A Guide to Gene‐Centric Analysis Using TreeSAPP

Gene-centric analysis is commonly used to chart the structure, function, and activity of microbial communities in natural and engineered environments. A common approach is to create custom ad hoc reference marker gene sets, but these come with the typical disadvantages of inaccuracy and limited utility beyond assigning query sequences taxonomic labels. The Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software package standardizes analysis of phylogenetic and functional marker genes and improves predictive performance using a classification algorithm that leverages information-rich reference packages consisting of a multiple sequence alignment, a profile hidden Markov model, taxonomic lineage information, and a phylogenetic tree. Here, we provide a set of protocols that link the various analysis modules in TreeSAPP into a coherent process that both informs and directs the user experience. This workflow, initiated from a collection of candidate reference sequences, progresses through construction and refinement of a reference package to marker identification and normalized relative abundance calculations for homologous sequences in metagenomic and metatranscriptomic datasets. The alpha subunit of methyl-coenzyme M reductase (McrA) involved in biological methane cycling is presented as a use case given its dual role as a phylogenetic and functional marker gene driving an ecologically relevant process. These protocols fill several gaps in prior TreeSAPP documentation and provide best practices for reference package construction and refinement, including manual curation steps from trusted sources in support of reproducible gene-centric analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating reference packages Support Protocol 1: Installing TreeSAPP Support Protocol 2: Annotating traits within a phylogenetic context Basic Protocol 2: Updating reference packages Basic Protocol 3: Calculating relative abundance of genes in metagenomic and metatranscriptomic datasets.

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