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tSFM 1.0: tRNA Structure-Function Mapper.

Travis J Lawrence1,2, Fatemeh Hadi-Nezhad1, Ivo Grosse3,4

  • 1Quantitative and Systems Biology Program, University of California, Merced, CA 95343, USA.

Bioinformatics (Oxford, England)
|April 27, 2021
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Summary
This summary is machine-generated.

This study introduces tRNA Structure-Function Mapper (tSFM), an open-source tool for analyzing RNA Class-Informative Features (CIFs) and their evolutionary divergences. tSFM offers improved accuracy and speed for calculating significance, aiding evolutionary biology research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Structure-conditioned information statistics aid in predicting tRNA Class-Informative Features (CIFs) and their evolutionary divergences.
  • Current methods for quantifying CIF divergence significance, like Monte Carlo approximations for permutation P-values, are slow and inaccurate.
  • The Peaks-over-Threshold approach offers potential improvements but lacks a public API.

Purpose of the Study:

  • To develop an efficient and accurate tool for computing and visualizing RNA Class-Informative Features (CIFs) and their evolutionary divergences.
  • To provide multiple estimators for permutation P-values of CIF evolutionary divergences with confidence intervals.
  • To make this functionality publicly available for diverse sequence families.

Main Methods:

  • Development of tRNA Structure-Function Mapper (tSFM) v1.0, an open-source, multi-threaded application.
  • Implementation in Python 3 with compiled C extensions for efficiency.
  • Inclusion of multiple permutation P-value estimators and confidence intervals.

Main Results:

  • tSFM v1.0 efficiently computes, visualizes, and assesses the significance of single- and paired-site CIFs and their evolutionary divergences.
  • The application supports analysis for any RNA, protein, gene, or genomic element sequence family.
  • Provides multiple estimators for permutation P-values with confidence intervals for CIF evolutionary divergences.

Conclusions:

  • tSFM v1.0 provides a significant advancement in the analysis of RNA sequence evolution and structure-function relationships.
  • The open-source nature and efficient implementation make tSFM a valuable tool for the research community.
  • Facilitates robust assessment of evolutionary divergences in various biological sequence families.