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tSFM 1.0: tRNA Structure-Function Mapper

Travis J Lawrence^1,2, Fatemeh Hadi-Nezhad¹, Ivo Grosse^3,4

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

Bioinformatics (Oxford, England)

|April 27, 2021

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.

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