Persistent Homology Analysis of RNA

Adane L. Mamuye, et al. "Persistent Homology Analysis of RNA." Molecular Based Mathematical Biology 4.1 (2016): null. <http://eudml.org/doc/287086>.

@article{AdaneL2016,
abstract = {Topological data analysis has been recently used to extract meaningful information frombiomolecules. Here we introduce the application of persistent homology, a topological data analysis tool, for computing persistent features (loops) of the RNA folding space. The scaffold of the RNA folding space is a complex graph from which the global features are extracted by completing the graph to a simplicial complex via the notion of clique and Vietoris-Rips complexes. The resulting simplicial complexes are characterised in terms of topological invariants, such as the number of holes in any dimension, i.e. Betti numbers. Our approach discovers persistent structural features, which are the set of smallest components to which the RNA folding space can be reduced. Thanks to this discovery, which in terms of data mining can be considered as a space dimension reduction, it is possible to extract a new insight that is crucial for understanding the mechanism of the RNA folding towards the optimal secondary structure. This structure is composed by the components discovered during the reduction step of the RNA folding space and is characterized by minimum free energy.},
author = {Adane L. Mamuye, Matteo Rucco, Luca Tesei, Emanuela Merelli},
journal = {Molecular Based Mathematical Biology},
keywords = {RNA folding space; persistent homology; persistent structural features},
language = {eng},
number = {1},
pages = {null},
title = {Persistent Homology Analysis of RNA},
url = {http://eudml.org/doc/287086},
volume = {4},
year = {2016},
}

TY - JOUR
AU - Adane L. Mamuye
AU - Matteo Rucco
AU - Luca Tesei
AU - Emanuela Merelli
TI - Persistent Homology Analysis of RNA
JO - Molecular Based Mathematical Biology
PY - 2016
VL - 4
IS - 1
SP - null
AB - Topological data analysis has been recently used to extract meaningful information frombiomolecules. Here we introduce the application of persistent homology, a topological data analysis tool, for computing persistent features (loops) of the RNA folding space. The scaffold of the RNA folding space is a complex graph from which the global features are extracted by completing the graph to a simplicial complex via the notion of clique and Vietoris-Rips complexes. The resulting simplicial complexes are characterised in terms of topological invariants, such as the number of holes in any dimension, i.e. Betti numbers. Our approach discovers persistent structural features, which are the set of smallest components to which the RNA folding space can be reduced. Thanks to this discovery, which in terms of data mining can be considered as a space dimension reduction, it is possible to extract a new insight that is crucial for understanding the mechanism of the RNA folding towards the optimal secondary structure. This structure is composed by the components discovered during the reduction step of the RNA folding space and is characterized by minimum free energy.
LA - eng
KW - RNA folding space; persistent homology; persistent structural features
UR - http://eudml.org/doc/287086
ER -