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Name Description Knots [Note 1]Links References trRosettaRNA: trRosettaRNA is an algorithm for automated prediction of RNA 3D structure. It builds the RNA structure by Rosetta energy minimization, with deep learning restraints from a transformer network (RNAformer). trRosettaRNA has been validated in blind tests, including CASP15 and RNA-Puzzles, which suggests that the automated predictions ...
The ViennaRNA Package is software, a set of standalone programs and libraries used for predicting and analysing RNA nucleic acid secondary structures. [1] The source code for the package is released as free and open-source software and compiled binaries are available for the operating systems Linux, macOS, and Windows.
The Nussinov algorithm is a nucleic acid structure prediction algorithm used in computational biology to predict the folding of an RNA molecule that makes use of dynamic programming principles. [1] The algorithm was developed by Ruth Nussinov in the late 1970s.
For DNA, RNA and protein molecules up to 32MB, aligns all sequences of size K or greater. Similar alignments are grouped together for analysis. Automatic repetitive sequence filter. Both Local E. Wachtel 2017 Bioconductor Biostrings::pairwiseAlignment Dynamic programming: Both: Both + Ends-free: P. Aboyoun: 2008 BioPerl dpAlign Dynamic ...
Center for non-coding RNA in Technology and Health, University of Copenhagen: Yes Yes Yes All Yes NGG, NGA, NAG Yes Yes , [3] [4] Invitrogen TrueDesign Genome Editor Thermo Fisher Scientific: Yes Yes No 3 No NGG Yes Yes [5] Breaking-Cas Spanish National Center for Biotechnology: Yes (over 1000 genomes) Yes Yes (by weights) 4 No User ...
This template is intended for use on nucleic acid structure pages. To insert use: {{DNA RNA structure}}. On the primary, secondary, tertiary and quaternary structure pages, it displays alternative versions of the image with the relevant section highlighted. Alternatively, for the non-interactive image, use [[File:DNA RNA structure (full).png]]
Salmon is a software tool for computing transcript abundance from RNA-seq data using either an alignment-free (based directly on the raw reads) or an alignment-based (based on pre-computed alignments) approach. It uses an online stochastic optimization approach to maximize the likelihood of the transcript abundances under the observed data.
The simplest way to find the lowest free energy structure would be to generate all possible structures and calculate the free energy for it, but the number of possible structures for a sequence increases exponentially with the length of RNA: number of secondary structures = (1,8) N, N- number of nucleotides. [6]