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Statistical Methods For Whole Transcriptome Sequencing
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Book Synopsis RNA-seq Data Analysis by : Eija Korpelainen
Download or read book RNA-seq Data Analysis written by Eija Korpelainen and published by CRC Press. This book was released on 2014-09-19 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: The State of the Art in Transcriptome AnalysisRNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript le
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 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 463 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 Biological Sequence Analysis by : Richard Durbin
Download or read book Biological Sequence Analysis written by Richard Durbin and published by Cambridge University Press. This book was released on 1998-04-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
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 Bayesian Inference for Gene Expression and Proteomics by : Kim-Anh Do
Download or read book Bayesian Inference for Gene Expression and Proteomics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2006-07-24 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Book Synopsis Deep Sequencing Data Analysis by : Noam Shomron
Download or read book Deep Sequencing Data Analysis written by Noam Shomron and published by Humana Press. This book was released on 2013-07-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new genetic revolution is fuelled by Deep Sequencing (or Next Generation Sequencing) apparatuses which, in essence, read billions of nucleotides per reaction. Effectively, when carefully planned, any experimental question which can be translated into reading nucleic acids can be applied.In Deep Sequencing Data Analysis, expert researchers in the field detail methods which are now commonly used to study the multi-facet deep sequencing data field. These included techniques for compressing of data generated, Chromatin Immunoprecipitation (ChIP-seq), and various approaches for the identification of sequence variants. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of necessary materials and reagents, step-by-step, readily reproducible protocols, and key tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Deep Sequencing Data Analysis seeks to aid scientists in the further understanding of key data analysis procedures for deep sequencing data interpretation.
Book Synopsis RNA-Seq Analysis: Methods, Applications and Challenges by : Filippo Geraci
Download or read book RNA-Seq Analysis: Methods, Applications and Challenges written by Filippo Geraci and published by Frontiers Media SA. This book was released on 2020-06-08 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Linear Models with R by : Julian J. Faraway
Download or read book Linear Models with R written by Julian J. Faraway and published by CRC Press. This book was released on 2016-04-19 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models
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 Asymptotic Statistics by : A. W. van der Vaart
Download or read book Asymptotic Statistics written by A. W. van der Vaart and published by Cambridge University Press. This book was released on 2000-06-19 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master s level statistics text, this book will also give researchers an overview of the latest research in asymptotic statistics.
Book Synopsis A First Course in Design and Analysis of Experiments by : Gary W. Oehlert
Download or read book A First Course in Design and Analysis of Experiments written by Gary W. Oehlert and published by W. H. Freeman. This book was released on 2000-01-19 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: Oehlert's text is suitable for either a service course for non-statistics graduate students or for statistics majors. Unlike most texts for the one-term grad/upper level course on experimental design, Oehlert's new book offers a superb balance of both analysis and design, presenting three practical themes to students: • when to use various designs • how to analyze the results • how to recognize various design options Also, unlike other older texts, the book is fully oriented toward the use of statistical software in analyzing experiments.
Book Synopsis Next-Generation Sequencing Data Analysis by : Xinkun Wang
Download or read book Next-Generation Sequencing Data Analysis written by Xinkun Wang and published by CRC Press. This book was released on 2016-04-06 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Practical Guide to the Highly Dynamic Area of Massively Parallel SequencingThe development of genome and transcriptome sequencing technologies has led to a paradigm shift in life science research and disease diagnosis and prevention. Scientists are now able to see how human diseases and phenotypic changes are connected to DNA mutation, polymorphi
Book Synopsis Optimal Bayesian Classification by : Lori A. Dalton
Download or read book Optimal Bayesian Classification written by Lori A. Dalton and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. This book treats classification, where the underlying random process is a feature-label distribution, and an optimal operator is a Bayes classifier, which is a classifier minimizing the classification error. With sufficient knowledge we can construct the feature-label distribution and thereby find a Bayes classifier. Rarely, do we possess such knowledge. On the other hand, if we had unlimited data, we could accurately estimate the feature-label distribution and obtain a Bayes classifier. Rarely do we possess sufficient data. The aim of this book is to best use whatever knowledge and data are available to design a classifier. The book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. In this way it takes full advantage of knowledge regarding the underlying system and the available data. Its origins lie in the need to estimate classifier error when there is insufficient data to hold out test data, in which case an optimal error estimate can be obtained relative to the uncertainty class. A natural next step is to forgo classical ad hoc classifier design and simply find an optimal classifier relative to the posterior distribution over the uncertainty class-this being an optimal Bayesian classifier"--
Book Synopsis Molecular Infection Biology by : Jörg Hinrich Hacker
Download or read book Molecular Infection Biology written by Jörg Hinrich Hacker and published by Wiley-Spektrum. This book was released on 2002-10-03 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive volume focuses on molecular methods and principles of prokaryotic and eukaryotic pathogens. The authors present the molecular and cellular aspects by focusing on the interactions between pathogenic microorganisms and their hosts. The publication begins with an overview of the most important and dangerous causative agents of infectious diseases. Next are discussions of how microbial "weapons," pathogenicity factors, protein secretion machines, and surface variation systems work, presenting the molecular and genetic methods that are used by scientists for their discovery and analysis. Furthermore, infectious diseases are discussed in light of the newly formed research areas of evolutionary and cellular microbiology and genomics. Future aspects on diagnostic techniques, therapy, and vaccine development are also presented.
Book Synopsis RNA Methylation by : Alexandra Lusser
Download or read book RNA Methylation written by Alexandra Lusser and published by Humana Press. This book was released on 2017-03-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides a comprehensive collection of current methods and protocols to study posttranscriptional base modifications in RNA with special focus on methylation. The protocols in this book discuss state-of-the-art methods for investigating aspects of RNA methylation on different types of RNA. The protocols cover topics such as wet-lab techniques for the detection of methylation, instructions for bioinformatics analyses of transcriptome-scale data, and protocols for the functional examination of RNA modifications and enzymes. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, RNA Methylation: Methods and Protocols is a valuable resource for biochemists and molecular biologists, from various fields, who wish to investigate different types of RNA methylations.
Book Synopsis RSEM: Accurate Transcript Quantification from RNA-Seq Data with Or Without a Reference Genome by : Applied Research Applied Research Press
Download or read book RSEM: Accurate Transcript Quantification from RNA-Seq Data with Or Without a Reference Genome written by Applied Research Applied Research Press and published by . This book was released on 2015-09-16 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
