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Genome Sequencing Technology And Algorithms
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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 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 Algorithms for Next-Generation Sequencing by : Wing-Kin Sung
Download or read book Algorithms for Next-Generation Sequencing written by Wing-Kin Sung and published by CRC Press. This book was released on 2017-05-18 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in sequencing technology have allowed scientists to study the human genome in greater depth and on a larger scale than ever before – as many as hundreds of millions of short reads in the course of a few days. But what are the best ways to deal with this flood of data? Algorithms for Next-Generation Sequencing is an invaluable tool for students and researchers in bioinformatics and computational biology, biologists seeking to process and manage the data generated by next-generation sequencing, and as a textbook or a self-study resource. In addition to offering an in-depth description of the algorithms for processing sequencing data, it also presents useful case studies describing the applications of this technology.
Book Synopsis Next Generation Sequencing and Sequence Assembly by : Ali Masoudi-Nejad
Download or read book Next Generation Sequencing and Sequence Assembly written by Ali Masoudi-Nejad and published by Springer Science & Business Media. This book was released on 2013-07-09 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this book is to introduce the biological and technical aspects of next generation sequencing methods, as well as algorithms to assemble these sequences into whole genomes. The book is organized into two parts; part 1 introduces NGS methods and part 2 reviews assembly algorithms and gives a good insight to these methods for readers new to the field. Gathering information, about sequencing and assembly methods together, helps both biologists and computer scientists to get a clear idea about the field. Chapters will include information about new sequencing technologies such as ChIp-seq, ChIp-chip, and De Novo sequence assembly.
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 2023-10-12 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guided by standard bioscience workflows in high-throughput sequencing analysis, this book for graduate students, researchers, and professionals in bioinformatics and computer science offers a unified presentation of genome-scale algorithms. This new edition covers the use of minimizers and other advanced data structures in pangenomics approaches.
Book Synopsis Next Generation Sequencing Technologies and Challenges in Sequence Assembly by : Sara El-Metwally
Download or read book Next Generation Sequencing Technologies and Challenges in Sequence Assembly written by Sara El-Metwally and published by Springer Science & Business. This book was released on 2014-04-19 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: The introduction of Next Generation Sequencing (NGS) technologies resulted in a major transformation in the way scientists extract genetic information from biological systems, revealing limitless insight about the genome, transcriptome and epigenome of any species. However, with NGS, came its own challenges that require continuous development in the sequencing technologies and bioinformatics analysis of the resultant raw data and assembly of the full length genome and transcriptome. Such developments lead to outstanding improvements of the performance and coverage of sequencing and improved quality for the assembled sequences, nevertheless, challenges such as sequencing errors, expensive processing and memory usage for assembly and sequencer specific errors remains major challenges in the field. This book aims to provide brief overviews the NGS field with special focus on the challenges facing the NGS field, including information on different experimental platforms, assembly algorithms and software tools, assembly error correction approaches and the correlated challenges.
Book Synopsis Next Generation Sequencing by : Jerzy Kulski
Download or read book Next Generation Sequencing written by Jerzy Kulski and published by BoD – Books on Demand. This book was released on 2016-01-14 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences.
Book Synopsis New High Throughput Technologies for DNA Sequencing and Genomics by : Keith R. Mitchelson
Download or read book New High Throughput Technologies for DNA Sequencing and Genomics written by Keith R. Mitchelson and published by Elsevier. This book was released on 2011-09-22 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the independent invention of DNA sequencing by Sanger and by Gilbert 30 years ago, it has grown from a small scale technique capable of reading several kilobase-pair of sequence per day into today's multibillion dollar industry. This growth has spurred the development of new sequencing technologies that do not involve either electrophoresis or Sanger sequencing chemistries. Sequencing by Synthesis (SBS) involves multiple parallel micro-sequencing addition events occurring on a surface, where data from each round is detected by imaging. New High Throughput Technologies for DNA Sequencing and Genomics is the second volume in the Perspectives in Bioanalysis series, which looks at the electroanalytical chemistry of nucleic acids and proteins, development of electrochemical sensors and their application in biomedicine and in the new fields of genomics and proteomics. The authors have expertly formatted the information for a wide variety of readers, including new developments that will inspire students and young scientists to create new tools for science and medicine in the 21st century.Reviews of complementary developments in Sanger and SBS sequencing chemistries, capillary electrophoresis and microdevice integration, MS sequencing and applications set the framework for the book.* 'Hot Topic' with DNA sequencing continuing as a major research activity in many areas of life science and medicine.* Bringing together new developments in DNA sequencing technology* Reviewing issues relevant to the new applications used
Book Synopsis Efficient Algorithms for Human Genetic Variation Detection Using High-throughput Sequencing Techniques by : Dan He
Download or read book Efficient Algorithms for Human Genetic Variation Detection Using High-throughput Sequencing Techniques written by Dan He and published by . This book was released on 2012 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing (HTS) technologies are one type of genome sequencing techniques where short DNA segments, or reads, are sequenced or sampled from genome. Compared with the traditional genome sequencing techniques, they have advantages such as low-cost and they are able to parallelize the sequencing process to produce millions of reads. These technologies have been widely used in many important problems related to human genetic variations. We mainly target three human genetic variation problems with the reads generated by HTS. It is well-known that human individuals differ from each other by 0.1%. The majority of the differences is in the form of SNPs, or Single Nucleotide Polymophisms. Haplotypes, defined as the sequences of SNPs on each chromosome of a human genome, are important for problems such as imputation of genetic variants, relatedness of human individuals, etc. A difficulty in haplotype inference is the presence of sequencing errors and a natural formulation of the problem is to infer haplotypes which are most consistent with the data from a combinatorial perspective. Unfortunately, this formulation of the haplotype assembly is known to be NP-hard. We proposed a few techniques including dynamic programming, MaxSAT and Hidden Markov Model (HMM) to solve the problem optimally from different perspectives. Structural variations and in particular Copy Number Variations (CNV) have dramatic effects of disease and traits. We first proposed an efficient algorithm to detect and reconstruct CNVs in unique genomic regions, where the sequencing reads generated from HTS are mapped to a reference genome and signatures indicating the presence of a CNV are identified. Then we extend the algorithm to a much more challenging problem where CNVs are in repeat-rich regions and the reads may be mapped to multiple mapping positions. To our knowledge, our method is the first attempt to both identify and reconstruct CNVs in repeat-rich regions, where the sequencing reads generated from HTS are mapped to a reference genome and signatures indicating the presence of a CNV are identified. Then we extend the algorithm to a much more challenging problem where CNVs are in repeat-rich regions and the reads may be mapped to multiple mapping positions. To our knowledge, our method is the first attempt to both identify and reconstruct CNVs in repeat-rich regions. Recent advances in sequencing technologies set the stage for large population based studies, in which the DNA or RNA of thousands of individuals will be sequenced. A few multiplexing schemes have been suggested, in which a small number of DNA pools are sequenced, and the results are then deconvoluted using compressed sensing or similar approaches. These methods, however, are limited to the detection of rare variants. We provide a new algorithm for the deconvolution of DNA pools multiplexing schemes. The presented algorithm utilizes a likelihood model and linear programming and is able to genotype both low and high allele frequency SNPs with microarray genotyping and imputation.
Book Synopsis Various Algorithms for High Throughput Sequencing by : Vladimir Yanovsky
Download or read book Various Algorithms for High Throughput Sequencing written by Vladimir Yanovsky and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Algorithms for Next-generation High-throughput Sequencing Technologies by : Wei-Chun Kao
Download or read book Algorithms for Next-generation High-throughput Sequencing Technologies written by Wei-Chun Kao and published by . This book was released on 2011 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Algorithms for Next-generation Sequencing by : Andreas Sundquist
Download or read book Algorithms for Next-generation Sequencing written by Andreas Sundquist and published by . This book was released on 2008 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 Large-scale Genome Sequence Processing by : Kasahara Masahiro
Download or read book Large-scale Genome Sequence Processing written by Kasahara Masahiro and published by Imperial College Press. This book was released on 2006-07-07 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Efficient computer programs have made it possible to elucidate and analyze large-scale genomic sequences. Fundamental tasks, such as the assembly of numerous whole-genome shotgun fragments, the alignment of complementary DNA sequences with a long genome, and the design of gene-specific primers or oligomers, require efficient algorithms and state-of-the-art implementation techniques. This textbook emphasizes basic software implementation techniques for processing large-scale genome sequences and provides executable sample programs./a
Book Synopsis Sequence Analysis Algorithms for Bioinformatics Application by : Mohamed Issa
Download or read book Sequence Analysis Algorithms for Bioinformatics Application written by Mohamed Issa and published by . This book was released on 2014-10-06 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2014 in the subject Computer Science - Bioinformatics, grade: N, language: English, abstract: The data from next generation sequencing technologies has led to an explosion in genome sequence data available in public databases. This data provides unique opportunities to study the molecular mechanisms of gene evolution: how new genes and proteins originate and how they diversify. A major challenge is retracing origin of extant genes or proteins, by searching existing databases for related sequences and identifying evolutionary similarities. Therefore, enhanced and faster search algorithms are being developed, e.g. on accelerators such as GPU, in order to cope with the huge size of today's DNA or protein sequence databases. Gene-Tracer is a tool was developed to localize the common sub-sequences between two ancestors and its offspring. Besides, compute percentages of ancestors' contributions in offspring. Gene-Tracer was developed to find the origin of unknown shuffling/offspring sequence. A database is scanned and the similarity between offspring sequence and each one in the database is computed using pairwise local sequence alignment algorithm. Based on similarity score, 100 sequences that have the highest score is re-aligned with shuffling sequence to determine length of common sub-sequences between them using local alignment algorithm. The two sequences that have longest sub-sequences with shuffling are the nearest origin to offspring. Swiss-port database contains around 400,000 proteins is used in the test. The execution time around hours. So, GPU is to accelerate the tool. Speedup is 84x using single-GPU Tesla C2075 versus Intel(c) Core i3 multiprocessor. Finally, the main contribution of work is developing fast tool that re-trace origins of unknown gene/protein sequences."
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.
