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Publication Detail
Sequence covariation analysis in biological polymers
  • Publication Type:
    Chapter
  • Authors:
    Taylor WR, Kandathil S, Jones DT
  • Publication date:
    29/07/2019
  • Pagination:
    325, 346
  • Volume:
    1
  • ISBN-13:
    9781119429142
  • Status:
    Published
  • Book title:
    Handbook of Statistical Genomics
Abstract
Methods for the analysis of covariation between positions in large alignments of biological sequences have improved markedly over the last decade and, when combined with the large volumes of sequence data currently available, can now be used routinely to predict 3D macromolecular structure and interactions between molecules. A number of applications are reviewed, including: protein tertiary structure prediction for both globular and transmembrane proteins and disordered regions; protein-protein interactions; protein dynamics and RNA structure prediction. Recent developments in methodology are described, focusing on the application of machine learning approaches to the analysis of covariation signals, including deep learning (convolutional neural net) methods.
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Dept of Computer Science
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