Transcriptome Data Analysis

by Rajeev K. Azad (Editor)

Overview

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 prools, 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 prools 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.

Product Details

ISBN-13:	9781071638859
Publisher:	Springer US
Publication date:	07/28/2024
Series:	Methods in Molecular Biology , #2812
Edition description:	2024
Pages:	394
Product dimensions:	7.01(w) x 10.00(h) x (d)

Table of 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 Prool 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.- Prool 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.