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Updated: Jul 2, 2026

Novel Sequence Discovery by Subtractive Genomics
09:40

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

TRiFLe, a program for in silico terminal restriction fragment length polymorphism analysis with user-defined sequence

Pilar Junier1, Thomas Junier, Karl-Paul Witzel

  • 1Environmental Microbiology Laboratory, Ecole Polytechnique Federale de Lausanne, CH-1015 Lausanne, Switzerland. pilar.junier@epfl.ch

Applied and Environmental Microbiology
|September 2, 2008
PubMed
Summary

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TRiFLe is a new software tool that predicts terminal restriction fragments (T-RFs) to help analyze microbial community data. This tool aids in identifying specific T-RFs in T-RFLP patterns for ecological studies.

Area of Science:

  • Bioinformatics
  • Molecular Ecology
  • Microbial Ecology

Background:

  • T-RFLP analysis is a common method for studying microbial community structures.
  • Accurate interpretation of T-RFLP patterns can be challenging due to potential overlaps and ambiguities.
  • Identifying specific terminal restriction fragments (T-RFs) is crucial for linking genetic data to microbial functions.

Purpose of the Study:

  • To introduce TRiFLe, a novel, freely accessible computer program for generating theoretical T-RFs.
  • To enable rapid identification of polymorphic enzymes for T-RFLP analysis.
  • To assist in the identification of specific T-RFs by comparing theoretical predictions with experimental T-RFLP patterns.

Main Methods:

  • TRiFLe accepts user-supplied sequence sets (e.g., from clone libraries or specific genes).

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  • The program generates theoretical T-RFs based on user-defined parameters and enzyme choices.
  • It facilitates the comparison of theoretical T-RFs with experimental T-RFLP data.

    • Main Results:

    • TRiFLe successfully generated theoretical T-RFs for analyzing T-RFLP data of amoA and pmoA genes.
    • The analysis revealed overlapping T-RFLP patterns, suggesting co-occurrence of ammonia- and methane-oxidizing bacteria.
    • These findings were observed in the metalimnion of a subtropical lake.

    Conclusions:

    • TRiFLe is a valuable tool for in silico prediction and analysis of T-RFLP data.
    • The program aids in the interpretation of complex microbial community structures.
    • Application of TRiFLe to amoA and pmoA genes provided insights into bacterial overlap in aquatic environments.