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Attention Score in Context
| Title |
Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing
|
|---|---|
| Published in |
Giga Science, August 2015
|
| DOI | 10.1186/s13742-015-0068-3 |
| Pubmed ID | |
| Authors |
Yong Hou, Kui Wu, Xulian Shi, Fuqiang Li, Luting Song, Hanjie Wu, Michael Dean, Guibo Li, Shirley Tsang, Runze Jiang, Xiaolong Zhang, Bo Li, Geng Liu, Niharika Bedekar, Na Lu, Guoyun Xie, Han Liang, Liao Chang, Ting Wang, Jianghao Chen, Yingrui Li, Xiuqing Zhang, Huanming Yang, Xun Xu, Ling Wang, Jun Wang |
| Abstract |
Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed. We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2). Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level. |
X Demographics
The data shown below were collected from the profiles of 16 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 | 2 | 13% |
| China | 2 | 13% |
| United Kingdom | 2 | 13% |
| Spain | 2 | 13% |
| France | 1 | 6% |
| Denmark | 1 | 6% |
| Hong Kong | 1 | 6% |
| Unknown | 5 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 63% |
| Scientists | 5 | 31% |
| Science communicators (journalists, bloggers, editors) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 222 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | <1% |
| United Kingdom | 1 | <1% |
| China | 1 | <1% |
| Japan | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 217 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 53 | 24% |
| Researcher | 45 | 20% |
| Student > Master | 25 | 11% |
| Student > Bachelor | 23 | 10% |
| Other | 13 | 6% |
| Other | 35 | 16% |
| Unknown | 28 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 71 | 32% |
| Biochemistry, Genetics and Molecular Biology | 70 | 32% |
| Medicine and Dentistry | 14 | 6% |
| Engineering | 11 | 5% |
| Computer Science | 5 | 2% |
| Other | 22 | 10% |
| Unknown | 29 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 08 May 2016.
All research outputs
#2,581,551
of 25,517,918 outputs
Outputs from Giga Science
#523
of 1,172 outputs
Outputs of similar age
#31,974
of 275,722 outputs
Outputs of similar age from Giga Science
#12
of 17 outputs
Altmetric has tracked 25,517,918 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,172 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.7. This one has gotten more attention than average, scoring higher than 55% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 275,722 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.