tSFM 1.0: tRNA Structure-Function Mapper

Travis J Lawrence; Fatemeh Hadi-Nezhad; Ivo Grosse; David H Ardell

doi:10.1093/bioinformatics/btab247

tSFM 1.0: tRNA Structure-Function Mapper

Bioinformatics. 2021 Oct 25;37(20):3654-3656. doi: 10.1093/bioinformatics/btab247.

Authors

Travis J Lawrence^{1

2}, Fatemeh Hadi-Nezhad¹, Ivo Grosse^{3

4}, David H Ardell^{1

5}

Affiliations

¹ Quantitative and Systems Biology Program, University of California, Merced, CA 95343, USA.
² Biosciences Division, Oak Ridge National Lab, Oak Ridge, TN 37830, USA.
³ Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle 06099, Germany.
⁴ German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig 04103, Germany.
⁵ Department of Molecular and Cell Biology, University of California, Merced, CA 95343, USA.

Abstract

Motivation: Structure-conditioned information statistics have proven useful to predict and visualize tRNA Class-Informative Features (CIFs) and their evolutionary divergences. Although permutation P-values can quantify the significance of CIF divergences between two taxa, their naive Monte Carlo approximation is slow and inaccurate. The Peaks-over-Threshold approach of Knijnenburg et al. (2009) promises improvements to both speed and accuracy of permutation P-values, but has no publicly available API.

Results: We present tRNA Structure-Function Mapper (tSFM) v1.0, an open-source, multi-threaded application that efficiently computes, visualizes and assesses significance of single- and paired-site CIFs and their evolutionary divergences for any RNA, protein, gene or genomic element sequence family. Multiple estimators of permutation P-values for CIF evolutionary divergences are provided along with confidence intervals. tSFM is implemented in Python 3 with compiled C extensions and is freely available through GitHub (https://github.com/tlawrence3/tSFM) and PyPI.

Availability and implementation: The data underlying this article are available on GitHub at https://github.com/tlawrence3/tSFM.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

Grants and funding