| Title |
DNA methylome analysis using short bisulfite sequencing data
|
|---|---|
| Published in |
Nature Methods, January 2012
|
| DOI | 10.1038/nmeth.1828 |
| Pubmed ID | |
| Authors |
Felix Krueger, Benjamin Kreck, Andre Franke, Simon R Andrews |
| Abstract |
Bisulfite conversion of genomic DNA combined with next-generation sequencing (BS-seq) is widely used to measure the methylation state of a whole genome, the methylome, at single-base resolution. However, analysis of BS-seq data still poses a considerable challenge. Here we summarize the challenges of BS-seq mapping as they apply to both base and color-space data. We also explore the effect of sequencing errors and contaminants on inferred methylation levels and recommend the most appropriate way to analyze this type of data. |
X Demographics
The data shown below were collected from the profiles of 7 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 | 43% |
| Netherlands | 1 | 14% |
| Japan | 1 | 14% |
| Unknown | 2 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 57% |
| Members of the public | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,058 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 21 | 2% |
| United Kingdom | 10 | <1% |
| Germany | 9 | <1% |
| France | 6 | <1% |
| Japan | 5 | <1% |
| Sweden | 5 | <1% |
| Spain | 4 | <1% |
| Belgium | 3 | <1% |
| Italy | 3 | <1% |
| Other | 18 | 2% |
| Unknown | 974 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 283 | 27% |
| Student > Ph. D. Student | 278 | 26% |
| Student > Master | 115 | 11% |
| Student > Bachelor | 73 | 7% |
| Student > Doctoral Student | 44 | 4% |
| Other | 145 | 14% |
| Unknown | 120 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 544 | 51% |
| Biochemistry, Genetics and Molecular Biology | 211 | 20% |
| Computer Science | 44 | 4% |
| Medicine and Dentistry | 38 | 4% |
| Immunology and Microbiology | 12 | 1% |
| Other | 69 | 7% |
| Unknown | 140 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 15 March 2022.
All research outputs
#2,536,529
of 26,017,215 outputs
Outputs from Nature Methods
#2,516
of 5,404 outputs
Outputs of similar age
#18,871
of 259,104 outputs
Outputs of similar age from Nature Methods
#26
of 80 outputs
Altmetric has tracked 26,017,215 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 5,404 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 36.7. This one has gotten more attention than average, scoring higher than 53% 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 259,104 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.