Title |
SNES: single nucleus exome sequencing
|
---|---|
Published in |
Genome Biology, March 2015
|
DOI | 10.1186/s13059-015-0616-2 |
Pubmed ID | |
Authors |
Marco L Leung, Yong Wang, Jill Waters, Nicholas E Navin |
Abstract |
Single-cell genome sequencing methods are challenged by poor physical coverage and high error rates, making it difficult to distinguish real biological variants from technical artifacts. To address this problem, we developed a method called SNES that combines flow-sorting of single G1/0 or G2/M nuclei, time-limited multiple-displacement-amplification, exome capture, and next-generation sequencing to generate high coverage (96%) data from single human cells. We validated our method in a fibroblast cell line, and show low allelic dropout and false-positive error rates, resulting in high detection efficiencies for single nucleotide variants (92%) and indels (85%) in single cells. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 33% |
India | 3 | 10% |
United Kingdom | 3 | 10% |
Australia | 1 | 3% |
Ireland | 1 | 3% |
Malaysia | 1 | 3% |
Austria | 1 | 3% |
Saudi Arabia | 1 | 3% |
Unknown | 9 | 30% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 16 | 53% |
Members of the public | 13 | 43% |
Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 2 | 1% |
United States | 2 | 1% |
Germany | 1 | <1% |
Korea, Republic of | 1 | <1% |
Czechia | 1 | <1% |
South Africa | 1 | <1% |
China | 1 | <1% |
United Kingdom | 1 | <1% |
Unknown | 170 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 49 | 27% |
Researcher | 44 | 24% |
Student > Bachelor | 17 | 9% |
Student > Master | 16 | 9% |
Student > Doctoral Student | 14 | 8% |
Other | 21 | 12% |
Unknown | 19 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 66 | 37% |
Biochemistry, Genetics and Molecular Biology | 51 | 28% |
Computer Science | 10 | 6% |
Medicine and Dentistry | 9 | 5% |
Neuroscience | 6 | 3% |
Other | 13 | 7% |
Unknown | 25 | 14% |