You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
StreAM-Tg: algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs
|
|---|---|
| Published in |
Algorithms for Molecular Biology, May 2017
|
| DOI | 10.1186/s13015-017-0105-0 |
| Pubmed ID | |
| Authors |
Sven Jager, Benjamin Schiller, Philipp Babel, Malte Blumenroth, Thorsten Strufe, Kay Hamacher |
| Abstract |
In this work, we present a new coarse grained representation of RNA dynamics. It is based on adjacency matrices and their interactions patterns obtained from molecular dynamics simulations. RNA molecules are well-suited for this representation due to their composition which is mainly modular and assessable by the secondary structure alone. These interactions can be represented as adjacency matrices of k nucleotides. Based on those, we define transitions between states as changes in the adjacency matrices which form Markovian dynamics. The intense computational demand for deriving the transition probability matrices prompted us to develop StreAM-[Formula: see text], a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA. We benchmark StreAM-[Formula: see text] (a) for random and RNA unit sphere dynamic graphs (b) for the robustness of our method against different parameters. Moreover, we address a riboswitch design problem by applying StreAM-[Formula: see text] on six long term molecular dynamics simulation of a synthetic tetracycline dependent riboswitch (500 ns) in combination with five different antibiotics. The proposed algorithm performs well on large simulated as well as real world dynamic graphs. Additionally, StreAM-[Formula: see text] provides insights into nucleotide based RNA dynamics in comparison to conventional metrics like the root-mean square fluctuation. In the light of experimental data our results show important design opportunities for the riboswitch. |
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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 60% |
| Researcher | 1 | 20% |
| Student > Ph. D. Student | 1 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 40% |
| Chemistry | 2 | 40% |
| Agricultural and Biological Sciences | 1 | 20% |
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 03 June 2017.
All research outputs
#18,552,700
of 22,977,819 outputs
Outputs from Algorithms for Molecular Biology
#197
of 264 outputs
Outputs of similar age
#241,115
of 316,105 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 6 outputs
Altmetric has tracked 22,977,819 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.1. 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 316,105 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.