OM17_taxa_assignment.Rmd

---
title: "16S rRNA gene taxonomic assignment on DNA extracted from samples obtained in Oman in 2017"
subtitle: "Source file: OM17_taxa_assignment.Rmd"
author: "Daniel Nothaft"
date: "`r format(Sys.Date(), '%d %b %Y')`"
output:
  html_document: 
    df_print: paged # omit to disable paged data table output
    css: stylesheet.css # omit if no need for custom stylesheet
    number_sections: yes # change to no for unnumbered sections
    toc: yes # change to no to disable table of contents
    toc_float: true # change to false to keep toc at the top
    toc_depth: 3 # change to specify which headings to include in toc
    code_folding: show # change to hide to hide code by default
editor_options:
  chunk_output_type: inline
---

# Setup

Data processed using the packages [dada2](https://benjjneb.github.io/dada2/index.html) version `r packageVersion("dada2")`.

Following [this tutorial](https://benjjneb.github.io/dada2/tutorial.html)

Load packages
```{r setup}
library(dada2); # processing sequence data
packageVersion("dada2")
library(tidyverse) # manipulating strings and data frames
packageVersion("tidyverse")

# global knitting options for automatic saving of all plots as .png and .pdf. Also sets cache directory.
knitr::opts_chunk$set(
  dev = c("png", "pdf"), fig.keep = "all",
  dev.args = list(pdf = list(encoding = "WinAnsi", useDingbats = FALSE)),
  fig.path = file.path("fig_output/", paste0(gsub("\\.[Rr]md", "/", knitr::current_input()))),
  cache.path = file.path("cache/", paste0(gsub("\\.[Rr]md", "/", knitr::current_input())))
)
```

```{r source}
# source all relevant scripting files
# paths.R contains paths to local data and programs
source(file.path("scripts", "paths.R"))
```

```{r load-seqtab}
seqtab_nochim <- read_rds("data_output/seqtab_nochim_OM17_processed_20200716_3.rds")
```

# Assign taxonomy
It is common at this point, especially in 16S/18S/ITS amplicon sequencing, to assign taxonomy to the sequence variants. The DADA2 package provides a native implementation of the naive Bayesian classifier method for this purpose. The assignTaxonomy function takes as input a set of sequences to be classified and a training set of reference sequences with known taxonomy, and outputs taxonomic assignments with at least minBoot bootstrap confidence.

Here, we use the Silva 138 training set.

Additionally, species are assigned based on exact matching between ASVs and sequenced reference strains.
```{r assign-taxonomy}
taxa <- assignTaxonomy(seqtab_nochim, ref_database_path, multithread=TRUE) %>% addSpecies(ref_database_species_path)
```


Let’s inspect the taxonomic assignments:
```{r inspect-taxa-assignments}
taxa.print <- taxa # Removing sequence rownames for display only
rownames(taxa.print) <- NULL
head(taxa.print)
```

# Export taxa table
```{r write-taxtab}
write_rds(taxa, path = format(Sys.Date(), "data_output/taxa_OM17_processed_%Y%m%d_2.rds"), compress = "gz")
```