| Title |
Spectacle: fast chromatin state annotation using spectral learning
|
|---|---|
| Published in |
Genome Biology, February 2015
|
| DOI | 10.1186/s13059-015-0598-0 |
| Pubmed ID | |
| Authors |
Jimin Song, Kevin C Chen |
| Abstract |
Epigenomic data from ENCODE can be used to associate specific combinations of chromatin marks with regulatory elements in the human genome. Hidden Markov models and the expectation-maximization (EM) algorithm are often used to analyze epigenomic data. However, the EM algorithm can have overfitting problems in data sets where the chromatin states show high class-imbalance and it is often slow to converge. Here we use spectral learning instead of EM and find that our software Spectacle overcame these problems. Furthermore, Spectacle is able to find enhancer subtypes not found by ChromHMM but strongly enriched in GWAS SNPs. Spectacle is available at https://github.com/jiminsong/Spectacle. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 30% |
| Germany | 1 | 10% |
| Congo, The Democratic Republic of the | 1 | 10% |
| United States | 1 | 10% |
| Unknown | 4 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 60% |
| Scientists | 3 | 30% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 5% |
| Turkey | 1 | 2% |
| Norway | 1 | 2% |
| Unknown | 51 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 30% |
| Researcher | 8 | 14% |
| Student > Doctoral Student | 6 | 11% |
| Student > Bachelor | 5 | 9% |
| Professor | 4 | 7% |
| Other | 11 | 20% |
| Unknown | 5 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 30% |
| Biochemistry, Genetics and Molecular Biology | 13 | 23% |
| Computer Science | 7 | 13% |
| Mathematics | 3 | 5% |
| Medicine and Dentistry | 3 | 5% |
| Other | 5 | 9% |
| Unknown | 8 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 April 2015.
All research outputs
#4,232,887
of 25,393,528 outputs
Outputs from Genome Biology
#2,641
of 4,470 outputs
Outputs of similar age
#57,829
of 367,510 outputs
Outputs of similar age from Genome Biology
#50
of 65 outputs
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