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Mendeley readers
Attention Score in Context
| Title |
A representation of a compressed de Bruijn graph for pan-genome analysis that enables search
|
|---|---|
| Published in |
Algorithms for Molecular Biology, July 2016
|
| DOI | 10.1186/s13015-016-0083-7 |
| Pubmed ID | |
| Authors |
Timo Beller, Enno Ohlebusch |
| Abstract |
Recently, Marcus et al. (Bioinformatics 30:3476-83, 2014) proposed to use a compressed de Bruijn graph to describe the relationship between the genomes of many individuals/strains of the same or closely related species. They devised an [Formula: see text] time algorithm called splitMEM that constructs this graph directly (i.e., without using the uncompressed de Bruijn graph) based on a suffix tree, where n is the total length of the genomes and g is the length of the longest genome. Baier et al. (Bioinformatics 32:497-504, 2016) improved their result. In this paper, we propose a new space-efficient representation of the compressed de Bruijn graph that adds the possibility to search for a pattern (e.g. an allele-a variant form of a gene) within the pan-genome. The ability to search within the pan-genome graph is of utmost importance and is a design goal of pan-genome data structures. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 3 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 2% |
| Unknown | 41 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 13 | 31% |
| Student > Ph. D. Student | 10 | 24% |
| Researcher | 7 | 17% |
| Professor | 3 | 7% |
| Student > Bachelor | 2 | 5% |
| Other | 2 | 5% |
| Unknown | 5 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 13 | 31% |
| Computer Science | 12 | 29% |
| Agricultural and Biological Sciences | 8 | 19% |
| Medicine and Dentistry | 2 | 5% |
| Neuroscience | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 5 | 12% |
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 07 August 2016.
All research outputs
#16,720,137
of 25,371,288 outputs
Outputs from Algorithms for Molecular Biology
#134
of 265 outputs
Outputs of similar age
#237,891
of 377,559 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 5 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 265 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 44th percentile – i.e., 44% 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 377,559 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.