Chapter title |
Enhancer RNAs
|
---|---|
Chapter number | 14 |
Book title |
Enhancer RNAs
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-4035-6_14 |
Pubmed ID | |
Book ISBNs |
978-1-4939-4033-2, 978-1-4939-4035-6
|
Authors |
Djebali, Sarah, Wucher, Valentin, Foissac, Sylvain, Hitte, Christophe, Corre, Evan, Derrien, Thomas, Sarah Djebali Ph.D., Valentin Wucher, Sylvain Foissac, Christophe Hitte, Evan Corre, Thomas Derrien Ph.D., Erwan Corre, Sarah Djebali, Thomas Derrien |
Editors |
Ulf Andersson Ørom |
Abstract |
The development of High Throughput Sequencing (HTS) for RNA profiling (RNA-seq) has shed light on the diversity of transcriptomes. While RNA-seq is becoming a de facto standard for monitoring the population of expressed transcripts in a given condition at a specific time, processing the huge amount of data it generates requires dedicated bioinformatics programs. Here, we describe a standard bioinformatics protocol using state-of-the-art tools, the STAR mapper to align reads onto a reference genome, Cufflinks to reconstruct the transcriptome, and RSEM to quantify expression levels of genes and transcripts. We present the workflow using human transcriptome sequencing data from two biological replicates of the K562 cell line produced as part of the ENCODE3 project. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 14% |
France | 1 | 14% |
China | 1 | 14% |
United Kingdom | 1 | 14% |
Unknown | 3 | 43% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 57% |
Scientists | 3 | 43% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
China | 1 | 1% |
Unknown | 87 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 19 | 22% |
Student > Ph. D. Student | 12 | 14% |
Student > Master | 10 | 11% |
Student > Bachelor | 6 | 7% |
Student > Doctoral Student | 5 | 6% |
Other | 15 | 17% |
Unknown | 21 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 27 | 31% |
Biochemistry, Genetics and Molecular Biology | 24 | 27% |
Computer Science | 3 | 3% |
Immunology and Microbiology | 3 | 3% |
Medicine and Dentistry | 3 | 3% |
Other | 5 | 6% |
Unknown | 23 | 26% |