Contents

1 Introduction

The identification of groups of homologous genes within and across species is a powerful tool for evolutionary genomics. The most widely used tools to identify orthogroups (i.e., groups of orthologous genes) are OrthoFinder (Emms and Kelly 2019) and OrthoMCL (Li, Stoeckert, and Roos 2003). However, these tools generate different results depending on the parameters used, such as mcl inflation parameter, E-value, maximum number of hits, and others. Here, we propose a protein domain-aware assessment of orthogroup inference. The goal is to maximize the percentage of shared protein domains for genes in the same orthogroup.

2 Installation

if(!requireNamespace('BiocManager', quietly = TRUE))
  install.packages('BiocManager')
BiocManager::install("cogeqc")
# Load package after installation
library(cogeqc)

3 Data description

Here, we will use orthogroups from the PLAZA 5.0 database (Van Bel et al. 2021), inferred with OrthoFinder (Emms and Kelly 2019). For the purpose of demonstration, the complete dataset was filtered to only keep orthogroups for the Brassicaceae species Arabidopsis thaliana and Brassica oleraceae. Interpro domain annotations were also retrieved from PLAZA 5.0.

# Orthogroups for Arabidopsis thaliana and Brassica oleraceae
data(og)
head(og)
#>     Orthogroup Species      Gene
#> 1 HOM05D000001     Ath AT1G02310
#> 2 HOM05D000001     Ath AT1G03510
#> 3 HOM05D000001     Ath AT1G03540
#> 4 HOM05D000001     Ath AT1G04020
#> 5 HOM05D000001     Ath AT1G04840
#> 6 HOM05D000001     Ath AT1G05750

# Interpro domain annotations
data(interpro_ath)
data(interpro_bol)

head(interpro_ath)
#>        Gene Annotation
#> 1 AT1G01010  IPR036093
#> 2 AT1G01010  IPR003441
#> 3 AT1G01010  IPR036093
#> 4 AT1G01020  IPR007290
#> 5 AT1G01020  IPR007290
#> 6 AT1G01030  IPR003340
head(interpro_bol)
#>           Gene Annotation
#> 1 BolC1t00001H  IPR014710
#> 2 BolC1t00001H  IPR018490
#> 3 BolC1t00002H  IPR013057
#> 4 BolC1t00003H  IPR013057
#> 5 BolC1t00004H  IPR005178
#> 6 BolC1t00004H  IPR005178

If you infer orthogroups with OrthoFinder, you can read and parse the output file Orthogroups.tsv with the function read_orthogroups(). For example:

# Path to the Orthogroups.tsv file created by OrthoFinder
og_file <- system.file("extdata", "Orthogroups.tsv.gz", package = "cogeqc") 

# Read and parse file
orthogroups <- read_orthogroups(og_file)
head(orthogroups)
#>     Orthogroup Species      Gene
#> 1 HOM05D000001     Ath AT1G02310
#> 2 HOM05D000001     Ath AT1G03510
#> 3 HOM05D000001     Ath AT1G03540
#> 4 HOM05D000001     Ath AT1G04020
#> 5 HOM05D000001     Ath AT1G04840
#> 6 HOM05D000001     Ath AT1G05750

4 Assessing orthogroups

In cogeqc, you can assess orthogroup inference with either a protein domain-based approach or a reference-based approach. Both approaches are described below.

4.1 Protein domain-based orthogroup assessment

To assess the orthogroup inference, you can use the function assess_orthogroups(). This function takes as input a list of annotation data frames1 NOTE: The names of the list elements must match the species abbreviations in the column Species of the orthogroups data frame. For instance, if your orthogroups data frame contains the species Ath and Bol, the data frames in the annotation list must be named Ath and Bol (not necessarily in that order, but with these exact names). and an orthogroups data frame, and returns the relative homogeneity frequency2 Interpretation guide: Relative homogeneity frequencies range from 0 to 1, with 1 meaning that all genes in that particular orthogroup share the same protein domains, and 0 meaning that each gene in the orthogroup has a different protein domain. of each orthogroup for each species.

# Create a list of annotation data frames
annotation <- list(Ath = interpro_ath, Bol = interpro_bol)
str(annotation) # This is what the list must look like
#> List of 2
#>  $ Ath:'data.frame': 131661 obs. of  2 variables:
#>   ..$ Gene      : chr [1:131661] "AT1G01010" "AT1G01010" "AT1G01010" "AT1G01020" ...
#>   ..$ Annotation: chr [1:131661] "IPR036093" "IPR003441" "IPR036093" "IPR007290" ...
#>  $ Bol:'data.frame': 212665 obs. of  2 variables:
#>   ..$ Gene      : chr [1:212665] "BolC1t00001H" "BolC1t00001H" "BolC1t00002H" "BolC1t00003H" ...
#>   ..$ Annotation: chr [1:212665] "IPR014710" "IPR018490" "IPR013057" "IPR013057" ...

og_assessment <- assess_orthogroups(og, annotation)
head(og_assessment)
#>    Orthogroups      Ath_H      Bol_H     Mean_H
#> 1 HOM05D000001 0.03225806 0.02000000 0.02612903
#> 2 HOM05D000002 0.06666667 0.07142857 0.06904762
#> 3 HOM05D000003 0.10000000 0.07692308 0.08846154
#> 4 HOM05D000004 0.33333333 0.06250000 0.19791667
#> 5 HOM05D000005 0.04761905 0.03333333 0.04047619
#> 6 HOM05D000006 0.14285714 0.12500000 0.13392857

Now, we can calculate the mean homogeneity for this orthogroup inference.

mean(og_assessment$Mean_H)
#> [1] 0.5191506

Ideally, to have a reliable orthogroup inference, you should be able to run OrthoFinder with multiple combinations of parameters and assess each inference with assess_orthogroups(). The inference with the highest mean homonegeneity will be the best.3 Friendly tip: if you want to calculate homogeneity scores using a single species as a proxy (your orthogroups data frame will have only one species), you can use the function calculate_H().

4.2 Reference-based orthogroup assessment

In some cases, you may want to compare your orthogroup inference to a reference orthogroup inference. To do that, you can use the function compare_orthogroups(). For example, let’s simulate a different orthogroup inference by shuffling some rows of the og data frame and comparing it to the original data frame.

set.seed(123)

# Subset the top 5000 rows for demonstration purposes
og_subset <- og[1:5000, ]
ref <- og_subset

# Shuffle 100 genes to simulate a test set
idx_shuffle <- sample(seq_len(nrow(og_subset)), 100, replace = FALSE)
test <- og_subset
test$Gene[idx_shuffle] <- sample(
  test$Gene[idx_shuffle], size = length(idx_shuffle), replace = FALSE
)

# Compare test set to reference set
comparison <- compare_orthogroups(ref, test)
head(comparison)
#>     Orthogroup Preserved
#> 1 HOM05D000001     FALSE
#> 2 HOM05D000002     FALSE
#> 3 HOM05D000003     FALSE
#> 4 HOM05D000004      TRUE
#> 5 HOM05D000005     FALSE
#> 6 HOM05D000006      TRUE

# Calculating percentage of preservation
preserved <- sum(comparison$Preserved) / length(comparison$Preserved)
preserved
#> [1] 0.2702703

As we can see, 27.03% of the orthogroups in the reference data set are preserved in the shuffled data set.

5 Visualizing summary statistics

Now that you have identified the best combination of parameters for your orthogroup inference, you can visually explore some of its summary statistics. OrthoFinder automatically saves summary statistics in a directory named Comparative_Genomics_Statistics. You can parse this directory in a list of summary statistics with the function read_orthofinder_stats(). To demonstrate it, let’s read the output of OrthoFinder’s example with model species.

stats_dir <- system.file("extdata", package = "cogeqc")
ortho_stats <- read_orthofinder_stats(stats_dir)
ortho_stats
#> $stats
#>                   Species N_genes N_genes_in_OGs Perc_genes_in_OGs N_ssOGs
#> 1             Danio_rerio   30313          28236              93.1     569
#> 2 Drosophila_melanogaster   13931          10674              76.6     675
#> 3            Homo_sapiens   23480          22669              96.5     268
#> 4            Mus_musculus   22859          22006              96.3     243
#> 5       Takifugu_rubripes   20545          19403              94.4     135
#> 6      Xenopus_tropicalis   19987          18755              93.8     234
#>   N_genes_in_ssOGs Perc_genes_in_ssOGs Dups
#> 1             3216                10.6 3353
#> 2             3313                23.8 4527
#> 3             1625                 6.9 2283
#> 4             2022                 8.8 4131
#> 5              446                 2.2 9585
#> 6             1580                 7.9 3650
#> 
#> $og_overlap
#>                         Danio_rerio Drosophila_melanogaster Homo_sapiens
#> Danio_rerio                   13472                    5872        11365
#> Drosophila_melanogaster        5872                    6651         5866
#> Homo_sapiens                  11365                    5866        14468
#> Mus_musculus                  11345                    5863        14076
#> Takifugu_rubripes             12100                    5810        10994
#> Xenopus_tropicalis            11086                    5725        11478
#>                         Mus_musculus Takifugu_rubripes Xenopus_tropicalis
#> Danio_rerio                    11345             12100              11086
#> Drosophila_melanogaster         5863              5810               5725
#> Homo_sapiens                   14076             10994              11478
#> Mus_musculus                   14411             10976              11446
#> Takifugu_rubripes              10976             12649              10776
#> Xenopus_tropicalis             11446             10776              12302
#> 
#> $duplications
#>                       Node Duplications_50
#> 1  Drosophila_melanogaster            3353
#> 2             Homo_sapiens            4527
#> 3                       N0              73
#> 4        Takifugu_rubripes            2283
#> 5             Mus_musculus            4131
#> 6              Danio_rerio            9585
#> 7                       N1            2458
#> 8                       N2            1530
#> 9                       N3             195
#> 10                      N4             745
#> 11      Xenopus_tropicalis            3650

Now, we can use this list to visually explore summary statistics.

5.1 Species tree

To start, one would usually want to look at the species tree to detect possible issues that would compromise the accuracy of orthologs detection. The tree file can be easily read with treeio::read.tree().

data(tree)
plot_species_tree(tree)

You can also include the number of gene duplications in each node.

plot_species_tree(tree, stats_list = ortho_stats)

5.2 Species-specific duplications

The species tree above shows duplications per node, but it does not show species-duplications. To visualize that, you can use the function plot_duplications().

plot_duplications(ortho_stats)

5.3 Genes in orthogroups

Visualizing the percentage of genes in orthogroups is particularly useful for quality check, since one would usually expect a large percentage of genes in orthogroups, unless there is a very distant species in OrthoFinder’s input proteome data.

plot_genes_in_ogs(ortho_stats)

5.4 Species-specific orthogroups

To visualize the number of species-specific orthogroups, use the function plot_species_specific_ogs(). This plot can reveal a unique gene repertoire of a particular species if it has a large number of species-specific OGs as compared to the other ones.

plot_species_specific_ogs(ortho_stats)

5.5 All in one

To get a complete picture of OrthoFinder results, you can combine all plots together with plot_orthofinder_stats(), a wrapper that integrates all previously demonstrated plotting functions.

plot_orthofinder_stats(
  tree, 
  xlim = c(-0.1, 2),
  stats_list = ortho_stats
)

5.6 Orthogroup overlap

You can also visualize a heatmap of pairwise orthogroup overlap across species with plot_og_overlap().

plot_og_overlap(ortho_stats)