Computational Genomic Signatures

Computational Genomic Signatures PDF Author: Ozkan Ufuk Nalbantoglu
Publisher: Springer Nature
ISBN: 3031016505
Category : Technology & Engineering
Languages : en
Pages : 113

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Book Description
Recent advances in development of sequencing technology has resulted in a deluge of genomic data. In order to make sense of this data, there is an urgent need for algorithms for data processing and quantitative reasoning. An emerging in silico approach, called computational genomic signatures, addresses this need by representing global species-specific features of genomes using simple mathematical models. This text introduces the general concept of computational genomic signatures, and it reviews some of the DNA sequence models which can be used as computational genomic signatures. The text takes the position that a practical computational genomic signature consists of both a model and a measure for computing the distance or similarity between models. Therefore, a discussion of sequence similarity/distance measurement in the context of computational genomic signatures is presented. The remainder of the text covers various applications of computational genomic signatures in the areas of metagenomics, phylogenetics and the detection of horizontal gene transfer. Table of Contents: Genome Signatures, Definition and Background / Other Computational Characterizations as Genome Signatures / Measuring Distance of Biological Sequences Using Genome Signatures / Applications: Phylogeny Construction / Applications: Metagenomics / Applications: Horizontal DNA Transfer Detection

Computational Genomic Signatures

Computational Genomic Signatures PDF Author: Ozkan Ufuk Nalbantoglu
Publisher: Springer Nature
ISBN: 3031016505
Category : Technology & Engineering
Languages : en
Pages : 113

Get Book Here

Book Description
Recent advances in development of sequencing technology has resulted in a deluge of genomic data. In order to make sense of this data, there is an urgent need for algorithms for data processing and quantitative reasoning. An emerging in silico approach, called computational genomic signatures, addresses this need by representing global species-specific features of genomes using simple mathematical models. This text introduces the general concept of computational genomic signatures, and it reviews some of the DNA sequence models which can be used as computational genomic signatures. The text takes the position that a practical computational genomic signature consists of both a model and a measure for computing the distance or similarity between models. Therefore, a discussion of sequence similarity/distance measurement in the context of computational genomic signatures is presented. The remainder of the text covers various applications of computational genomic signatures in the areas of metagenomics, phylogenetics and the detection of horizontal gene transfer. Table of Contents: Genome Signatures, Definition and Background / Other Computational Characterizations as Genome Signatures / Measuring Distance of Biological Sequences Using Genome Signatures / Applications: Phylogeny Construction / Applications: Metagenomics / Applications: Horizontal DNA Transfer Detection

Computational Genomic Signatures and Metagenomics

Computational Genomic Signatures and Metagenomics PDF Author: Ozkan Ufuk Nalbantoglu
Publisher:
ISBN: 9781124588902
Category : Computational biology
Languages : en
Pages :

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Computational Identification of Genomic Signatures for Cancer Diagnosis

Computational Identification of Genomic Signatures for Cancer Diagnosis PDF Author: Yanen Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 66

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Book Description


Introduction to Computational Genomics

Introduction to Computational Genomics PDF Author: Nello Cristianini
Publisher: Cambridge University Press
ISBN: 9780521856034
Category : Computers
Languages : en
Pages : 200

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Book Description
Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the reader with Matlab-related software tools for reproducing the steps demonstrated in the book.

Computational Exome and Genome Analysis

Computational Exome and Genome Analysis PDF Author: Peter Nicholas Robinson
Publisher: Chapman & Hall/CRC Mathematical and Computational Biology
ISBN: 9781498775984
Category : Computational biology
Languages : en
Pages : 557

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Book Description
Cover -- Half Title -- Series Editor -- Published Titles -- Title -- Copyright -- Dedication -- Contents -- Who is this book for? -- Preface -- Contributors -- Part I Introduction -- Chapter 1 Introduction: Whole Exome and Genome Sequencing -- Chapter 2 NGS Technology -- Chapter 3 Illumina Technology -- Chapter 4 Data -- Part II Raw Data Processing -- Chapter 5 FASTQ Format -- Chapter 6 Raw Data: Quality Control -- Chapter 7 Trimming -- Part III Alignment -- Chapter 8 Alignment: Mapping Reads to the Reference Genome -- Chapter 9 SAM/BAM Format -- Chapter 10 Postprocessing the Alignment -- Chapter 11 Alignment Data: Quality Control -- Part IV Variant Calling -- Chapter 12 Variant Calling and Quality- Based Filtering -- Chapter 13 Variant Call Format (VCF) -- Chapter 14 Jannovar -- Chapter 15 Variant Annotation -- Chapter 16 Variant Calling: Quality Control -- Chapter 17 Integrative Genomics Viewer (IGV): Visualizing Alignments and Variants -- Chapter 18 De Novo Variants -- Chapter 19 Structural Variation -- Part V Variant Filtering -- Chapter 20 Pedigree and Linkage Analysis -- Chapter 21 Intersection Analysis and Rare Variant Association Studies -- Chapter 22 Variant Frequency Analysis -- Chapter 23 Variant Pathogenicity Prediction -- Part VI Prioritization -- Chapter 24 Variant Prioritization -- Chapter 25 Prioritization by Random Walk Analysis -- Chapter 26 Phenotype Analysis -- Chapter 27 Exomiser and Genomiser -- Chapter 28 Medical Interpretation -- Part VII Cancer -- Chapter 29 A (Very) Short Introduction to Cancer -- Chapter 30 Somatic Variants in Cancer -- Chapter 31 Tumor Evolution and Sample Purity -- Chapter 32 Driver Mutations and Mutational Signatures -- Appendix A Hints and Answers -- References -- Index

Computational Methods for Understanding Bacterial and Archaeal Genomes

Computational Methods for Understanding Bacterial and Archaeal Genomes PDF Author: Ying Xu
Publisher: World Scientific
ISBN: 1860949827
Category : Medical
Languages : en
Pages : 494

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Book Description
Over 500 prokaryotic genomes have been sequenced to date, and thousands more have been planned for the next few years. While these genomic sequence data provide unprecedented opportunities for biologists to study the world of prokaryotes, they also raise extremely challenging issues such as how to decode the rich information encoded in these genomes. This comprehensive volume includes a collection of cohesively written chapters on prokaryotic genomes, their organization and evolution, the information they encode, and the computational approaches needed to derive such information. A comparative view of bacterial and archaeal genomes, and how information is encoded differently in them, is also presented. Combining theoretical discussions and computational techniques, the book serves as a valuable introductory textbook for graduate-level microbial genomics and informatics courses.

Computational Methods for the Analysis of Genomic Data and Biological Processes

Computational Methods for the Analysis of Genomic Data and Biological Processes PDF Author: Francisco A. Gómez Vela
Publisher: MDPI
ISBN: 3039437712
Category : Medical
Languages : en
Pages : 222

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Book Description
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.

Computational Epigenetics and Diseases

Computational Epigenetics and Diseases PDF Author:
Publisher: Academic Press
ISBN: 0128145145
Category : Medical
Languages : en
Pages : 452

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Book Description
Computational Epigenetics and Diseases, written by leading scientists in this evolving field, provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases. In particular, the major computational tools, databases, and strategies for computational epigenetics analysis, for example, DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA, are summarized, in the context of human diseases. This book discusses bioinformatics methods for epigenetic analysis specifically applied to human conditions such as aging, atherosclerosis, diabetes mellitus, schizophrenia, bipolar disorder, Alzheimer disease, Parkinson disease, liver and autoimmune disorders, and reproductive and respiratory diseases. Additionally, different organ cancers, such as breast, lung, and colon, are discussed. This book is a valuable source for graduate students and researchers in genetics and bioinformatics, and several biomedical field members interested in applying computational epigenetics in their research. - Provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases - Summarizes the major computational tools, databases, and strategies for computational epigenetics analysis, such as DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA - Covers the major milestones and future directions of computational epigenetics in various kinds of human diseases such as aging, atherosclerosis, diabetes, heart disease, neurological disorders, cancers, blood disorders, liver diseases, reproductive diseases, respiratory diseases, autoimmune diseases, human imprinting disorders, and infectious diseases

Impact of Advances in Computing and Communications Technologies on Chemical Science and Technology

Impact of Advances in Computing and Communications Technologies on Chemical Science and Technology PDF Author: National Research Council
Publisher: National Academies Press
ISBN: 0309184029
Category : Mathematics
Languages : en
Pages : 235

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Book Description
The Chemical Sciences Roundtable provides a forum for discussing chemically related issues affecting government, industry and government. The goal is to strengthen the chemical sciences by foster communication among all the important stakeholders. At a recent Roundtable meeting, information technology was identified as an issue of increasing importance to all sectors of the chemical enterprise. This book is the result of a workshop convened to explore this topic.

Identifying and Characterizing the Genomic Signatures of Natural Selection

Identifying and Characterizing the Genomic Signatures of Natural Selection PDF Author: Roy Ronen
Publisher:
ISBN: 9781321452006
Category :
Languages : en
Pages : 142

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Book Description
Despite being founded in the early 1920's, the field of Population and Evolutionary Genetics is currently in its second life. This is primarily driven by the recent data influx from genomic studies of ever-increasing size. The shear amount and complexity of data produced by these studies is also creating a need for improved computational techniques to be used for analysis and inference. In this thesis, I present three computational methods that are aimed at improving our understanding of genetic variation in natural populations. First, I present an algorithm for improving the accuracy of genome assemblies using the positional de Bruijn graph. I show that, using the original sequence reads in conjunction with a novel data structure, I can significantly improve the accuracy of assembled draft genomes. Necessarily, this leads to improved accuracy of all downstream inferences that use the draft as a reference, including gene discovery, transcript expression, variant calling, and many others. Second, I describe a computational framework that uses supervised learning of mutation frequency profiles to identify genomic regions impacted by positive natural selection. This is desirable, as it allows pinpointing and understanding the mechanisms responsible for adaptive traits, such as lactose tolerance in northern European populations, hypoxia tolerance in high altitude populations, and malaria resistance in African populations. Extending the widely used theoretical framework of the site frequency spectrum (SFS), I show that higher power to detect selection is achieved by training parameter-specific models of the SFS. I further show that these models can be generalized, allowing their use without prior knowledge. Last, I describe a new statistic that naturally captures many of the properties shared by haplotypes carrying an adaptive allele. I provide a theoretical model for the behavior of the statistic under different demographic and evolutionary scenarios, and validate the model using simulated data. Using this framework, I develop an algorithm that - given a region we know to be under positive selection - predicts carriers of the adaptive mutation without knowing its position. I demonstrate its high accuracy on simulated data, as well as on genetic data from well-known instances of positive selection in human populations.