Basin hopping graph framework: Download
We introduced the basin hopping graph
(BHG) to capture more information regarding adjacency between
LMs. Nodes in the BHG are local minima (LMs), and two LMs are neighbored
only if the direct transition between their corresponding basins
are 'energetically favorable'. The corresponding saddle height is
annotated on the edge. In this abstraction, possible folding pathways
are represented as sequences of adjacent basins represented by their
LMs. The BHG is particularly suitable to describe the ruggedness of
RNA folding landscapes and to explain the interconversion between
multiple 'active' LMs as observed by Solomatin et al. (2010). Details see homepage of BHG.
If you think it is helpful to your research, please do
not forget to cite us when you achieve your success!
Marcel Kucharik, Ivo L. Hofacker, Peter F. Stadler and Jing Qin
Basin Hopping Graph: A computational framework to characterize RNA folding landscapes
Bioinformatics (2014) 30 (14): 2009-2017.
Marcel Kucharik, Ivo L. Hofacker, Peter F. Stadler and Jing Qin
Psedudoknots in RNA folding landscapes
Bioinformatics (2015) doi: 10.1093/bioinformatics/btv572.
RNA-RNA interaction prediction: RNAripalign download
The input RNAripalign consists of two (given) multiple sequence alignments (MSA). ripalign outputs (i) the partition function, (ii) base pairing probabilities, (iii) hybrid probabilities and (iv) a set of Boltzmann-sampled suboptimal structures consisting of canonical joint structures that are compatible to the alignments. Compared to the single sequence-pair folding algorithm rip, ripalign requires negligible additional memory resource but offers much better sensitivity and specificity, once alignments of suitable quality are given. ripalign additionally allows to incorporate structure constraints as input parameters. Details see README in the package.
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not forget to cite us when you achieve your success!
Andrew X. Li, Manja Marz, Jing Qin and Christian M. Reidys
RNA-RNA interaction prediction based on multiple sequence alignments
Bioinformatics (2011) 27 (4): 456-463.
Graph distance distribution in secondary structure ensemble: RNAgraphdist download
Large RNA molecules often carry multiple functional domains whose spatial
arrangement is an important determinant of their function. Pre-mRNA
splicing, furthermore, relies on the spatial proximity of the splice
junctions that can be separated by very long introns. Similar effects
appear in the processing of RNA virus genomes. Albeit a crude measure,
the distribution of spatial distances in thermodynamic equilibrium therefore
provides useful information on the overall shape of the molecule can
provide insights into the interplay of its functional domains. Spatial
distance can be approximated by the graph-distance in RNA
secondary structure.
The RNAgraphdist calculates the equilibrium distribution of graph-distances between arbitrary pair of
nucleotides in an RNA molecule. The program reads RNA structures generated from RNAsubopt, calculates
their equilibrium distribution and outputs the graph-distances in a tab delimited list or in the terminal Details see README in the package.
If you think it is helpful to your research, please do
not forget to cite us when you achieve your success!
Jing Qin, Markus Fricke, Manja Marz, Peter F. Stadler and Rolf Backofen
Graph-distance distribution of the Boltzmann ensemble of RNA secondary structures
Algorithms for Molecular Biology 2014, 9:19.
