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Title: tSFM 1.0: tRNA Structure–Function Mapper

Journal Article · · Bioinformatics
ORCiD logo [1];  [2]; ORCiD logo [3];  [2]
  1. Univ. of California, Merced, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  2. Univ. of California, Merced, CA (United States)
  3. Martin Luther University Halle (Germany); German Centre for Integrative Biodiversity Research (Germany)

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. Here, 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.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1827219
Journal Information:
Bioinformatics, Journal Name: Bioinformatics Journal Issue: 20 Vol. 37; ISSN 1367-4803
Publisher:
Oxford University PressCopyright Statement
Country of Publication:
United States
Language:
English

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