Geometric combinatorics and computational molecular biology: Branching polytopes for RNA sequences

Questions in computational molecular biology generate various discrete optimization problems, such as DNA sequence alignment and RNA secondary structure prediction. However, the optimal solutions are fundamentally dependent on the parameters used in the objective functions. The goal of a parametric...

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Main Authors: Harrington, H, Drellich, E, Gainer-Dewar, A, He, Q, Heitsch, C, Poznanovic, S
Format: Book section
Izdano: American Mathematical Society 2017
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author Harrington, H
Drellich, E
Gainer-Dewar, A
He, Q
Heitsch, C
Poznanovic, S
author2 Harrington, H
author_facet Harrington, H
Harrington, H
Drellich, E
Gainer-Dewar, A
He, Q
Heitsch, C
Poznanovic, S
author_sort Harrington, H
collection OXFORD
description Questions in computational molecular biology generate various discrete optimization problems, such as DNA sequence alignment and RNA secondary structure prediction. However, the optimal solutions are fundamentally dependent on the parameters used in the objective functions. The goal of a parametric analysis is to elucidate such dependencies, especially as they pertain to the accuracy and robustness of the optimal solutions. Techniques from geometric combinatorics, including polytopes and their normal fans, have been used previously to give parametric analyses of simple models for DNA sequence alignment and RNA branching configurations. Here, we present a new computational framework, and proof-of-principle results, which give the first complete parametric analysis of the branching portion of the nearest neighbor thermodynamic model for secondary structure prediction for real RNA sequences.
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spelling oxford-uuid:569331e3-f3db-42c8-a933-5ff5b9dd9c6b2022-03-26T16:51:02ZGeometric combinatorics and computational molecular biology: Branching polytopes for RNA sequencesBook sectionhttp://purl.org/coar/resource_type/c_3248uuid:569331e3-f3db-42c8-a933-5ff5b9dd9c6bSymplectic Elements at OxfordAmerican Mathematical Society2017Harrington, HDrellich, EGainer-Dewar, AHe, QHeitsch, CPoznanovic, SHarrington, HWright, MOmar, MQuestions in computational molecular biology generate various discrete optimization problems, such as DNA sequence alignment and RNA secondary structure prediction. However, the optimal solutions are fundamentally dependent on the parameters used in the objective functions. The goal of a parametric analysis is to elucidate such dependencies, especially as they pertain to the accuracy and robustness of the optimal solutions. Techniques from geometric combinatorics, including polytopes and their normal fans, have been used previously to give parametric analyses of simple models for DNA sequence alignment and RNA branching configurations. Here, we present a new computational framework, and proof-of-principle results, which give the first complete parametric analysis of the branching portion of the nearest neighbor thermodynamic model for secondary structure prediction for real RNA sequences.
spellingShingle Harrington, H
Drellich, E
Gainer-Dewar, A
He, Q
Heitsch, C
Poznanovic, S
Geometric combinatorics and computational molecular biology: Branching polytopes for RNA sequences
title Geometric combinatorics and computational molecular biology: Branching polytopes for RNA sequences
title_full Geometric combinatorics and computational molecular biology: Branching polytopes for RNA sequences
title_fullStr Geometric combinatorics and computational molecular biology: Branching polytopes for RNA sequences
title_full_unstemmed Geometric combinatorics and computational molecular biology: Branching polytopes for RNA sequences
title_short Geometric combinatorics and computational molecular biology: Branching polytopes for RNA sequences
title_sort geometric combinatorics and computational molecular biology branching polytopes for rna sequences
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AT heitschc geometriccombinatoricsandcomputationalmolecularbiologybranchingpolytopesforrnasequences
AT poznanovics geometriccombinatoricsandcomputationalmolecularbiologybranchingpolytopesforrnasequences