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Updated: Sep 25, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
Evolutionary reconstruction of thiamine biosynthesis pathway: An integrative bioinformatics workflow
Naiara Almeida de Toledo1, Marie-Anne Van Sluys1, Henrique Moura Dias2
1Departamento de Botânica, Instituto de Biociências, Universidade de São Paulo, Sao Paulo, SP, Brazil.
Abstract:
Thiamine (vitamin B1) biosynthesis involves enzymatic activities that exhibit remarkable diversity across the tree of life, including deep sequence divergence, domain rearrangements, lineage-specific duplications and losses, and horizontal gene transfer events. These features complicate computational inference based on single evidence types. This chapter describes a reproducible, modular bioinformatics workflow for pathway-scale comparative analysis of thiamine biosynthesis genes across Bacteria, Archaea, and Eukarya. The workflow integrates four complementary evidence streams: (i) profile-based homolog discovery using Hidden Markov Models (HMMs) with domain architecture validation; (ii) per-family phylogenetic reconstruction to evaluate evolutionary relationships, distinguish orthologs from paralogs, and identify lineage-specific patterns; (iii) detection of TPP riboswitches using covariance models, with an overview of upstream promoter motif discovery as a complementary observational approach; and (iv) transcriptome integration to assess context-dependent gene expression. We further demonstrate how variation in protein domain architecture, particularly single versus multi domain configurations, shape sequence recovery, phylogenetic inference, and evolutionary interpretation. Step-by-step protocols cover computational environment setup, parameter optimization, quality control checkpoints, and interpretation guidelines. The workflow addresses common bioinformatic challenges and annotation inconsistencies across databases. All scripts, HMM profiles, and example datasets are available through GitHub, enabling researchers to apply these methods to thiamine pathway analysis or adapt them for other metabolic pathways with similar evolutionary diversity.
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