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Mendeley readers
Attention Score in Context
| Title |
Eulertigs: minimum plain text representation of k-mer sets without repetitions in linear time
|
|---|---|
| Published in |
Algorithms for Molecular Biology, July 2023
|
| DOI | 10.1186/s13015-023-00227-1 |
| Pubmed ID | |
| Authors |
Sebastian Schmidt, Jarno N. Alanko |
| Abstract |
A fundamental operation in computational genomics is to reduce the input sequences to their constituent k-mers. For maximum performance of downstream applications it is important to store the k-mers in small space, while keeping the representation easy and efficient to use (i.e. without k-mer repetitions and in plain text). Recently, heuristics were presented to compute a near-minimum such representation. We present an algorithm to compute a minimum representation in optimal (linear) time and use it to evaluate the existing heuristics. Our algorithm first constructs the de Bruijn graph in linear time and then uses a Eulerian-cycle-based algorithm to compute the minimum representation, in time linear in the size of the output. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 50% |
| Student > Bachelor | 1 | 25% |
| Other | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 75% |
| Chemical Engineering | 1 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 06 July 2023.
All research outputs
#15,127,047
of 24,024,220 outputs
Outputs from Algorithms for Molecular Biology
#113
of 253 outputs
Outputs of similar age
#83,846
of 178,507 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 3 outputs
Altmetric has tracked 24,024,220 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 253 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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 178,507 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.