| Chapter title |
The FANTOM5 Computation Ecosystem: Genomic Information Hub for Promoters and Active Enhancers
|
|---|---|
| Chapter number | 15 |
| Book title |
Protein Function Prediction
|
| Published in |
Methods in molecular biology, April 2017
|
| DOI | 10.1007/978-1-4939-7015-5_15 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7013-1, 978-1-4939-7015-5, 978-1-4939-7013-1, 978-1-4939-7015-5
|
| Authors |
Imad Abugessaisa, Shuhei Noguchi, Piero Carninci, Takeya Kasukawa |
| Editors |
Daisuke Kihara |
| Abstract |
The Functional Annotation of the Mammalian Genome 5 (FANTOM5) project conducted transcriptome analysis of various mammalian cell types and provided a comprehensive resource to understand transcriptome and transcriptional regulation in individual cellular states encoded in the genome.FANTOM5 used cap analysis of gene expression (CAGE) with single-molecule sequencing to map transcription start sites (TSS) and measured their expression in a diverse range of samples. The main results from FANTOM5 were published as a promoter-level mammalian expression atlas and an atlas of active enhancers across human cell types. The FANTOM5 dataset is composed of raw experimental data and the results of bioinformatics analyses. In this chapter, we give a detailed description of the content of the FANTOM5 dataset and elaborate on different computing applications developed to publish the data and enable reproducibility and discovery of new findings. We present use cases in which the FANTOM5 dataset has been reused, leading to new findings. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 25% |
| Denmark | 1 | 8% |
| United Kingdom | 1 | 8% |
| India | 1 | 8% |
| Russia | 1 | 8% |
| Japan | 1 | 8% |
| Unknown | 4 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 58% |
| Members of the public | 5 | 42% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 24 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 46% |
| Researcher | 3 | 13% |
| Other | 2 | 8% |
| Student > Bachelor | 2 | 8% |
| Lecturer | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 2 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 33% |
| Agricultural and Biological Sciences | 5 | 21% |
| Computer Science | 3 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Neuroscience | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 4 | 17% |