---
title: "Assessing genome assembly quality"
author: 
  - name: Fabricio Almeida-Silva
    affiliation: VIB-UGent Center for Plant Systems Biology, Ghent University, Ghent, Belgium
  - name: Yves Van de Peer
    affiliation: VIB-UGent Center for Plant Systems Biology, Ghent University, Ghent, Belgium
output: 
  BiocStyle::html_document:
    toc: true
    number_sections: yes
bibliography: vignette_01.bib
vignette: >
  %\VignetteIndexEntry{Assessing genome assembly}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}  
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    crop = NULL,
    dpi = 100
)
```

# Introduction

One of the most common metrics to assess the quality of genome assemblies is BUSCO (best universal single-copy orthologs) [@simao2015busco]. `cogeqc` allows users to run BUSCO from an R session and visualize results graphically. BUSCO summary statistics will help you assess which assemblies have high quality based on the percentage of complete BUSCOs.

# Installation

```{r installation, eval=FALSE}
if(!requireNamespace('BiocManager', quietly = TRUE))
  install.packages('BiocManager')
BiocManager::install("cogeqc")
```

```{r load_package, message=FALSE}
# Load package after installation
library(cogeqc)
```

# Running BUSCO

To run BUSCO from R, you will use the function `run_busco()`[^1]. Here, we will use an example FASTA file containing the first 1,000 lines of the *Herbaspirilllum seropedicae SmR1* genome (GCA_000143225), which was downloaded from Ensembl Bacteria. We will run BUSCO using *burkholderiales_odb10* as the lineage dataset. To view all available datasets, run `list_busco_datasets()`.

[^1]: **NOTE:** You must have BUSCO installed and in your PATH to use `run_busco()`. You can check if BUSCO is installed by running `busco_is_installed()`. If you don't have it already, you can manually install it or use a conda virtual environment with the Bioconductor package `Herper` [@herper].

```{r run_busco, eval=FALSE}
# Path to FASTA file
sequence <- system.file("extdata", "Hse_subset.fa", package = "cogeqc")

# Path to directory where BUSCO datasets will be stored
download_path <- paste0(tempdir(), "/datasets")

# Run BUSCO if it is installed
if(busco_is_installed()) {
  run_busco(sequence, outlabel = "Hse", mode = "genome",
            lineage = "burkholderiales_odb10",
            outpath = tempdir(), download_path = download_path)
}
```

The output will be stored in the directory specified in *outpath*. You can read and parse BUSCO's output with the function `read_busco()`. For example, let's read the output of a BUSCO run using the genome of the green algae *Ostreococcus tauri*. The output directory is `/extdata`.

```{r}
# Path to output directory
output_dir <- system.file("extdata", package = "cogeqc")

busco_summary <- read_busco(output_dir)
busco_summary
```

This is an example output for a BUSCO run with a single FASTA file. You can also specify a directory containing multiple FASTA files in the *sequence* argument of `run_busco()`. This way, BUSCO will be run in batch mode. Let's see what the output of BUSCO in batch mode looks like:

```{r}
data(batch_summary)
batch_summary
```

The only difference between this data frame and the previous one is the column **File**, which contains information on the FASTA file. The example dataset `batch_summary` contains the output of `run_busco()` using a directory containing two genomes (*Herbaspirillum seropedicae SmR1* and *Herbaspirillum rubrisubalbicans M1*) as parameter to the *sequence* argument.

# Visualizing summary statistics

After using `run_busco()` and parsing its output with `read_busco()`, users can visualize summary statistics with `plot_busco()`.

```{r plot_busco, out.width = '100%'}
# Single FASTA file - Ostreococcus tauri
plot_busco(busco_summary)

# Batch mode - Herbaspirillum seropedicae and H. rubrisubalbicans
plot_busco(batch_summary)
```

We usually consider genomes with >90% of complete BUSCOs as having high quality. Thus, we can conclude that the three genomes analyzed here are high-quality genomes.

# Session information {.unnumbered}

This document was created under the following conditions:

```{r session_info}
sessioninfo::session_info()
```

# References {.unnumbered}

