--- product_id: 7193859 title: "RNA-seq Data Analysis (Chapman & Hall/CRC Computational Biology Series)" price: "€ 147.82" currency: EUR in_stock: true reviews_count: 13 url: https://www.desertcart.pt/products/7193859-rna-seq-data-analysis-chapman-and-hall-crc-computational-biology store_origin: PT region: Portugal --- # RNA-seq Data Analysis (Chapman & Hall/CRC Computational Biology Series) **Price:** € 147.82 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** RNA-seq Data Analysis (Chapman & Hall/CRC Computational Biology Series) - **How much does it cost?** € 147.82 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.pt](https://www.desertcart.pt/products/7193859-rna-seq-data-analysis-chapman-and-hall-crc-computational-biology) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description The State of the Art in Transcriptome Analysis RNA 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 levels and to discover novel genes, transcripts, and whole transcriptomes. Balanced Coverage of Theory and Practice. Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools and practical examples. Accessible to both bioinformaticians and nonprogramming wet lab scientists, the examples illustrate the use of command-line tools, R, and other open source tools, such as the graphical Chipster software. The Tools and Methods to Get Started in Your Lab. Taking readers through the whole data analysis workflow, this self-contained guide provides a detailed overview of the main RNA-seq data analysis methods and explains how to use them in practice. It is suitable for researchers from a wide variety of backgrounds, including biology, medicine, genetics, and computer science. The book can also be used in a graduate or advanced undergraduate course. Review: Excellent, detailed introduction to RNAseq technology and application - Having now purchased a few other books on this topic from desertcart, I have to say this one is the best if you need an introduction to the field. The others could be 1) downloaded from your university journal subscription, and 2) focus much more on theory and suited better suited for those already familiar with the topic. They could still be useful but I doubt you would use them by themselves - you would probably find yourself looking up a lot of other information online or consulting other books. In contrast, this book is very self-contained. It covers all the basics of RNAseq analysis with a pretty detailed look at a typical pipeline. It covers many different available tools and even has a step-by-step code approach for using many of the common/popular tools. Most of the book uses either R or Bash for the code. It covers, RNA isolation techniques/QC, library prep methods, different sequencing platforms and how to choose, overview of RNAseq applications, preprocessing reads/QC, alignment, transcriptome assembly (including de novo), quantitation, Bioconductor packages, differential gene expression, differential exon usage analysis, annotation, visualization, and small/noncoding RNAseq analysis. I was happy to see that it covers a lot of the QC metrics, what they mean, and in what context they are important. Overall, this is a very thorough book. As a beginners guide it will get you the furthest compared to the other books currently available as of this writing. It will easily get you to that point where you are comfortable enough with the terminology and general pipeline for you to easily search for the answer to more detailed and specific questions online which is the biggest hurdle for this field. I would recommend the following papers to compliment this book: "Count-based differential expression analysis of RNA seqencing data using R and Bioconductor" by Anders et al. 2013 in Nature Protocols - a step-by-step code-based analysis guide that uses EdgeR/DEseq2 "A survey of best practices for RNA-seq data analysis" by Conesa et al. 2016 in Genome Biology - a good summary of the basic metrics for QC and experimental design Review: Good for learning. Good for teaching - I teach 2 undergraduate courses in Bioinformatics and I'm constantly looking for books on the subject, but I haven't seen other books as practical and comprehensive as this one in terms of RNA-seq bioinformatic analysis. It talks a bit about theoretical issues but most of it is pure practice including command lines, data sets for download and comparison of the different software that can be used. It's very good, as RNA-seq Analysis (considering all the changes bioinformatics undergo in time) has become more or less a standard cook recipe. The index is very good in the sense that the book takes you step by step if you are learning. I'm thinking on using at least a few chapters as teaching material in my Bioinformatics course this semester. The few commands I have tried so far, have worked. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #1,256,991 in Books ( See Top 100 in Books ) #117 in Bioinformatics (Books) #699 in Genetics (Books) #1,803 in Biology & Life Sciences | | Customer Reviews | 4.2 out of 5 stars 32 Reviews | ## Images ![RNA-seq Data Analysis (Chapman & Hall/CRC Computational Biology Series) - Image 1](https://m.media-amazon.com/images/I/61Uyx8QZa5L.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Excellent, detailed introduction to RNAseq technology and application *by S***N on July 23, 2016* Having now purchased a few other books on this topic from Amazon, I have to say this one is the best if you need an introduction to the field. The others could be 1) downloaded from your university journal subscription, and 2) focus much more on theory and suited better suited for those already familiar with the topic. They could still be useful but I doubt you would use them by themselves - you would probably find yourself looking up a lot of other information online or consulting other books. In contrast, this book is very self-contained. It covers all the basics of RNAseq analysis with a pretty detailed look at a typical pipeline. It covers many different available tools and even has a step-by-step code approach for using many of the common/popular tools. Most of the book uses either R or Bash for the code. It covers, RNA isolation techniques/QC, library prep methods, different sequencing platforms and how to choose, overview of RNAseq applications, preprocessing reads/QC, alignment, transcriptome assembly (including de novo), quantitation, Bioconductor packages, differential gene expression, differential exon usage analysis, annotation, visualization, and small/noncoding RNAseq analysis. I was happy to see that it covers a lot of the QC metrics, what they mean, and in what context they are important. Overall, this is a very thorough book. As a beginners guide it will get you the furthest compared to the other books currently available as of this writing. It will easily get you to that point where you are comfortable enough with the terminology and general pipeline for you to easily search for the answer to more detailed and specific questions online which is the biggest hurdle for this field. I would recommend the following papers to compliment this book: "Count-based differential expression analysis of RNA seqencing data using R and Bioconductor" by Anders et al. 2013 in Nature Protocols - a step-by-step code-based analysis guide that uses EdgeR/DEseq2 "A survey of best practices for RNA-seq data analysis" by Conesa et al. 2016 in Genome Biology - a good summary of the basic metrics for QC and experimental design ### ⭐⭐⭐⭐⭐ Good for learning. Good for teaching *by C***K on August 16, 2015* I teach 2 undergraduate courses in Bioinformatics and I'm constantly looking for books on the subject, but I haven't seen other books as practical and comprehensive as this one in terms of RNA-seq bioinformatic analysis. It talks a bit about theoretical issues but most of it is pure practice including command lines, data sets for download and comparison of the different software that can be used. It's very good, as RNA-seq Analysis (considering all the changes bioinformatics undergo in time) has become more or less a standard cook recipe. The index is very good in the sense that the book takes you step by step if you are learning. I'm thinking on using at least a few chapters as teaching material in my Bioinformatics course this semester. The few commands I have tried so far, have worked. ### ⭐⭐⭐⭐⭐ Five Stars *by J***N on August 23, 2017* The best RNA-seq book in the market. ## Frequently Bought Together - RNA-seq Data Analysis: A Practical Approach (Chapman & Hall/CRC Computational Biology Series) - RNA Sequencing: Principles and Data Analysis - R Bioinformatics Cookbook: Utilize R packages for bioinformatics, genomics, data science, and machine learning --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.pt/products/7193859-rna-seq-data-analysis-chapman-and-hall-crc-computational-biology](https://www.desertcart.pt/products/7193859-rna-seq-data-analysis-chapman-and-hall-crc-computational-biology) --- *Product available on Desertcart Portugal* *Store origin: PT* *Last updated: 2026-05-12*