NUPACK: Analysis and design of nucleic acid systems
By J. Zadeh, C. Steenberg, Justin S. Bois, Brian R. Wolfe, Marshall B. Pierce, Asif R. Khan, Robert M. Dirks, Niles A. Pierce. Journal of Computational Chemistry, 2011
1,650 citations recorded by the source corpus.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/ef9ed896d4d4f37dde501e70400600130c071a36
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In Molecular Biology
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Citations in the corpus
- RNA design rules from a massive open laboratory (2014, methodology): “First round designs from EteRNA participants were significantly better than designs from RNAInverse and NUPACK in the last three puzzles, with P values of 2.9 × 10 − 4 against both algorithms (Fig.”
- RNA target highlights in CASP15: Evaluation of predicted models by structure providers (2023, methodology): “45 Our design process includes validation of our sequences by comparing the predicted secondary structures from Vienna RNA 46 and NUPACK 47,48 to our designs.”
- ViennaRNA Package 2.0 (2011, methodology): “The NUPACK suite [21] focuses on folding of several interacting RNA strands and design problems.”
- Toehold Switches: De-Novo-Designed Regulators of Gene Expression (2014, positive): “We used the NUPACK nucleic acid sequence design package (Zadeh et al., 2011) to generate libraries of de-novo-designed translational activators satisfying the desired device parameters (see section S6 of the Extended Experimental Procedures and Figure S1C available online).”
- Enzyme-free nucleic acid dynamical systems (2017, methodology): “…minimization to obtain sequences for the long domains; fifth, proposed sequence sets are scored using heuristic criteria that make use of NUPACK ( 52 ) to evaluate key secondary structure and spurious binding interactions; finally, multiple independent sequence designs are compared according to…”
- UFold: fast and accurate RNA secondary structure prediction with deep learning (2020, neutral): “For this purpose, we use all RNA structures in the RNAStralign test set, on which we then benchmark UFold against other methods that can predict pseudoknots, including SPOT-RNA, e2efold, RNAs-tructure(ProbKnot) (56), NuPack (57) and HotKnots (58).”