Package 'cogeqc' reference manual

Title:	Systematic quality checks on comparative genomics analyses
Description:	cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualization functions to explore QC summary statistics.
Authors:	Fabrício Almeida-Silva [aut, cre] , Yves Van de Peer [aut]
Maintainer:	Fabrício Almeida-Silva <[email protected]>
License:	GPL-3
Version:	1.11.0
Built:	2024-12-19 03:23:25 UTC
Source:	https://github.com/bioc/cogeqc

Assess orthogroup inference based on functional annotation

Description

Assess orthogroup inference based on functional annotation

Usage

assess_orthogroups(
  orthogroups = NULL,
  annotation = NULL,
  correct_overclustering = TRUE
)
assess_orthogroups(
  orthogroups = NULL,
  annotation = NULL,
  correct_overclustering = TRUE
)

Arguments

`orthogroups`	A 3-column data frame with columns Orthogroup, Species, and Gene. This data frame can be created from the 'Orthogroups.tsv' file generated by OrthoFinder with the function `read_orthogroups()`.
`annotation`	A list of 2-column data frames with columns Gene (gene ID) and Annotation (annotation ID). The names of list elements must correspond to species names as in the second column of orthogroups. For instance, if there are two species in the orthogroups data frame named "SpeciesA" and "SpeciesB", annotation must be a list of 2 data frames, and each list element must be named "SpeciesA" and "SpeciesB".
`correct_overclustering`	Logical indicating whether to correct for overclustering in orthogroups. Default: TRUE.

Value

A data frame.

Examples

data(og)
data(interpro_ath)
data(interpro_bol)
# Subsetting annotation for demonstration purposes.
annotation <- list(Ath = interpro_ath[1:1000,], Bol = interpro_bol[1:1000,])
assess <- assess_orthogroups(og, annotation)
data(og)
data(interpro_ath)
data(interpro_bol)
# Subsetting annotation for demonstration purposes.
annotation <- list(Ath = interpro_ath[1:1000,], Bol = interpro_bol[1:1000,])
assess <- assess_orthogroups(og, annotation)

Assess synteny network based on graph properties

Description

Assess synteny network based on graph properties

Usage

assess_synnet(synnet = NULL, cc_type = "average")
assess_synnet(synnet = NULL, cc_type = "average")

Arguments

`synnet`	Edge list for the synteny network in a 2-column data frame, with columns 1 and 2 representing names of loci in anchor 1 and anchor 2, respectively.
`cc_type`	Type of clustering coefficient to be calculated. One of 'global' or 'average'. Default: 'average'.

Details

Network score is the product of the network's clustering coefficient, node count, and R squared for the scale-free topology fit.

Value

A data frame with the following variables:

CC: Numeric representing clustering coefficient.
Node_count: Numeric representing number of nodes in the network.
Rsquared: Numeric indicating the coefficient of determination for the scale-free topology fit.
Score: Numeric representing network score, which is the product of 'CC' and 'Node_number'.

Examples

data(synnet)
assess_synnet(synnet)
data(synnet)
assess_synnet(synnet)

Assess list of synteny networks as in `assess_synnet`

Description

Assess list of synteny networks as in assess_synnet

Usage

assess_synnet_list(synnet_list = NULL, cc_type = "average")
assess_synnet_list(synnet_list = NULL, cc_type = "average")

Arguments

`synnet_list`	A list of networks, each network being an edge list as a 2-column data frame, with columns 1 and 2 representing names of loci in anchor 1 and anchor 2, respectively.
`cc_type`	Type of clustering coefficient to be calculated. One of 'global' or 'average'. Default: 'average'.

Value

A data frame with the following variables:

CC: Numeric representing clustering coefficient.
Node_count: Numeric representing number of nodes in the network.
Rsquared: Numeric indicating the coefficient of determination for the scale-free topology fit.
Score: Numeric representing network score, which is the product of 'CC' and 'Node_number'.
Network: Character of network name.

Examples

set.seed(123)
data(synnet)
net1 <- synnet
net2 <- synnet[-sample(1:10000, 500), ]
net3 <- synnet[-sample(1:10000, 1000), ]
synnet_list <- list(net1 = net1, net2 = net2, net3 = net3)
assess_synnet_list(synnet_list)
set.seed(123)
data(synnet)
net1 <- synnet
net2 <- synnet[-sample(1:10000, 500), ]
net3 <- synnet[-sample(1:10000, 1000), ]
synnet_list <- list(net1 = net1, net2 = net2, net3 = net3)
assess_synnet_list(synnet_list)

BUSCO summary output for batch mode

Description

This object was created with the function read_busco() using a batch run of BUSCO on the genomes of Herbaspirillum seropedicae SmR1 and Herbaspirillum rubrisubalbicans M1.

Usage

data(batch_summary)
data(batch_summary)

Format

A 2-column data frame with the following variables:

Class: Factor of BUSCO classes
Frequency: Numeric with the percentage of BUSCOs in each class.
Lineage: Character with the lineage dataset used.
File: Character with the name of the FASTA file used.

Examples

data(batch_summary)
data(batch_summary)

Check if BUSCO is installed

Description

Check if BUSCO is installed

Usage

busco_is_installed()
busco_is_installed()

Value

Logical indicating whether BUSCO is installed or not.

Examples

busco_is_installed()
busco_is_installed()

Calculate homogeneity scores for orthogroups

Description

Calculate homogeneity scores for orthogroups

Usage

calculate_H(
  orthogroup_df,
  correct_overclustering = TRUE,
  max_size = 200,
  update_score = TRUE
)
calculate_H(
  orthogroup_df,
  correct_overclustering = TRUE,
  max_size = 200,
  update_score = TRUE
)

Arguments

`orthogroup_df`	Data frame with orthogroups and their associated genes and annotation. The columns Gene, Orthogroup, and Annotation are mandatory, and they must represent Gene ID, Orthogroup ID, and Annotation ID (e.g., Interpro/PFAM), respectively.
`correct_overclustering`	Logical indicating whether to correct for overclustering in orthogroups. Default: TRUE.
`max_size`	Numeric indicating the maximum orthogroup size to consider. If orthogroups are too large, calculating Sorensen-Dice indices for all pairwise combinations could take a long time, so setting a limit prevents that. Default: 200.
`update_score`	Logical indicating whether to replace scores with corrected scores or not. If FALSE, the dispersal term and corrected scores are returned as separate variables in the output data frame.

Details

Homogeneity is calculated based on pairwise Sorensen-Dice similarity indices between gene pairs in an orthogroup, and they range from 0 to 1. Thus, if all genes in an orthogroup share the same domain, the orthogroup will have a homogeneity score of 1. On the other hand, if genes in an orthogroup do not have any domain in common, the orthogroup will have a homogeneity score of 0. The percentage of orthogroups with size greater than max_size will be subtracted from the homogeneity scores, since too large orthogroups typically have very low scores. Additionally, users can correct for overclustering by penalizing protein domains that appear in multiple orthogroups (default).

Value

A 2-column data frame with the variables Orthogroup and Score, corresponding to orthogroup ID and orthogroup score, respectively. If update_score = FALSE, additional columns named Dispersal and Score_c are added, which correspond to the dispersal term and corrected scores, respectively.

Examples

data(og)
data(interpro_ath)
orthogroup_df <- merge(og[og$Species == "Ath", ], interpro_ath)
# Filter data to reduce run time
orthogroup_df <- orthogroup_df[1:10000, ]
H <- calculate_H(orthogroup_df)
data(og)
data(interpro_ath)
orthogroup_df <- merge(og[og$Species == "Ath", ], interpro_ath)
# Filter data to reduce run time
orthogroup_df <- orthogroup_df[1:10000, ]
H <- calculate_H(orthogroup_df)

Compare user-defined assembly statistics with statistics of NCBI genomes

Description

This function helps users analyze their genome assembly stats in a context by comparing metrics obtained by users with "reference" metrics in closely-related organisms.

Usage

compare_genome_stats(ncbi_stats = NULL, user_stats = NULL)
compare_genome_stats(ncbi_stats = NULL, user_stats = NULL)

Arguments

ncbi_stats

A data frame of summary statistics for a particular taxon obtained from the NCBI, as obtained with the function get_genome_stats.

user_stats

A data frame with assembly statistics obtained by the user. A column named accession is mandatory, and it must contain unique identifiers for the genome(s) analyzed by the user. Dummy variables can be used as identifiers (e.g., "my_genome_001"), as long as they are unique. All other column containing assembly stats must have the same names as their corresponding columns in the data frame specified in ncbi_stats. For instance, stats on total number of genes and sequence length must be in columns named "gene_count_total" and "sequence_length", as in the ncbi_stats data frame.

Details

For each genome assembly statistic (e.g., "gene_count_total"), values in user_stats are compared to a distribution of values from ncbi_stats, and their percentile and rank in the distributions are reported.

Value

A data frame with the following variables:

accession: character, unique identifier as in user_stats$accession.
variable: character, name of the genome assembly metric (e.g., "CC_ratio").
percentile: numeric, percentile in the distribution.
rank: numeric, rank in the distribution (highest to lowest). For the variable "CC_ratio", ranks go from lowest to highest.

Examples

# Use case: user assembled a maize (Zea mays) genome

## Obtain stats for maize genomes on the NCBI
ncbi_stats <- get_genome_stats(taxon = "Zea mays")

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

# Compare stats
compare_genome_stats(ncbi_stats, user_stats)

# Use case: user assembled a maize (Zea mays) genome

## Obtain stats for maize genomes on the NCBI
ncbi_stats <- get_genome_stats(taxon = "Zea mays")

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

# Compare stats
compare_genome_stats(ncbi_stats, user_stats)

Compare inferred orthogroups to a reference set

Description

Compare inferred orthogroups to a reference set

Usage

compare_orthogroups(ref_orthogroups = NULL, test_orthogroups = NULL)
compare_orthogroups(ref_orthogroups = NULL, test_orthogroups = NULL)

Arguments

`ref_orthogroups`	Reference orthogroups in a 3-column data frame with columns Orthogroup, Species, and Gene. This data frame can be created from the 'Orthogroups.tsv' file generated by OrthoFinder with the function `read_orthogroups()`.
`test_orthogroups`	Test orthogroups that will be compared to ref_orthogroups in the same 3-column data frame format.

Details

This function compares a test set of orthogroups to a reference set and returns which orthogroups in the reference set are fully preserved in the test set (i.e., identical gene repertoire) and which are not. Species names (column 2) must be the same between reference and test set. If some species are not shared between reference and test sets, they will not be considered for the comparison.

Value

A 2-column data frame with the following variables:

Orthogroup: Character of orthogroup IDs.
Preserved: A logical vector of preservation status. It is TRUE if the orthogroup in the reference set is fully preserved in the test set, and FALSE otherwise.

Examples

set.seed(123)
data(og)
og <- og[1:5000, ]
ref <- og
# Shuffle genes to simulate a different set
test <- data.frame(
    Orthogroup = sample(og$Orthogroup, nrow(og), replace = FALSE),
    Species = og$Species,
    Gene = og$Gene
)
comparison <- compare_orthogroups(ref, test)

# Calculating percentage of preservation
sum(comparison$Preserved) / length(comparison$Preserved)
set.seed(123)
data(og)
og <- og[1:5000, ]
ref <- og
# Shuffle genes to simulate a different set
test <- data.frame(
    Orthogroup = sample(og$Orthogroup, nrow(og), replace = FALSE),
    Species = og$Species,
    Gene = og$Gene
)
comparison <- compare_orthogroups(ref, test)

# Calculating percentage of preservation
sum(comparison$Preserved) / length(comparison$Preserved)

Goodness of fit test for the scale-free topology model

Description

Goodness of fit test for the scale-free topology model

Usage

fit_sft(edges)
fit_sft(edges)

Arguments

edges

A 2-column data frame with network edges represented in each. Columns 1 and 2 represent nodes 1 and 2 of each edge.

Value

A numeric scalar with the R squared for the scale-free topology fit.

Examples

data(synnet)
edges <- synnet
fit_sft(edges)
data(synnet)
edges <- synnet
fit_sft(edges)

Get summary statistics for genomes on NCBI using the NCBI Datasets API

Description

Get summary statistics for genomes on NCBI using the NCBI Datasets API

Usage

get_genome_stats(taxon = NULL, filters = NULL)
get_genome_stats(taxon = NULL, filters = NULL)

Arguments

`taxon`	Taxon for which summary statistics will be retrieved, either as a character scalar (e.g., "brassicaceae") or as a numeric scalar representing NCBI Taxonomy ID (e.g., 3700).
`filters`	(optional) A list of filters to use when querying the API in the form of key-value pairs, with keys in list names and values in list elements (e.g., `list(filters.reference_only = "true")`, see examples for details).

Details

Possible filters for the filters parameter can be accessed at https://www.ncbi.nlm.nih.gov/datasets/docs/v2/reference-docs/rest-api/#get-/genome/taxon/-taxons-/dataset_report.

Value

A data frame with the following variables:

accession: character, accession number.
source: character, data source.
species_taxid: numeric, NCBI Taxonomy ID.
species_name: character, species' scientific name.
species_common_name: character, species' common name.
species_ecotype: character, species' ecotype.
species_strain: character, species' strain.
species_isolate: character, species' isolate.
species_cultivar: character, species' cultivar.
assembly_level: factor, assembly level ("Complete", "Chromosome", "Scaffold", or "Contig").
assembly_status: character, assembly status.
assembly_name: character, assembly name.
assembly_type: character, assembly type.
submission_date: character, submission date (YYYY-MM-DD).
submitter: character, submitter name.
sequencing_technology: character, sequencing technology.
atypical: logical, indicator of wheter the genome is atypical.
refseq_category: character, RefSeq category.
chromosome_count: numeric, number of chromosomes.
sequence_length: numeric, total sequence length.
ungapped_length: numeric, ungapped sequence length.
contig_count: numeric, number of contigs.
contig_N50: numeric, contig N50.
contig_L50: numeric, contig L50.
scaffold_N50: numeric, contig N50.
scaffold_L50: numeric, contig L50.
GC_percent: numeric, GC percentage (0-100).
annotation_provider: character, name of annotation provider.
annotation_release_date: character, annotation release date (YYYY-MM-DD).
gene_count_total: numeric, total number of genes.
gene_count_coding: numeric, number of protein-coding genes.
gene_count_noncoding: numeric, number of non-coding genes.
gene_count_pseudogene: numeric, number of pseudogenes.
gene_count_other: numeric, number of other genes.
CC_ratio: numeric, ratio of the number of contigs to the number of chromosomes.

Examples

# Example 1: Search for A. thaliana genomes by tax ID
ex1 <- get_genome_stats(taxon = 3702)

# Example 2: Search for A. thaliana genomes by name
ex2 <- get_genome_stats(taxon = "Arabidopsis thaliana")

# Example 3: Search for chromosome-level Brassicaeae genomes
ex3 <- get_genome_stats(
    taxon = "brassicaceae",
    filters = list(filters.assembly_level = "chromosome")
)

# Example 1: Search for A. thaliana genomes by tax ID
ex1 <- get_genome_stats(taxon = 3702)

# Example 2: Search for A. thaliana genomes by name
ex2 <- get_genome_stats(taxon = "Arabidopsis thaliana")

# Example 3: Search for chromosome-level Brassicaeae genomes
ex3 <- get_genome_stats(
    taxon = "brassicaceae",
    filters = list(filters.assembly_level = "chromosome")
)

Intepro annotation for Arabidopsis thaliana's genes

Description

The annotation data were retrieved from PLAZA Dicots 5.0.

Usage

data(interpro_ath)
data(interpro_ath)

Format

A 2-column data frame:

Gene: Character of gene IDs.
Annotation: Character of Interpro domains.

References

Van Bel, M., Silvestri, F., Weitz, E. M., Kreft, L., Botzki, A., Coppens, F., & Vandepoele, K. (2021). PLAZA 5.0: extending the scope and power of comparative and functional genomics in plants. Nucleic acids research.

Examples

data(interpro_ath)
data(interpro_ath)

Intepro annotation for Brassica oleraceae's genes

Description

The annotation data were retrieved from PLAZA Dicots 5.0.

Usage

data(interpro_bol)
data(interpro_bol)

Format

A 2-column data frame:

Gene: Character of gene IDs.
Annotation: Character of Interpro domains.

References

Examples

data(interpro_bol)
data(interpro_bol)

List BUSCO data sets

Description

List BUSCO data sets

Usage

list_busco_datasets()
list_busco_datasets()

Value

A hierarchically organized list of available data sets as returned by busco --list-datasets.

Examples

if(busco_is_installed()) {
    list_busco_datasets()
}
if(busco_is_installed()) {
    list_busco_datasets()
}

Orthogroups between Arabidopsis thaliana and Brassica oleraceae

Description

Data obtained from PLAZA Dicots 5.0.

Usage

data(og)
data(og)

Format

A 3-column data frame with the following variables:

Orthogroup: Orthogroup ID.
Species: Abbreviation for species' name.
Gene: Gene ID

References

Examples

data(og)
data(og)

Plot BUSCO summary output

Description

Plot BUSCO summary output

Usage

plot_busco(summary_df = NULL)
plot_busco(summary_df = NULL)

Arguments

summary_df

Data frame with BUSCO summary output as returned by read_busco().

Value

A ggplot object with a barplot of BUSCOs in each class.

Examples

# Single file
result_dir <- system.file("extdata", package = "cogeqc")
summary_df <- read_busco(result_dir)
# Batch mode
data(batch_summary)
plot_busco(summary_df)
plot_busco(batch_summary)
# Single file
result_dir <- system.file("extdata", package = "cogeqc")
summary_df <- read_busco(result_dir)
# Batch mode
data(batch_summary)
plot_busco(summary_df)
plot_busco(batch_summary)

Plot species-specific duplications

Description

Plot species-specific duplications

Usage

plot_duplications(stats_list = NULL)
plot_duplications(stats_list = NULL)

Arguments

stats_list

A list of data frames with Orthofinder summary stats as returned by the function read_orthofinder_stats.

Value

A ggplot object with a barplot of number of species-specific duplications.

Examples

dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_duplications(stats_list)
dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_duplications(stats_list)

Plot percentage of genes in orthogroups for each species

Description

Plot percentage of genes in orthogroups for each species

Usage

plot_genes_in_ogs(stats_list = NULL)
plot_genes_in_ogs(stats_list = NULL)

Arguments

stats_list

A list of data frames with Orthofinder summary stats as returned by the function read_orthofinder_stats.

Value

A ggplot object with a barplot of percentages of genes in orthogroups for each species.

Examples

dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_genes_in_ogs(stats_list)
dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_genes_in_ogs(stats_list)

Plot statistics on genome assemblies on the NCBI

Description

Plot statistics on genome assemblies on the NCBI

Usage

plot_genome_stats(ncbi_stats = NULL, user_stats = NULL)
plot_genome_stats(ncbi_stats = NULL, user_stats = NULL)

Arguments

ncbi_stats

A data frame of summary statistics for a particular taxon obtained from the NCBI, as obtained with the function get_genome_stats.

user_stats

(Optional) A data frame with assembly statistics obtained by the user. Statistics in this data frame are highlighted in red if this data frame is passed. A column named accession is mandatory, and it must contain unique identifiers for the genome(s) analyzed by the user. Dummy variables can be used as identifiers (e.g., "my_genome_001"), as long as they are unique. All other column containing assembly stats must have the same names as their corresponding columns in the data frame specified in ncbi_stats. For instance, stats on total number of genes and sequence length must be in columns named "gene_count_total" and "sequence_length", as in the ncbi_stats data frame.

Value

A composition of ggplot objects made with patchwork.

Examples

# Example 1: plot stats on maize genomes on the NCBI
## Obtain stats for maize genomes on the NCBI
ncbi_stats <- get_genome_stats(taxon = "Zea mays")

plot_genome_stats(ncbi_stats)

## Plot stats
# Example 2: highlight user-defined stats in the distribution
## Create a data frame of stats for fictional maize genome
user_stats <- data.frame(
    accession = "my_lovely_maize",
    sequence_length = 2.4 * 1e9,
    gene_count_total = 50000,
    CC_ratio = 1
)

plot_genome_stats(ncbi_stats, user_stats)

# Example 1: plot stats on maize genomes on the NCBI
## Obtain stats for maize genomes on the NCBI
ncbi_stats <- get_genome_stats(taxon = "Zea mays")

plot_genome_stats(ncbi_stats)

## Plot stats
# Example 2: highlight user-defined stats in the distribution
## Create a data frame of stats for fictional maize genome
user_stats <- data.frame(
    accession = "my_lovely_maize",
    sequence_length = 2.4 * 1e9,
    gene_count_total = 50000,
    CC_ratio = 1
)

plot_genome_stats(ncbi_stats, user_stats)

Plot pairwise orthogroup overlap between species

Description

Plot pairwise orthogroup overlap between species

Usage

plot_og_overlap(stats_list = NULL, clust = TRUE)
plot_og_overlap(stats_list = NULL, clust = TRUE)

Arguments

`stats_list`	A list of data frames with Orthofinder summary stats as returned by the function `read_orthofinder_stats`.
`clust`	Logical indicating whether to clust data based on overlap. Default: TRUE

Value

A ggplot object with a heatmap.

Examples

dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_og_overlap(stats_list)
dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_og_overlap(stats_list)

Plot orthogroup sizes per species

Description

Plot orthogroup sizes per species

Usage

plot_og_sizes(orthogroups = NULL, log = FALSE, max_size = NULL)
plot_og_sizes(orthogroups = NULL, log = FALSE, max_size = NULL)

Arguments

`orthogroups`	A 3-column data frame with columns Orthogroup, Species, and Gene. This data frame can be created from the 'Orthogroups.tsv' file generated by OrthoFinder with the function `read_orthogroups()`.
`log`	Logical indicating whether to transform orthogroups sizes with natural logarithms. Default: FALSE.
`max_size`	Numeric indicating the maximum orthogroup size to consider. If this parameter is not NULL, orthogroups larger than `max_size` (e.g., 100) will not be considered. Default: NULL.

Value

A ggplot object with a violin plot.

Examples

data(og)
plot_og_sizes(og, log = TRUE)
plot_og_sizes(og, max_size = 100)
plot_og_sizes(og, log = TRUE, max_size = 100)
data(og)
plot_og_sizes(og, log = TRUE)
plot_og_sizes(og, max_size = 100)
plot_og_sizes(og, log = TRUE, max_size = 100)

Plot a panel with a summary of Orthofinder stats

Description

This function is a wrapper for plot_species_tree, plot_duplications, plot_genes_in_ogs, plot_species_specific_ogs.

Usage

plot_orthofinder_stats(tree = NULL, stats_list = NULL, xlim = c(0, 1))
plot_orthofinder_stats(tree = NULL, stats_list = NULL, xlim = c(0, 1))

Arguments

`tree`	Tree object as returned by `treeio::read.`, a family of functions in the treeio* package to import tree files in multiple formats, such as Newick, Phylip, NEXUS, and others. If your species tree was inferred with Orthofinder (using STAG), the tree file is located in Species_Tree/SpeciesTree_rooted_node_labels.txt. Then, it can be imported with `treeio::read_tree(path_to_file)`.
`stats_list`	(optional) A list of data frames with Orthofinder summary stats as returned by the function `read_orthofinder_stats`. If this list is given as input, nodes will be labeled with the number of duplications.
`xlim`	Numeric vector of x-axis limits. This is useful if your node tip labels are not visible due to margin issues. Default: c(0, 1).

Value

A panel of ggplot objects.

Examples

data(tree)
dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_orthofinder_stats(tree, xlim = c(0, 1.5), stats_list = stats_list)
data(tree)
dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_orthofinder_stats(tree, xlim = c(0, 1.5), stats_list = stats_list)

Plot number of species-specific orthogroups

Description

Plot number of species-specific orthogroups

Usage

plot_species_specific_ogs(stats_list = NULL)
plot_species_specific_ogs(stats_list = NULL)

Arguments

stats_list

A list of data frames with Orthofinder summary stats as returned by the function read_orthofinder_stats.

Value

A ggplot object with a barplot of number of species-specific orthogroups for each species.

Examples

dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_species_specific_ogs(stats_list)
dir <- system.file("extdata", package = "cogeqc")
stats_list <- read_orthofinder_stats(dir)
plot_species_specific_ogs(stats_list)

Plot species tree

Description

Plot species tree

Usage

plot_species_tree(tree = NULL, xlim = c(0, 1), stats_list = NULL)
plot_species_tree(tree = NULL, xlim = c(0, 1), stats_list = NULL)

Arguments

`tree`	Tree object as returned by `treeio::read.`, a family of functions in the treeio* package to import tree files in multiple formats, such as Newick, Phylip, NEXUS, and others. If your species tree was inferred with Orthofinder (using STAG), the tree file is located in Species_Tree/SpeciesTree_rooted_node_labels.txt. Then, it can be imported with `treeio::read_tree(path_to_file)`.
`xlim`	Numeric vector of x-axis limits. This is useful if your node tip labels are not visible due to margin issues. Default: c(0, 1).
`stats_list`	(optional) A list of data frames with Orthofinder summary stats as returned by the function `read_orthofinder_stats`. If this list is given as input, nodes will be labeled with the number of duplications.

Value

A ggtree/ggplot object with the species tree.

Examples

data(tree)
plot_species_tree(tree)
data(tree)
plot_species_tree(tree)

Read and parse BUSCO's summary report

Description

Read and parse BUSCO's summary report

Usage

read_busco(result_dir = NULL)
read_busco(result_dir = NULL)

Arguments

result_dir

Path to the directory where BUSCO results are stored. This function will look for the short_summary* file (single run) or short_summary* file (batch mode).

Value

A data frame with the following variables:

Class: BUSCO class. One of Complete_SC, Complete_duplicate, Fragmented, or Missing
Frequency: Frequency of BUSCOs in each class. If BUSCO was run in batch mode, this variable will contain relative frequencies. If BUSCO was run for a single file, it will contain absolute frequencies.
Lineage: Name of the lineage dataset used.
File (batch mode only): Name of the input FASTA file.

Examples

result_dir <- system.file("extdata", package = "cogeqc")
df <- read_busco(result_dir)
result_dir <- system.file("extdata", package = "cogeqc")
df <- read_busco(result_dir)

Read and parse Orthofinder summary statistics

Description

Read and parse Orthofinder summary statistics

Usage

read_orthofinder_stats(stats_dir = NULL)
read_orthofinder_stats(stats_dir = NULL)

Arguments

stats_dir

Path to directory containing Orthofinder's comparative genomics statistics. In your Orthofinder results directory, this directory is named Comparative_Genomics_Statistics.

Value

A list of data frames with the following elements:

stats A data frame of summary stats per species with the following variables:

Species

Factor of species names.

N_genes

Numeric of number of genes.

N_genes_in_OGs

Numeric of number of genes in orthogroups.

Perc_genes_in_OGs

Numeric of percentage of genes in orthogroups.

N_ssOGs

Numeric of number of species-specific orthogroups.

N_genes_in_ssOGs

Numeric of number of genes in species-specific orthogroups.

Perc_genes_in_ssOGs

Numeric of percentage of genes in species-specific orthogroups.

Dups

Integer with number of duplications per species.
og_overlap A symmetric data frame of pairwise orthogroup overlap between species.
duplications A 2-column data frame with node IDs in the first column and number of gene duplications (50% support) in the second column.

Examples

stats_dir <- system.file("extdata", package = "cogeqc")
ortho_stats <- read_orthofinder_stats(stats_dir)
stats_dir <- system.file("extdata", package = "cogeqc")
ortho_stats <- read_orthofinder_stats(stats_dir)

Read and parse orthogroups file created by OrthoFinder

Description

This function converts the orthogroups file named Orthogroups.tsv to a parsed data frame.

Usage

read_orthogroups(orthogroups_path = NULL)
read_orthogroups(orthogroups_path = NULL)

Arguments

orthogroups_path

Path to Orthogroups/Orthogroups.tsv file generated by OrthoFinder.

Value

A 3-column data frame with orthogroups, species IDs and gene IDs, respectively.

Author(s)

Fabricio Almeida-Silva

Examples

path <- system.file("extdata", "Orthogroups.tsv.gz", package = "cogeqc")
og <- read_orthogroups(path)
path <- system.file("extdata", "Orthogroups.tsv.gz", package = "cogeqc")
og <- read_orthogroups(path)

Run BUSCO assessment of assembly and annotation quality

Description

Run BUSCO assessment of assembly and annotation quality

Usage

run_busco(
  sequence = NULL,
  outlabel = NULL,
  mode = c("genome", "transcriptome", "proteins"),
  lineage = NULL,
  auto_lineage = NULL,
  force = FALSE,
  threads = 1,
  outpath = NULL,
  download_path = tempdir()
)
run_busco(
  sequence = NULL,
  outlabel = NULL,
  mode = c("genome", "transcriptome", "proteins"),
  lineage = NULL,
  auto_lineage = NULL,
  force = FALSE,
  threads = 1,
  outpath = NULL,
  download_path = tempdir()
)

Arguments

`sequence`	An object of class DNAStringSet/AAStringSet/RNAStringSet or path to FASTA file with the genome, transcriptome, or protein sequences to be analyzed. If there are many FASTA files in a directory, you can input the path to this directory, so BUSCO will be run in all FASTA files inside it.
`outlabel`	Character with a recognizable short label for analysis directory and files.
`mode`	Character with BUSCO mode. One of 'genome', 'transcriptome', or 'proteins'.
`lineage`	Character with name of lineage to be used.
`auto_lineage`	Character indicating whether BUSCO should determine optimum lineage path automatically. One of 'euk', 'prok', 'all', or NULL. If 'euk', it will determine optimum lineage path on eukaryote tree. If 'prok', it will determine optimum lineage path on non-eukaryote trees. If 'all', it will determine optimum lineage path for all trees. If NULL, it will not automatically determine lineage, and lineage must be manually specified. Default: NULL.
`force`	Logical indicating whether existing runs with the same file names should be overwritten. Default: FALSE.
`threads`	Numeric with the number of threads/cores to use. Default: 1.
`outpath`	Path to results directory. If NULL, results will be stored in the current working directory. Default: NULL.
`download_path`	Path to directory where BUSCO datasets will be stored after downloading. Default: tempdir().

Value

A character vector with the names of subdirectories and files in the results directory.

Examples


sequence <- system.file("extdata", "Hse_subset.fa", package = "cogeqc")
download_path <- paste0(tempdir(), "/datasets")
if(busco_is_installed()) {
    run_busco(sequence, outlabel = "Hse", mode = "genome",
              lineage = "burkholderiales_odb10",
              outpath = tempdir(), download_path = download_path)
}

sequence <- system.file("extdata", "Hse_subset.fa", package = "cogeqc")
download_path <- paste0(tempdir(), "/datasets")
if(busco_is_installed()) {
    run_busco(sequence, outlabel = "Hse", mode = "genome",
              lineage = "burkholderiales_odb10",
              outpath = tempdir(), download_path = download_path)
}

Synteny network for Brassica oleraceae, B. napus, and B. rapa

Description

Synteny network for Brassica oleraceae, B. napus, and B. rapa

Usage

data(synnet)
data(synnet)

Format

A 2-column data frame with the variables anchor1 and anchor2, containing names of loci in anchor 1 and anchor 2, respectively.

References

Zhao, T., & Schranz, M. E. (2019). Network-based microsynteny analysis identifies major differences and genomic outliers in mammalian and angiosperm genomes. Proceedings of the National Academy of Sciences, 116(6), 2165-2174.

Examples

data(synnet)
data(synnet)

Species tree for model species

Description

The data used to create this object was retrieved from Orthofinder's example output for model species, available in https://bioinformatics.plants.ox.ac.uk/davidemms/public_data/.

Usage

data(tree)
data(tree)

Format

An object of class "phylo" as returned by treeio::read.tree().

References

Emms, D. M., & Kelly, S. (2019). OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome biology, 20(1), 1-14.

Examples

data(tree)
data(tree)

Package 'cogeqc'

Help Index

Assess orthogroup inference based on functional annotation

Description

Usage

Arguments

Value

Examples

Assess synteny network based on graph properties

Description

Usage

Arguments

Details

Value

Examples

Assess list of synteny networks as in assess_synnet

Description

Usage

Arguments

Value

Examples

BUSCO summary output for batch mode

Description

Usage

Format

Examples

Check if BUSCO is installed

Description

Usage

Value

Examples

Calculate homogeneity scores for orthogroups

Description

Usage

Arguments

Details

Value

Examples

Compare user-defined assembly statistics with statistics of NCBI genomes

Description

Usage

Arguments

Details

Value

Examples

Compare inferred orthogroups to a reference set

Description

Usage

Arguments

Details

Value

Examples

Goodness of fit test for the scale-free topology model

Description

Usage

Arguments

Value

Examples

Get summary statistics for genomes on NCBI using the NCBI Datasets API

Description

Usage

Arguments

Details

Value

Examples

Intepro annotation for Arabidopsis thaliana's genes

Description

Usage

Format

References

Examples

Intepro annotation for Brassica oleraceae's genes

Description

Usage

Format

References

Examples

List BUSCO data sets

Description

Usage

Assess list of synteny networks as in `assess_synnet`