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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Transposon identification using profile HMMs
|
|---|---|
| Published in |
BMC Genomics, February 2010
|
| DOI | 10.1186/1471-2164-11-s1-s10 |
| Pubmed ID | |
| Authors |
Paul T Edlefsen, Jun S Liu |
| Abstract |
Transposons are "jumping genes" that account for large quantities of repetitive content in genomes. They are known to affect transcriptional regulation in several different ways, and are implicated in many human diseases. Transposons are related to microRNAs and viruses, and many genes, pseudogenes, and gene promoters are derived from transposons or have origins in transposon-induced duplication. Modeling transposon-derived genomic content is difficult because they are poorly conserved. Profile hidden Markov models (profile HMMs), widely used for protein sequence family modeling, are rarely used for modeling DNA sequence families. The algorithm commonly used to estimate the parameters of profile HMMs, Baum-Welch, is prone to prematurely converge to local optima. The DNA domain is especially problematic for the Baum-Welch algorithm, since it has only four letters as opposed to the twenty residues of the amino acid alphabet. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 13% |
| Brazil | 2 | 6% |
| Canada | 1 | 3% |
| Denmark | 1 | 3% |
| Egypt | 1 | 3% |
| Unknown | 23 | 72% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 44% |
| Student > Ph. D. Student | 5 | 16% |
| Student > Master | 5 | 16% |
| Professor > Associate Professor | 2 | 6% |
| Professor | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 25 | 78% |
| Computer Science | 3 | 9% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Neuroscience | 1 | 3% |
| Unknown | 2 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 24 May 2014.
All research outputs
#20,656,161
of 25,374,647 outputs
Outputs from BMC Genomics
#8,709
of 11,244 outputs
Outputs of similar age
#160,546
of 174,317 outputs
Outputs of similar age from BMC Genomics
#53
of 63 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 12th percentile – i.e., 12% 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 174,317 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.