Dental calculus metagenomics suggest that ecology, not host phylogeny, shapes the oral microbiome in closely related species

# Studying the gorilla oral microbiome using dental calculus from natural history museum specimens

Studying the oral microbiome of three gorilla subspecies using a shotgun metagenomic approach on dental calculus. The workflow is adapted from Brealey et al. 2020.

## Pre-processing of reads

- 2_polyGrem: Removal of PolyG tails
- 3_adapterRem: Adapter removal and merging of paired-end reads
- 4_barcodeTrim: Barcode trimming
- 5_qualityFilt: Quality filtering
- 6_deduplicate: Removal of duplicates
- 7_phixRem: Removal of sequences mapping to the PhiX genome
- 8_humanHostFilt: Removal of sequences mapping to the human and gorilla genome

## Host mtDNA analysis

- H1_mtgenomes: Obtaining mt genomes from dental calculus by mapping to a the gorilla mitochondrial genome reference. Script for obtaining mitochondrial sequences using a reference genome were adapted from Samantha Lopez Clinton's work.
- H2_haplotype_network: Plot haplotype network using downloaded complete mt genomes and dental calculus genomes > 80% complete. For dental calculus genomes < 80% and > 8% complete, find the most closely related genome from the haplotype network based on ANI values. Haplotype network script plot_hapnets.r was adapted from Zach Nolen's work. (not used in final report)
- H3_diagnostic_sites: Identifying and using mitochondrial diagnostic sites to verify species and subspecies assignments. Python and R scripts (DistanceToReferences.py, FindDiagnosticSites_edited.py, plot_readCoverage.r) were adapted from Axel Jensen's work.

## Taxonomic classification and analysis

- T1_kraken2: Taxonomic classification using Kraken2.
- T2_bracken: Re-estimation of taxa abundances using Bracken
- T3_community-level: Rscripts for the analysis of the taxonomic classification output (decontamination, abundancefiltering, PERMANOVA, identification of differentially abundant taxa, etc.)

## Read-level decontamination and MapDamage

- RD3: The scripts in this directory are run as part of the taxonomic-level analysis scripts in T3_community-level, in order to identify contaminants based on damage patterns. Takes list of taxa that are found in both oral and contaminant lists (produced by T3_community-level/6a_reference_based_decontam.R) and are represented by a sufficient number of reads, downloads one genome per taxon and then runs mapDamage. The output is then processed with T3_community-level/6b_reference_based_decontam.R
- RD1: First step of read-level decontamination of the FastQ files. Removes reads that were assigned to identified contaminants by Kraken2.
- RD2: Second step of read-level decontamination of the FastQ files. Removed contaminant reads by mapping to a concatenated file of all contaminant genomes.

## Functional profiling and analysis

- F1_HUMAnN2: Functional profiling using HUMAnN2
- F2_functional_stats: Rscripts for the analysis of the functional profiling output (identify subspecies associated biological processes, quantify contributions of different taxa to biological processes, etc.)

## Metagenomes-assembled genomes (MAGs)

- M1_metawrap: assembly, binning, refinement, taxonomic classification, and phylogenies of MAGs
- M2_isolation-sources: Isolation source information from multiple databases, categorization and visualization of isolation sources for each MAG
- M3_trees: scripts for producing core-gene trees for select oral taxa, using Phylophlan.
- M4_pangenome: scripts for running pangenomic analyses

## Other

- plots: scripts for producing most figures presented in the report and the supplement
- tables_and_stats: script producing most information provided in supplementary tables