In almeidasilvaf/cogeqc: Systematic quality checks on comparative genomics analyses

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Introduction

When working on your own genome project or when using publicly available genomes for comparative analyses, it is critical to assess the quality of your data. Over the past years, several tools have been developed and several metrics have been proposed to assess the quality of a genome assembly and annotation. cogeqc helps users interpret their genome assembly statistics by comparing them with statistics on publicly available genomes on the NCBI. Additionally, cogeqc also provides an interface to BUSCO [@simao2015busco], a popular tool to assess gene space completeness. Graphical functions are available to make publication-ready plots that summarize the results of quality control.

Installation

You can install cogeqc from Bioconductor with the following code:

if(!requireNamespace('BiocManager', quietly = TRUE))
  install.packages('BiocManager')
BiocManager::install("cogeqc")

# Load package after installation
library(cogeqc)

Assessing genome assembly quality: statistics in a context

When analyzing and interpreting genome assembly statistics, it is often useful to place your stats in a context by comparing them with stats from genomes of closely-related or even the same species. cogeqc provides users with an interface to the NCBI Datasets API, which can be used to retrieve summary stats for genomes on NCBI. In this section, we will guide you on how to retrieve such information and use it as a reference to interpret your data.

Obtaining assembly statistics for NCBI genomes

To obtain a data frame of summary statistics for NCBI genomes of a particular taxon, you will use the function get_genome_stats(). In the taxon parameter, you must specify the taxon from which data will be extracted. This can be done either by passing a character scalar with taxon name or by passing a numeric scalar with NCBI Taxonomy ID. For example, the code below demonstrates two ways of extracting stats on maize (Zea mays) genomes on NCBI:

# Example 1: get stats for all maize genomes using taxon name
maize_stats <- get_genome_stats(taxon = "Zea mays")
head(maize_stats)
str(maize_stats)

# Example 2: get stats for all maize genomes using NCBI Taxonomy ID
maize_stats2 <- get_genome_stats(taxon = 4577)

# Checking if objects are the same
identical(maize_stats, maize_stats2)

As you can see, there are r nrow(maize_stats) maize genomes on the NCBI. You can also include filters in your searches by passing a list of key-value pairs with keys in list names and values in elements. For instance, to obtain only chromosome-scale and annotated maize genomes, you would run:

# Get chromosome-scale maize genomes with annotation
## Create list of filters
filt <- list(
    filters.has_annotation = "true",
    filters.assembly_level = "chromosome"
)
filt

## Obtain data
filtered_maize_genomes <- get_genome_stats(taxon = "Zea mays", filters = filt)
dim(filtered_maize_genomes)

For a full list of filtering parameters and possible arguments, see the API documentation.

Comparing custom stats with NCBI stats

Now, suppose you sequenced a genome, obtained assembly and annotation stats, and want to compare them to NCBI genomes to identify potential issues. Examples of situations you may encounter include:

• The genome you assembled is huge and you think there might be a problem with your assembly.

• Your gene annotation pipeline predicted n genes, but you are not sure if this number is reasonable compared to other assemblies of the same species or closely-related species.

To compare user-defined summary stats with NCBI stats, you will use the function compare_genome_stats(). This function will include the values you observed for each statistic into a distribution (based on NCBI stats) and return the percentile and rank of your observed values in each distribution.

As an example, let's go back to our maize stats we obtained in the previous section. Suppose you sequenced a new maize genome and observed the following values:

1. Genome size = 2.4 Gb
2. Number of genes = 50,000
3. CC ratio = 2 [^1]

[^1]: Note: The CC ratio is the ratio of the number of contigs to the number of chromosome pairs, and it has been proposed in @wang2022proposed as a measurement of contiguity that compensates for the flaws of N50 and allows cross-species comparisons.

To compare your observed values with those for publicly available maize genomes, you need to store them in a data frame. The column accession is mandatory, and any other column will be matched against columns in the data frame obtained with get_genome_stats(). Thus, make sure column names in your data frame match column names in the reference data frame. Then, you can compare both data frames as below:

# Check column names in the data frame of stats for maize genomes on the NCBI
names(maize_stats)

# Create a simulated data frame of stats for a maize genome
my_stats <- data.frame(
    accession = "my_lovely_maize",
    sequence_length = 2.4 * 1e9,
    gene_count_total = 50000,
    CC_ratio = 2
)

# Compare stats
compare_genome_stats(ncbi_stats = maize_stats, user_stats = my_stats)

Visualizing summary assembly statistics

To have a visual representation of the summary stats obtained with get_genome_stats(), you will use the function plot_genome_stats().

# Summarize genome stats in a plot
plot_genome_stats(ncbi_stats = maize_stats)

Finally, you can pass your data frame of observed stats to highlight your values (as red points) in the distributions.

plot_genome_stats(ncbi_stats = maize_stats, user_stats = my_stats)

Assessing gene space completeness with BUSCO

One of the most common metrics to assess gene space completeness 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.

Running BUSCO

To run BUSCO from R, you will use the function run_busco()[^2]. 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().

[^2]: 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].

# 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.

# 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:

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 BUSCO summary statistics

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

# 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:

sessioninfo::session_info()

References {.unnumbered}

almeidasilvaf/cogeqc documentation built on Jan. 29, 2024, 7:20 a.m.