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Cited by 762

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Conformational analysis of nucleic acids revisited: Curves+

Open access
Lavery, R., Moakher, M., Maddocks, J. H., Petkeviciute, D., Zakrzewska, K.·2009·Nucleic Acids Research

Abstract

We describe Curves+, a new nucleic acid conformational analysis program which is applicable to a wide range of nucleic acid structures, including those with up to four strands and with either canonical or modified bases and backbones. The program is algorithmically simpler and computationally much faster than the earlier Curves approach, although it still provides both helical and backbone parameters, including a curvilinear axis and parameters relating the position of the bases to this axis. It additionally provides a full analysis of groove widths and depths. Curves+ can also be used to analyse molecular dynamics trajectories. With the help of the accompanying program Canal, it is possible to produce a variety of graphical output including parameter variations along a given structure and time series or histograms of parameter variations during dynamics.

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Crossref · 10.1093/nar/gkp608

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Metadata

Authors
Lavery, R., Moakher, M., Maddocks, J. H., Petkeviciute, D., & Zakrzewska, K.
Venue
Nucleic Acids Research
Published
2009-07-22
Volume
37
Issue
17
Pages
5917-5929
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
OpenAlex
W2032183039
Cited by
762
References
3
FWCI
10.32
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Conformational analysis of nucleic acids revisited: Curves+. https://app.cochat.ai/papers/10.1093/nar/gkp608
The paper
Lavery, R., Moakher, M., Maddocks, J. H., Petkeviciute, D., & Zakrzewska, K. (2009). Conformational analysis of nucleic acids revisited: Curves+. Nucleic Acids Research. https://doi.org/10.1093/nar/gkp608

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.