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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Data compression for sequencing data
|
|---|---|
| Published in |
Algorithms for Molecular Biology, November 2013
|
| DOI | 10.1186/1748-7188-8-25 |
| Pubmed ID | |
| Authors |
Sebastian Deorowicz, Szymon Grabowski |
| Abstract |
: Post-Sanger sequencing methods produce tons of data, and there is a general agreement that the challenge to store and process them must be addressed with data compression. In this review we first answer the question "why compression" in a quantitative manner. Then we also answer the questions "what" and "how", by sketching the fundamental compression ideas, describing the main sequencing data types and formats, and comparing the specialized compression algorithms and tools. Finally, we go back to the question "why compression" and give other, perhaps surprising answers, demonstrating the pervasiveness of data compression techniques in computational biology. |
X Demographics
The data shown below were collected from the profiles of 8 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 | 25% |
| United Kingdom | 2 | 25% |
| Unknown | 4 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 63% |
| Members of the public | 3 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| France | 3 | 3% |
| Netherlands | 2 | 2% |
| Sweden | 2 | 2% |
| Brazil | 1 | <1% |
| Germany | 1 | <1% |
| Portugal | 1 | <1% |
| Spain | 1 | <1% |
| United Kingdom | 1 | <1% |
| Other | 2 | 2% |
| Unknown | 88 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 27% |
| Student > Ph. D. Student | 24 | 23% |
| Student > Master | 19 | 18% |
| Other | 6 | 6% |
| Student > Bachelor | 6 | 6% |
| Other | 13 | 12% |
| Unknown | 9 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 38 | 36% |
| Agricultural and Biological Sciences | 31 | 30% |
| Engineering | 12 | 11% |
| Biochemistry, Genetics and Molecular Biology | 9 | 9% |
| Physics and Astronomy | 1 | <1% |
| Other | 3 | 3% |
| Unknown | 11 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 16 November 2021.
All research outputs
#4,337,626
of 25,706,302 outputs
Outputs from Algorithms for Molecular Biology
#25
of 266 outputs
Outputs of similar age
#46,380
of 317,439 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 4 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 266 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done particularly well, scoring higher than 90% 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 317,439 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 85% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them