Transcriptome Data Analysis
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Rna Seq Data Analysis
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Author : Eija Korpelainen
language : en
Publisher: CRC Press
Release Date : 2014-09-19
Rna Seq Data Analysis written by Eija Korpelainen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-19 with Computers categories.
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
Transcriptome Data Analysis
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Author : Rajeev K. Azad
language : en
Publisher: Springer Nature
Release Date : 2024-07-27
Transcriptome Data Analysis written by Rajeev K. Azad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-27 with Science categories.
This detailed volume presents a comprehensive exploration of the advances in transcriptomics, with a focus on methods and pipelines for transcriptome data analysis. In addition to well-established RNA sequencing (RNA-Seq) data analysis protocols, the chapters also examine specialized pipelines, such as multi-omics data integration and analysis, gene interaction network construction, single-cell trajectory inference, detection of structural variants, application of machine learning, and more. As part of the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that leads to best results in the lab. Authoritative and practical, Transcriptome Data Analysis serves as an ideal resource for educators and researchers looking to understand new developments in the field, learn usage of the protocols for transcriptome data analysis, and implement the tools or pipelines to address relevant problemsof their interest. Chapter 4 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Transcriptome Data Analysis
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Author : Yejun Wang
language : en
Publisher:
Release Date : 2018
Transcriptome Data Analysis written by Yejun Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Genetic transcription categories.
This detailed volume provides comprehensive practical guidance on transcriptome data analysis for a variety of scientific purposes. Beginning with general protocols, the collection moves on to explore protocols for gene characterization analysis with RNA-seq data as well as protocols on several new applications of transcriptome studies. Written for the highly successful Methods in Molecular Biology series, 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. Authoritative and useful, Transcriptome Data Analysis: Methods and Protocols serves as an ideal guide to the expanding purposes of this field of study.
Transcriptome Analysis
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Author : Miroslav Blumenberg
language : en
Publisher: BoD – Books on Demand
Release Date : 2019-11-20
Transcriptome Analysis written by Miroslav Blumenberg and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-20 with Medical categories.
Transcriptome analysis is the study of the transcriptome, of the complete set of RNA transcripts that are produced under specific circumstances, using high-throughput methods. Transcription profiling, which follows total changes in the behavior of a cell, is used throughout diverse areas of biomedical research, including diagnosis of disease, biomarker discovery, risk assessment of new drugs or environmental chemicals, etc. Transcriptome analysis is most commonly used to compare specific pairs of samples, for example, tumor tissue versus its healthy counterpart. In this volume, Dr. Pyo Hong discusses the role of long RNA sequences in transcriptome analysis, Dr. Shinichi describes the next-generation single-cell sequencing technology developed by his team, Dr. Prasanta presents transcriptome analysis applied to rice under various environmental factors, Dr. Xiangyuan addresses the reproductive systems of flowering plants and Dr. Sadovsky compares codon usage in conifers.
Machine Learning Based Methods For Rna Data Analysis Volume Ii
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Author : Lihong Peng
language : en
Publisher: Frontiers Media SA
Release Date : 2023-01-02
Machine Learning Based Methods For Rna Data Analysis Volume Ii written by Lihong Peng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-02 with Science categories.
Rna Seq Analysis Methods Applications And Challenges
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Author : Filippo Geraci
language : en
Publisher: Frontiers Media SA
Release Date : 2020-06-08
Rna Seq Analysis Methods Applications And Challenges written by Filippo Geraci and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-08 with categories.
Computational Methods For Multi Omics Data Analysis In Cancer Precision Medicine
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Author : Ehsan Nazemalhosseini-Mojarad
language : en
Publisher: Frontiers Media SA
Release Date : 2023-08-02
Computational Methods For Multi Omics Data Analysis In Cancer Precision Medicine written by Ehsan Nazemalhosseini-Mojarad and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-02 with Science categories.
Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.
Machine Learning Based Methods For Rna Data Analysis Volume Iii
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Author : Lihong Peng
language : en
Publisher: Frontiers Media SA
Release Date : 2023-02-17
Machine Learning Based Methods For Rna Data Analysis Volume Iii written by Lihong Peng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-17 with Science categories.
Transcriptome Profiling
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Author : Mohammad Ajmal Ali
language : en
Publisher: Elsevier
Release Date : 2022-10-07
Transcriptome Profiling written by Mohammad Ajmal Ali and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-07 with Science categories.
Transcriptome Profiling: Progress and Prospects assists readers in assessing and interpreting a large number of genes, up to and including an entire genome. It provides key insights into the latest tools and techniques used in transcriptomics and its relevant topics which can reveal a global snapshot of the complete RNA component of a cell at a given time. This snapshot, in turn, enables the distinction between different cell types, different disease states, and different time points during development. Transcriptome analysis has been a key area of biological inquiry for decades. The next-generation sequencing technologies have revolutionized transcriptomics by providing opportunities for multidimensional examinations of cellular transcriptomes in which high-throughput expression data are obtained at a single-base resolution. Transcriptome analysis has evolved from the detection of single RNA molecules to large-scale gene expression profiling and genome annotation initiatives. Written by a team of global experts, key topics in Transcriptome Profiling include transcriptome characterization, expression analysis of transcripts, transcriptome and gene regulation, transcriptome profiling and human health, medicinal plants transcriptomics, transcriptomics and genetic engineering, transcriptomics in agriculture, and phylotranscriptomics. - Presents recent development in the tools and techniques in transcriptomic characterization - Integrates expression analysis of transcripts and gene regulation - Includes the application of transcriptomics in human health, genetic engineering and agriculture
Gene Expression Data Analysis
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Author : Pankaj Barah
language : en
Publisher: CRC Press
Release Date : 2021-11-08
Gene Expression Data Analysis written by Pankaj Barah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-08 with Computers categories.
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences