics and Functional Genomics by a trained biologist, experienced researcher, and award-winning instructor. In this new text, author Jonathan Pevsner, winner of the 2001 Johns Hopkins University "Teacher of the Year" award, explains problem-solving using bioinformatic approaches using real examples such as breast cancer, HIV-1, and retinal-binding protein throughout. His book includes 375 figures and over 170 tables. Each chapter includes: Problems, discussion of Pitfalls, Boxes explaining key techniques and math/stats principles, Summary, Recommended Reading list, and URLs for freely available software. The text is suitable for professionals and students at every level, including those with little to no background in computer science.
Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela
Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by MDPI. This book was released on 2021-02-05 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Book Synopsis Complex Genome Analysis with High-throughput Sequencing Data by : Xin Li
Download or read book Complex Genome Analysis with High-throughput Sequencing Data written by Xin Li and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The genomes of most eukaryotes are large and complex. The presence of large amounts of non-coding sequences is a general property of the genomes of complex eukaryotes. High-throughput sequencing is increasingly important for the study of complex genomes. In this dissertation, we focus on two computational problems for high-throughput sequence data analysis, including detecting circular RNA and calling structural variations (especially deletions). Circular RNA (or circRNA) is a kind of non-coding RNA, which consists of a circular configuration through a typical 5' to 3' phosphodiester bond by non-canonical splicing. CircRNA was originally thought as the byproduct from the process of mis-splicing and considered to be of low abundance. Recently, however, circRNA is considered as a new class of functional molecule, and the importance of circRNA in gene regulation and their biological functions in some human diseases have started to be recognized. In this research work, we propose two algorithms to detect potential circRNA. In order to improve the performance of running time, we design an algorithm called CircMarker to find circRNA by creating k-mer table rather than conventional reads mapping. Furthermore, we develop an algorithm named CircDBG by taking advantage of the information from both reads and annotated genome to create de Bruijn graph for circRNA detection, which improves the accuracy and sensitivity. Structural variation (SV), which ranges from 50 bp to ~3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replication. In this research work, we develop a new method called EigenDel for detecting genomic deletions. EigenDel first takes advantage of discordant read-pairs and clipped reads to get initial deletion candidates. Then, EigenDel clusters similar deletion candidates together and calls true deletions from each cluster by using unsupervised learning method. EigenDel outperforms other major methods in terms of balancing accuracy and sensitivity as well as reducing bias. Our results in this dissertation show that sequencing data can be used to study complex genomes by using effective computational approaches.
