menu
{ "item_title" : "Transcriptome Data Analysis", "item_author" : [" Rajeev K. Azad "], "item_description" : "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. ", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/07/163/885/1071638858_b.jpg", "price_data" : { "retail_price" : "249.99", "online_price" : "249.99", "our_price" : "249.99", "club_price" : "249.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Transcriptome Data Analysis|Rajeev K. Azad

Transcriptome Data Analysis

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

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 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.

This item is Non-Returnable

Details

  • ISBN-13: 9781071638859
  • ISBN-10: 1071638858
  • Publisher: Humana
  • Publish Date: July 2024
  • Dimensions: 10 x 7 x 0.94 inches
  • Shipping Weight: 2.03 pounds
  • Page Count: 394

Related Categories

You May Also Like...

    1

BAM Customer Reviews