| Title |
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
|
|---|---|
| Published in |
Nature Biotechnology, July 2015
|
| DOI | 10.1038/nbt.3300 |
| Pubmed ID | |
| Authors |
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, Brendan J Frey |
| Abstract |
Knowing the sequence specificities of DNA- and RNA-binding proteins is essential for developing models of the regulatory processes in biological systems and for identifying causal disease variants. Here we show that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery. Using a diverse array of experimental data and evaluation metrics, we find that deep learning outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data. We call this approach DeepBind and have built a stand-alone software tool that is fully automatic and handles millions of sequences per experiment. Specificities determined by DeepBind are readily visualized as a weighted ensemble of position weight matrices or as a 'mutation map' that indicates how variations affect binding within a specific sequence. |
X Demographics
The data shown below were collected from the profiles of 321 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 States | 92 | 29% |
| India | 16 | 5% |
| Canada | 15 | 5% |
| United Kingdom | 11 | 3% |
| Spain | 8 | 2% |
| Japan | 8 | 2% |
| France | 7 | 2% |
| Australia | 5 | 2% |
| Germany | 4 | 1% |
| Other | 52 | 16% |
| Unknown | 103 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 232 | 72% |
| Scientists | 82 | 26% |
| Practitioners (doctors, other healthcare professionals) | 4 | 1% |
| Science communicators (journalists, bloggers, editors) | 3 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 2,972 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 30 | 1% |
| Spain | 7 | <1% |
| Germany | 6 | <1% |
| Japan | 6 | <1% |
| United Kingdom | 6 | <1% |
| France | 5 | <1% |
| Canada | 5 | <1% |
| Sweden | 4 | <1% |
| Korea, Republic of | 3 | <1% |
| Other | 22 | <1% |
| Unknown | 2878 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 796 | 27% |
| Researcher | 482 | 16% |
| Student > Master | 370 | 12% |
| Student > Bachelor | 242 | 8% |
| Student > Doctoral Student | 128 | 4% |
| Other | 394 | 13% |
| Unknown | 560 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 632 | 21% |
| Biochemistry, Genetics and Molecular Biology | 624 | 21% |
| Computer Science | 528 | 18% |
| Engineering | 148 | 5% |
| Medicine and Dentistry | 73 | 2% |
| Other | 328 | 11% |
| Unknown | 639 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 263. 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 02 April 2024.
All research outputs
#140,821
of 25,768,270 outputs
Outputs from Nature Biotechnology
#275
of 8,617 outputs
Outputs of similar age
#1,364
of 275,328 outputs
Outputs of similar age from Nature Biotechnology
#2
of 111 outputs
Altmetric has tracked 25,768,270 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,617 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 44.5. This one has done particularly well, scoring higher than 96% of its peers.
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We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.