ResidualBind: Uncovering Sequence-Structure Preferences of RNA-Binding Proteins with Deep Neural Networks.

Journal: Methods in molecular biology (Clifton, N.J.)
PMID: 36705906

Abstract

Deep neural networks have demonstrated improved performance at predicting sequence specificities of DNA- and RNA-binding proteins. However, it remains unclear why they perform better than previous methods that rely on k-mers and position weight matrices. Here, we highlight a recent deep learning-based software package, called ResidualBind, that analyzes RNA-protein interactions using only RNA sequence as an input feature and performs global importance analysis for model interpretability. We discuss practical considerations for model interpretability to uncover learned sequence motifs and their secondary structure preferences.

Authors

  • Peter K Koo
    Howard Hughes Medical Institute, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, United States.
  • Matt Ploenzke
    Department of Biostatistics, Harvard University, Cambridge, MA, USA.
  • Praveen Anand
    Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
  • Steffan Paul
    Bioinformatics Program, Harvard Medical School, Boston, MA, USA.
  • Antonio Majdandzic
    Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America.

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