Chapter title |
RNA-Seq Data Analysis: From Raw Data Quality Control to Differential Expression Analysis
|
---|---|
Chapter number | 23 |
Book title |
Plant Germline Development
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-7286-9_23 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7285-2, 978-1-4939-7286-9
|
Authors |
Weihong Qi, Ralph Schlapbach, Hubert Rehrauer |
Abstract |
As a revolutionary technology for life sciences, RNA-seq has many applications and the computation pipeline has also many variations. Here, we describe a protocol to perform RNA-seq data analysis where the aim is to identify differentially expressed genes in comparisons of two conditions. The protocol follows the recently published RNA-seq data analysis best practice and applies quality checkpoints throughout the analysis to ensure reliable data interpretation. It is written to help new RNA-seq users to understand the basic steps necessary to analyze an RNA-seq dataset properly. An extension of the protocol has been implemented as automated workflows in the R package ezRun, available also in the data analysis framework SUSHI, for reliable, repeatable, and easily interpretable analysis results. |
X Demographics
Geographical breakdown
Country | Count | As % |
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France | 2 | 67% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 68 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 15 | 22% |
Student > Ph. D. Student | 11 | 16% |
Student > Bachelor | 8 | 12% |
Researcher | 8 | 12% |
Professor > Associate Professor | 4 | 6% |
Other | 11 | 16% |
Unknown | 11 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 27 | 40% |
Agricultural and Biological Sciences | 17 | 25% |
Medicine and Dentistry | 2 | 3% |
Environmental Science | 2 | 3% |
Neuroscience | 2 | 3% |
Other | 8 | 12% |
Unknown | 10 | 15% |