Transcriptome Data Analysis / edited by Rajeev K. Azad.

Format:

Book

Contributor:

Azad, Rajeev K., Editor.

Series:

Methods in Molecular Biology, 1940-6029 ; 2812

Language:

English

Subjects (All):

Genetic transcription.

Quantitative research.

Gene Transcription.

Data Analysis and Big Data.

Local Subjects:

Gene Transcription.

Data Analysis and Big Data.

Physical Description:

1 online resource (XI, 394 p. 79 illus., 76 illus. in color.)

Edition:

1st ed. 2024.

Place of Publication:

New York, NY : Springer US : Imprint: Humana, 2024.

Summary:

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.

Contents:

An RNA-Seq Data Analysis Pipeline

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications

EMPathways2: Estimation of Enzyme Expression and Metabolic Pathway Activity Using RNA-Seq Reads

Efficient and Powerful Integration of Targeted Metabolomics and Transcriptomics for Analyzing the Metabolism Behind Desirable Traits in Plants

A RNAseq Data Analysis for Differential Gene Expression Using HISAT2-stringTie-Ballgown Pipeline

RNA-Sequencing Experimental Analysis Workflow Using Caenorhabditis elegans

Inferring Novel Cells in Single Cell RNA Sequencing Data

Unsupervised Single-Cell Clustering with Asymmetric Within-Sample Transformation and Per Cluster Supervised Features Selection

Inferring Tree-Shaped Single-Cell Trajectories with Totem

Zebrafish Thrombocyte Transcriptome Analysis and Functional Genomics

Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines

Cotton Meristem Transcriptomes: Constructing an RNA-Seq Pipeline to Explore Crop Architecture Regulation

Detecting Somatic Insertions/Deletions (Indels) Using Tumor RNA-Seq Data

A Protocol for the Detection of Fusion Transcripts Using RNA-Sequencing Data

GAN Learning Methods for Bulk RNA-Seq Data and Their Interpretive Application in the Context of Disease Progression

Protocol for Analyzing Epigenetic Regulation Mechanisms in Breast Cancer

Identification of Virus-Derived Small Interfering RNAs (vsiRNAs) from Infected sRNA-Seq Samples

Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis

Utilizing RNA-Seq Data to Infer Bacterial Transcription Termination Sites and Validate Predictions

RNA-Seq Analysis of Mammalian Prion Disease

In Silico Identification of tRNA Fragments, Novel Candidates for Cancer Biomarkers, and Therapeutic Targets.

ISBN:

9781071638866