| Title |
Robust single-cell DNA methylome profiling with snmC-seq2
|
|---|---|
| Published in |
Nature Communications, September 2018
|
| DOI | 10.1038/s41467-018-06355-2 |
| Pubmed ID | |
| Authors |
Chongyuan Luo, Angeline Rivkin, Jingtian Zhou, Justin P. Sandoval, Laurie Kurihara, Jacinta Lucero, Rosa Castanon, Joseph R. Nery, António Pinto-Duarte, Brian Bui, Conor Fitzpatrick, Carolyn O’Connor, Seth Ruga, Marc E. Van Eden, David A. Davis, Deborah C. Mash, M. Margarita Behrens, Joseph R. Ecker |
| Abstract |
Single-cell DNA methylome profiling has enabled the study of epigenomic heterogeneity in complex tissues and during cellular reprogramming. However, broader applications of the method have been impeded by the modest quality of sequencing libraries. Here we report snmC-seq2, which provides improved read mapping, reduced artifactual reads, enhanced throughput, as well as increased library complexity and coverage uniformity compared to snmC-seq. snmC-seq2 is an efficient strategy suited for large-scale single-cell epigenomic studies. |
Login to access the Attention Digest and the Sentiment Analysis related to this output.
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 11 | 31% |
| United Kingdom | 3 | 9% |
| France | 2 | 6% |
| Netherlands | 2 | 6% |
| Italy | 1 | 3% |
| Czechia | 1 | 3% |
| Korea, Republic of | 1 | 3% |
| Austria | 1 | 3% |
| Switzerland | 1 | 3% |
| Other | 6 | 17% |
| Unknown | 6 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 19 | 54% |
| Scientists | 15 | 43% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 210 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 45 | 21% |
| Student > Ph. D. Student | 42 | 20% |
| Student > Bachelor | 14 | 7% |
| Student > Master | 13 | 6% |
| Student > Doctoral Student | 8 | 4% |
| Other | 25 | 12% |
| Unknown | 63 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 67 | 32% |
| Agricultural and Biological Sciences | 33 | 16% |
| Neuroscience | 12 | 6% |
| Engineering | 10 | 5% |
| Computer Science | 5 | 2% |
| Other | 17 | 8% |
| Unknown | 66 | 31% |