RNA-seq Data Analysis: A Practical Approach (Chapman & Hall/CRC Computational Biology Series) 1st Edition

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

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.

Good book if you want to learn about RNA seq data analysis.However it s more oriented tobioinformaticians than wet lab scientists

The best RNA-seq book in the market.

Excellent book to learn about RNA-Seq data analysis.

I'm a true novice to RNAseq data analysis, and this book has been a life saver. Coming into a project from the very beginning and with little idea of how to progress from start to finish, this book quickly and clearly describes the typical steps to doing this sort of analysis. It's very good at helping you make decisions about how to reach your goal depending on the type of starting data, for example, if you have paired end data and need trimming software that can handle it. While it doesn't include every tool out there, and may not necessarily cover your type of study (though it does cover gene abundance estimation) it will really help get you going, and there's no other book quite like it. I keep this book nearby and it help give me the confidence that I'm taking the correct steps forward in my research.

Data set in the book could not be get on the recommended website. It is useless.

This book has been highly valuable during my first real dive-into-transcriptomics. I read through the entire book prior to starting to work with this kind of data, and I have been using it as a reference since then. Warmly recommended.