Tree construction
Maximum-likelihood trees were constructed using IQ-Tree (Nguyen et
al. , 2015), under the optimal model defined by the ModelFinder (-MFP)
command (Kalyaanamoorthy et al. , 2017) (Table S6). Ultrafast
bootstraps and approximate likelihood ratio tests were performed using
IQ-Tree’s ultrafast bootstrap and Sh-aLRT parameters (Minh, Nguyen and
Von Haeseler, 2013; Hoang et al. , 2018). Full treefiles,
supports, and expanded accession data for all sequences are provided in
Supplementary Datafile S5.
Scripts and Jupyter Notebook files (Kluyver et al. , 2016) used
for automating alignment or treefile analysis, curation, and
visualization are available at
https://github.com/slschwartz/fournierlab-scripts.
Gene and enzyme structural analysis :
FIND (Murali et al. , 2019) was used to identify structural
features and conserved denitrification pathway genes in deep subsurface
genomes. Putative domains within denitrification gene ORFs were
identified and compared across genomes using NCBI’s Conserved Domains
Database (CDD) (Marchler-Bauer et al. , 2015; Lu et al. ,
2020) and EMBL Interpro (Mitchell et al. , 2019).
Existing enzyme structures for canonical denitrification genes were
downloaded from the RCSB Protein Data Bank (PDB) (Berman et al. ,
2000). Anaerolineales-type enzyme structures were predicted using
SWISS-MODEL (Waterhouse et al. , 2018). All enzyme
structures were visualized and analyzed in PyMOL (The PyMOL Molecular
Graphics System, Version 2.0 Schrödinger, LLC.)