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StreAM- $$T_g$$ T g : algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs

Overview of attention for article published in Algorithms for Molecular Biology, May 2017
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Title
StreAM- $$T_g$$ T g : 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 100%
Researcher 1 33%
Student > Ph. D. Student 1 33%
Readers by discipline Count As %
Computer Science 2 67%
Chemistry 2 67%
Agricultural and Biological Sciences 1 33%

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
#8,994,587
of 11,251,036 outputs
Outputs from Algorithms for Molecular Biology
#116
of 176 outputs
Outputs of similar age
#192,220
of 267,746 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 5 outputs
Altmetric has tracked 11,251,036 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 176 research outputs from this source. They receive a mean Attention Score of 2.8. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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