In almeidasilvaf/syntenet: Inference And Analysis Of Synteny Networks

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    crop = NULL ## Related to https://stat.ethz.ch/pipermail/bioc-devel/2020-April/016656.html
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Introduction

The analysis of synteny (i.e., conserved gene content and order in a genomic segment across species) can help understand the trajectory of duplicated genes through evolution. In particular, synteny analyses are widely used to investigate the evolution of genes derived from whole-genome duplication (WGD) events, as they can reveal genomic rearrangements that happened following the duplication of all chromosomes. However, synteny analysis are typically performed in a pairwise manner, which can be difficult to explore and interpret when comparing several species. To understand global patterns of synteny, @zhao2017network proposed a network-based approach to analyze synteny. In synteny networks, genes in a given syntenic block are represented as nodes connected by an edge. Synteny networks have been used to explore, among others, global synteny patterns in mammalian and angiosperm genomes [@zhao2019network], the evolution of MADS-box transcription factors [@zhao2017phylogenomic], and infer a microsynteny-based phylogeny for angiosperms [@zhao2021whole]. r BiocStyle::Biocpkg("syntenet") is a package that can be used to infer synteny networks from protein sequences and perform downstream network analyses that include:

• Network clustering using the Infomap algorithm;

• Phylogenomic profiling, which consists in identifying which species contain which clusters. This analysis can reveal highly conserved synteny clusters and taxon-specific ones (e.g., family- and order-specific clusters);

• Microsynteny-based phylogeny reconstruction with maximum likelihood, which can be achieved by inferring a phylogeny from a binary matrix of phylogenomic profiles with IQ-TREE.

Installation

r BiocStyle::Biocpkg("syntenet") can be installed from Bioconductor with the following code:

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

BiocManager::install("syntenet")

# Load package after installation
library(syntenet)

Data description

For this vignette, we will use the proteomes and gene annotation of the algae species Ostreococcus lucimarinus and Ostreococcus sp RCC809, which were obtained from Pico-PLAZA 3.0 [@vandepoele2013pico].

# Protein sequences
data(proteomes)
head(proteomes)

# Annotation (ranges)
data(annotation)
head(annotation)

Importing data to the R session

To detect synteny and infer synteny networks, r BiocStyle::Biocpkg("syntenet") requires two objects as input:

• seq: A list of AAStringSet objects containing the translated sequences of primary transcripts for each species.
• annotation: A GRangesList or CompressedGRangesList object containing the coordinates for the genes in seq.

If you have whole-genome protein sequences in FASTA files, store all FASTA files in the same directory and use the function fasta2AAStringSetlist() to read all FASTA files into a list of AAStringSet objects.

Likewise, if you have gene annotation in GFF/GFF3/GTF files, store all files in the same directory and use the function gff2GRangesList() to read all GFF/GFF3/GTF files into a GRangesList object.

For a demonstration, we will read example FASTA and GFF3 files stored in subdirectories named sequences/ and annotation/, which are located in the extdata/ directory of this package.

From FASTA files to a list of AAStringSet objects

Here is how you can use fasta2AAStringSetlist() to read FASTA files in a directory as a list of AAStringSet objects.

# Path to directory containing FASTA files
fasta_dir <- system.file("extdata", "sequences", package = "syntenet")
fasta_dir

dir(fasta_dir) # see the contents of the directory

# Read all FASTA files in `fasta_dir`
aastringsetlist <- fasta2AAStringSetlist(fasta_dir)
aastringsetlist

And that's it! Now you have a list of AAStringSet objects.

From GFF/GTF files to a GRangesList object

Here is how you can use gff2GRangesList() to read GFF/GFF3/GTF files in a directory as a GRangesList object.

# Path to directory containing FASTA files
gff_dir <- system.file("extdata", "annotation", package = "syntenet")
gff_dir

dir(gff_dir) # see the contents of the directory

# Read all FASTA files in `fasta_dir`
grangeslist <- gff2GRangesList(gff_dir)
grangeslist

And now you have a GRangesList object.

Data preprocessing

The first part of the pipeline consists in processing the data to make it match a standard structure. However, before processing the data for synteny detection, you must use the function check_input() to check if your data can enter the pipeline. This function checks the input data for 3 required conditions:

1. Names of seq list (i.e., names(seq)) match the names of annotation GRangesList/CompressedGRangesList (i.e., names(annotation))

2. For each species (list elements), the number of sequences in seq is not greater than the number of genes in annotation. This is a way to ensure users do not input the translated sequences for multiple isoforms of the same gene (generated by alternative splicing). Ideally, the number of sequences in seq should be equal to the number of genes in annotation, but this may not always stand true because of non-protein-coding genes.

3. For each species, sequence names (i.e., names(seq[[x]]), equivalent to FASTA headers) match gene names in annotation. By default, r BiocStyle::Biocpkg("syntenet") looks for gene IDs in a column named "gene_id" in the GRanges objects (default field in GFF3 files). If your gene IDs are in a different column (e.g., "Name"), you can specify it in the gene_field parameter of check_input() and process_input().

Let's check if the example data sets satisfy these 3 criteria:

check_input(proteomes, annotation)

As you can see, the data passed the checks. Now, let's process them with the function process_input().

This function processes the input sequences and annotation to:

1. Remove whitespace and anything after it in sequence names (i.e., names(seq[[x]]), which is equivalent to FASTA headers), if there is any.

2. Add a unique species identifier to sequence names. The species identifier consists of the first 3-5 strings of the element name. For instance, if the first element of the seq list is named "Athaliana", each sequence in it will have an identifier "Atha_" added to the beginning of each gene name (e.g., Atha_AT1G01010).

3. If sequences have an asterisk (*) representing stop codon, remove it.

4. Add a unique species identifier (same as above) to gene and chromosome names of each element of the annotation GRangesList/CompressedGRangesList.

5. Filter each element of the annotation GRangesList/CompressedGRangesList to keep only seqnames, ranges, and gene ID.

Let's process our input data:

pdata <- process_input(proteomes, annotation)

# Looking at the processed data
pdata$seq
pdata$annotation

Synteny network inference

Now that we have our processed data, we can infer the synteny network. To detect synteny, we need the tabular output from BLASTp [@altschul1997gapped] or similar programs. To get that, you can use the functions run_diamond(), or run_last(), which runs DIAMOND [@buchfink2021sensitive] or last [@kielbasa2011adaptive] from the R session and automatically parses its output to a list of data frames [^1].

[^1]: Alternative: if you want to use a different program for similarity searches, you can run it on the command line, save the output in a DIAMOND/BLAST-like tabular format, and read the output files as a list of data frames with the function read_diamond() or read_last() (see the FAQ for details).

Let's demonstrate how run_diamond() works. Needless to say, you need to have DIAMOND installed in your machine and in your PATH to run this function. To check if you have DIAMOND installed, use the function diamond_is_installed() [^2].

[^2]: Note: in the code chunk below, the if statement is not required. We just added it to make sure that the function run_diamond() is only executed if DIAMOND is installed, to avoid problems when building this vignette in machines that do not have DIAMOND installed. If you want to reproduce the code in this vignette and do not have DIAMOND installed, you can use the example output of run_diamond() stored in the blast_list object (loaded with data(blast_list)).

data(blast_list)
if(diamond_is_installed()) {
    blast_list <- run_diamond(seq = pdata$seq)
}

The output of run_diamond() is a list of data frames containing the tabular output of all-vs-all DIAMOND searches. Let's take a look at it.

# List names
names(blast_list)

# Inspect first data frame
head(blast_list$Olucimarinus_Olucimarinus)

Now, we can use this list of DIAMOND data frames to detect synteny. Here, we reimplemented the popular MCScanX algorithm [@wang2012mcscanx], originally written in C++, using the r BiocStyle::CRANpkg("Rcpp") [@eddelbuettel2011rcpp] framework for R and C++ integration. This means that r BiocStyle::Biocpkg("syntenet") comes with a native version of the MCScanX algorithm, so you can run MCScanX in R without having to install it yourself. Amazing, huh?

To detect synteny and infer the synteny network, use the function infer_syntenet(). The output is a network represented as a so-called edge list (i.e., a 2-column data frame with node 1 and node 2 in columns 1 and 2, respectively).

# Infer synteny network
net <- infer_syntenet(blast_list, pdata$annotation)

# Look at the output
head(net)

In a synteny network, each row of the edge list represents an anchor pair. In the edge list above, for example, the genes r net[1,1] and r net[1,2] are an anchor pair (i.e., duplicates derived from a large-scale duplication event).

Note that gene IDs are preceded by IDs created with process_input(). Under the hood, process_input() uses the function create_species_id_table() to create unique IDs from the names of the seq and annotation lists. To obtain a data frame of all IDs and their corresponding species, you can use the following code:

# Get a 2-column data frame of species IDs and names
id_table <- create_species_id_table(names(proteomes))

id_table

Phylogenomic profiling

After inferring the synteny network, the first thing you would want to do is cluster your network and identify which phylogenetic groups are contained in each cluster. This is what we call phylogenomic profiling. This way, you can identify clade-specific clusters, and highly conserved clusters, for instance. Here, we will use an example network of BUSCO genes for 25 eudicot species, which was obtained from @zhao2019network.

To obtain the phylogenomic profiles, you first need to cluster your network. This can be done with cluster_network(). [^3]

[^3]: Friendly tip: r BiocStyle::Biocpkg("syntenet") uses the Infomap algorithm to cluster networks, which has been shown to have the best performance [@zhao2019network]. However, you can use any other network clustering method implemented in the cluster_ family of functions from the r BiocStyle::CRANpkg("igraph") package by passing the function directly to the clust_function parameter (see ?cluster_network for details). Importantly, the Infomap algorithm (default clustering method) assigns each gene to a single cluster. However, for some cases (e.g., detection of tandem arrays), you may want to use an algorithm that allows community overlap (i.e., a gene can be part of more than one cluster). If this is your case, we recommend the clique percolation algorithm, which is implemented in the R package r BiocStyle::CRANpkg("CliquePercolation") [@lange2021cliquepercolation].

# Load example data and take a look at it
data(network)
head(network)

# Cluster network
clusters <- cluster_network(network)
head(clusters)

Now that each gene has been assigned to a cluster, we can identify the phylogenomic profiles of each cluster. This function returns a matrix of phylogenomic profiles, with clusters in rows and species in columns.

# Phylogenomic profiling
profiles <- phylogenomic_profile(clusters)

# Exploring the output
head(profiles)

As a plot is worth a thousand words (or numbers), you can use the function plot_profiles() to visualize the phylogenomic profiles as a heatmap with species in rows and synteny network clusters in columns. The heatmap generated by this function is highly customizable by users. Some important remarks are:

1. You can add a legend for species metadata (e.g., taxonomic information) by passing a 2-column data frame to the parameter species_annotation.

2. Columns (network clusters) are grouped with Ward's clustering on a matrix of distances. The method to compute the distance matrix can be defined by users in parameters dist_function and dist_params. By default, it uses the function stats::dist() with parameter method = "euclidean". Likewise, the function to cluster the distance matrix and additional parameters can be specified in clust_function and clust_params. By default, it uses stats::hclust with parameter method = "ward.D".

3. The order in which species are displayed can be defined by users in parameter cluster_species. We strongly recommend passing a vector of species order that matches the species tree, so that patterns can be explored in a phylogenetic context. Importantly, if the character vector is named, vector names will be used as species names in the plot. This a nice way to replace species abbreviations with their full names.

Here, to briefly demonstrate how to play with the parameters we just mentioned in the 3 remarks above, we will:

• Create a vector with the order in which we want species to be displayed, with longer species names in vector names.

• Create a metadata data frame containing the family of each species.

• Use the function dsvdis() from the r BiocStyle::CRANpkg("labdsv") package to calculate Ruzicka distances when clustering columns.

# 1) Create a named vector of custom species order to plot
species_order <- setNames(
    # vector elements
    c(
        "vra", "van", "pvu", "gma", "cca", "tpr", "mtr", "adu", "lja",
        "Lang", "car", "pmu", "ppe", "pbr", "mdo", "roc", "fve",
        "Mnot", "Zjuj", "jcu", "mes", "rco", "lus", "ptr"
    ),
    # vector names
    c(
        "V. radiata", "V. angularis", "P. vulgaris", "G. max", "C. cajan",
        "T. pratense", "M. truncatula", "A. duranensis", "L. japonicus",
        "L. angustifolius", "C. arietinum", "P. mume", "P. persica",
        "P. bretschneideri", "M. domestica", "R. occidentalis", 
        "F. vesca", "M. notabilis", "Z. jujuba",
        "J. curcas", "M. esculenta", "R. communis", 
        "L. usitatissimum", "P. trichocarpa"
    )
)
species_order

# 2) Create a metadata data frame containing the family of each species
species_annotation <- data.frame(
    Species = species_order,
    Family = c(
        rep("Fabaceae", 11), rep("Rosaceae", 6),
        "Moraceae", "Ramnaceae", rep("Euphorbiaceae", 3), 
        "Linaceae", "Salicaceae"
    )
)
head(species_annotation)


# 3) Plot phylogenomic profiles, but using Ruzicka distances
plot_profiles(
    profiles, 
    species_annotation, 
    cluster_species = species_order, 
    dist_function = labdsv::dsvdis,
    dist_params = list(index = "ruzicka")
)

The heatmap is a nice way to observe patterns. For instance, you can see some Rosaceae-specific clusters, Fabaceae-specific clusters, and highly conserved ones as well.

If you want to explore in more details the group-specific clusters, you can use the function find_GS_clusters(). For that, you only need to input the profiles matrix and a data frame of species annotation (i.e., species groups).

# Find group-specific clusters
gs_clusters <- find_GS_clusters(profiles, species_annotation)

head(gs_clusters)

# How many family-specific clusters are there?
nrow(gs_clusters)

As you can see, there are r nrow(gs_clusters) family-specific clusters in the network. Let's plot a heatmap of group-specific clusters only.

# Filter profiles matrix to only include group-specific clusters
idx <- rownames(profiles) %in% gs_clusters$Cluster
p_gs <- profiles[idx, ]

# Plot heatmap
plot_profiles(
    p_gs, species_annotation, 
    cluster_species = species_order, 
    cluster_columns = TRUE
)

Pretty cool, huh? You can also visualize clusters as a network plot with the function plot_network(). For example, let's visualize the group-specific clusters.

# 1) Visualize a network of first 5 GS-clusters
id <- gs_clusters$Cluster[1:5]
plot_network(network, clusters, cluster_id = id)

# 2) Coloring nodes by family
genes <- unique(c(network$node1, network$node2))
gene_df <- data.frame(
    Gene = genes,
    Species = unlist(lapply(strsplit(genes, "_"), head, 1))
)
gene_df <- merge(gene_df, species_annotation)[, c("Gene", "Family")]
head(gene_df)

plot_network(network, clusters, cluster_id = id, color_by = gene_df)

# 3) Interactive visualization (zoom out and in to explore it)
plot_network(
    network, clusters, cluster_id = id, 
    interactive = TRUE, dim_interactive = c(500, 300)
)

Microsynteny-based phylogeny reconstruction

Finally, you can use the information on presence/absence of clusters in each species to reconstruct a microsynteny-based phylogeny.

To do that, you first need to binarize the profiles matrix (0s and 1s representing absence and presence, respectively) and transpose it. This can be done with binarize_and_tranpose().

bt_mat <- binarize_and_transpose(profiles)

# Looking at the first 5 rows and 5 columns of the matrix
bt_mat[1:5, 1:5]

Now, you can use this transposed binary matrix as input to IQ-TREE [@minh2020iq] to infer a phylogeny. To do so, you can use the function infer_microsynteny_phylogeny(), which allows you to run IQ-TREE from an R session [^4]. You need to have IQ-TREE installed in your machine and in your PATH to run this function. You can check if you have IQ-TREE installed with iqtree_is_installed().

[^4]: Alternative: if you want to use a different program rather than IQ-TREE, you can use the function profiles2phylip() to write the transposed binary matrix to a PHYLIP file and run your favorite program on the command line. However, when inferring a phylogeny from phylogenomic profiles, you need to make sure that the program you are using supports substitution models for binary data. In IQ-TREE, for instance, using binary, morphological models requires passing parameters -st MORPH.

For the sake of demonstration, we will infer a phylogeny with infer_microsynteny_phylogeny() using the profiles for BUSCO genes for six legume species only. We will also remove non-variable sites (i.e., clusters that are present in all species or absent in all species). However, we're only using this filtered data set for speed issues. For real-life applications, the best thing to do is to include phylogenomic profiles for all genes (not only BUSCO genes).

# Leave only 6 legume species and P. mume as an outgroup for testing purposes
included <- c("gma", "pvu", "vra", "van", "cca", "pmu")
bt_mat <- bt_mat[rownames(bt_mat) %in% included, ]

# Remove non-variable sites
bt_mat <- bt_mat[, colSums(bt_mat) != length(included)]

if(iqtree_is_installed()) {
    phylo <- infer_microsynteny_phylogeny(bt_mat, outgroup = "pmu", 
                                          threads = 1)
}

The output of infer_microsynteny_phylogeny() is a character vector with paths to the output files from IQ-TREE. Usually, you are interested in the file ending in .treefile. This is the species tree in Newick format, and it can be visualized with your favorite tree viewer. We strongly recommend using the read.tree() function from the Bioconductor package r BiocStyle::Biocpkg("treeio") [@wang2020treeio] to read the tree, and visualizing it with the r BiocStyle::Biocpkg("ggtree") Bioc package [@yu2017ggtree]. For example, let's visualize a microsynteny-based phylogeny for 123 angiosperm species, whose phylogenomic profiles were obtained from @zhao2021whole.

data(angiosperm_phylogeny)

# Plotting the tree
library(ggtree)
ggtree(angiosperm_phylogeny) +
    geom_tiplab(size = 3) +
    xlim(0, 0.3)

syntenet as a synteny detection tool

In some cases, users do not want to infer a synteny network, but only want to identify syntenic regions within a single genome or between two genomes. This can be accomplished with the functions intraspecies_synteny() and interspecies_synteny(). In fact, these functions are used under the hood by infer_syntenet() to infer a network.

To detect synteny, you will need:

1. A list of DIAMOND/BLAST data frames as returned by run_diamond(). For intraspecies_synteny(), only intraspecies comparisons must be included; for interspecies_synteny(), only interspecies comparisons must be included.
2. A GRangesList object containing the processed annotation for your species of interest, as returned by process_input().

The output of intraspecies_synteny() and interspecies_synteny() is the path to the .collinearity files generated by MCScanX [@wang2012mcscanx], which can be read and parsed with the parse_collinearity() function.

To demonstrate the usage of intraspecies_synteny(), let's identify syntenic regions in the genome of Saccharomyces cerevisiae. The processed annotation and DIAMOND output are stored in the example data sets scerevisiae_annot and scerevisiae_diamond.

# Load data
data(scerevisiae_annot)
data(scerevisiae_diamond)

# Take a look at the data
head(scerevisiae_annot) 

names(scerevisiae_diamond)
head(scerevisiae_diamond$Scerevisiae_Scerevisiae)

# Detect intragenome synteny
intra_syn <- intraspecies_synteny(
    scerevisiae_diamond, scerevisiae_annot
)

intra_syn # see where the .collinearity file is

# Read .collinearity file
scerevisiae_syn <- parse_collinearity(intra_syn)
head(scerevisiae_syn)

To demonstrate the usage of interspecies_synteny(), let's detect syntenic regions between the genomes of Ostreococcus lucimarinus and Ostreococcus sp RCC809. For these genomes, we already have processed annotation and the DIAMOND list in the objects pdata and blast_list, obtained in previous sections of this vignette.

# Keep only interspecies DIAMOND comparisons
names(blast_list)
diamond_inter <- blast_list[c(2, 3)]

# Double-check if we have processed annotation for these 2 species
names(pdata$annotation)

# Detect interspecies synteny
intersyn <- interspecies_synteny(diamond_inter, pdata$annotation)

intersyn # see where the .collinearity file is

# Read .collinearity file
ostreoccocus_syn <- parse_collinearity(intersyn)
head(ostreoccocus_syn)

Note that parse_collinearity() returns a data frame of anchor pairs by default, but you can also obtain synteny block information, or a combination of both by changing the argument to the as parameter (check the man page with ?parse_collinearity for details):

# 1) Get anchors with `parse_collinearity()`
anchors <- parse_collinearity(intra_syn)
head(anchors)

# 2) Get synteny block with `parse_collinearity()`
blocks <- parse_collinearity(intra_syn, as = "blocks")
head(blocks)

# 3) Get synteny blocks and anchor pairs in a single data frame
all <- parse_collinearity(intra_syn, as = "all")
head(all)

FAQ {.unnumbered}

How do I execute an external dependency that is not in my PATH? {.unnumbered}

If you have DIAMOND and/or IQ-TREE installed, but in a directory that is not in your PATH, you can add this given directory to your PATH with the function Sys.setenv().

For example, suppose your DIAMOND binary is in /home/username/bioinfo_tools. To add this directory to your PATH, you would run:

# Add example directory /home/username/bioinfo_tools to PATH
Sys.setenv(
    PATH = paste(
        Sys.getenv("PATH"), "/home/username/bioinfo_tools", sep = ":"
    )
)

Note that your R PATH is not the same as your system's PATH. Thus, even if you add the directory /home/username/bioinfo_tools to your system's path (e.g., by editing your ~/.bashrc file if you are on Linux), you would still need to update your R PATH.

Can I run the DIAMOND searches on the command line and import the results? {.unnumbered}

Yes. This case is quite common for users who have a large amount of data and want to execute DIAMOND or any similarity search program in an HPC cluster (by submitting a job with qsub to execute a Bash script).

To do that, you will have to follow the 3 steps below:

1. Export the processed sequences (as returned by process_input()) with the function export_sequences(). This function will write the sequences to FASTA files in the directory specified in the outdir parameter.

2. Navigate (i.e., cd) to the directory specified in outdir, where the FASTA files are, and execute all-vs-all similarity searches. The output files must be named "[species]_[species].tsv", where [species] indicates the basename of the FASTA files (e.g., "speciesA" for a FASTA file named speciesA.fasta). For DIAMOND, you can use the following code (with adaptations, if you prefer):

```{bash eval=FALSE}

!/bin/bash

Create output directories dbs and results

mkdir -p dbs mkdir -p results

1. Make dbs for each species

for seqfile in *.fasta do dbfile="dbs/$(basename "$seqfile" .fasta)" diamond makedb --in "$seqfile" -d "$dbfile" --quiet done

2. Perform all-vs-all pairwise similarity searches

species=( $(basename -s .fasta *.fasta) ) for (( i=0; i<${#species[@]}; i++ )) do query="${species[$i]}.fasta" for (( j=0; j<${#species[@]}; j++ )) do db="dbs/${species[$j]}" outfile="results/${species[$i]}_${species[$j]}.tsv" diamond blastp -q "$query" -d "$db" -o "$outfile" \ --max-hsps 1 -k 5 --quiet done done

3. Read the output of the similarity searches into a list of 
data frames with the `read_diamond()` function. As input, `read_diamond()`
takes the path to the directory containing the DIAMOND/BLAST output files.

## My sequence names do not match gene IDs in the annotation. What should I do? {.unnumbered}

When the names of your sequences (equivalent to FASTA headers) do not match
the gene IDs in your `GRanges` objects, __syntenet__ throws the following error:

> Sequence names in 'seq' do not match gene names in 'annotation'.

In most (if not all) of the cases, this error happens because users have 
protein IDs as sequence names, and __syntenet__ looks for gene IDs 
(i.e., using rows of the `GRanges` objects for which the 
column *type* is "gene"). This is the case, for instance, for data obtained
from NCBI's RefSeq database. To solve the issue, you need to replace 
**protein/transcript IDs** with **gene IDs** in sequence names, which can
be done with the function `collapse_protein_ids()`. 


To demonstrate how this works, let's explore an example data set containing
protein sequences and gene annotation obtained from RefSeq. The data contains
information on a subset of 16 genes from the fish species *Alosa alosa*, and
it is stored in the `extdata/RefSeq_parsing_example` directory of this
package.

```r
# Path to directory containing data
data_dir <- system.file(
    "extdata", "RefSeq_parsing_example", package = "syntenet"
)
dir(data_dir)

# Reading the files to a format that syntenet understands
seqs <- fasta2AAStringSetlist(data_dir)
annot <- gff2GRangesList(data_dir)

# Taking a look at the data
seqs
head(names(seqs$Aalosa))

annot

The first problem we can observe is that sequence names have additional text describing the sequences (e.g., "GSK3-beta..."), and we must have only the IDs. To solve this issue, we can remove whitespace and everything that comes after it.

# Remove whitespace and everything after it
names(seqs$Aalosa) <- gsub(" .*", "", names(seqs$Aalosa))

# Taking a look at the new names
head(names(seqs$Aalosa))

Great, we removed the unnecessary text! However, there is still a problem: sequence names start with XP_..... If you look closer at the first row of annot$Aalosa (which contains ranges for genes), you will notice that none of the columns contain such XP_... IDs. This is because RefSeq uses such IDs for CDS, not for genes. Let's check if we can indeed find the XP_... IDs in rows that have "CDS" in the type column.

# Show only rows for which `type` is "CDS"
head(annot$Aalosa[annot$Aalosa$type == "CDS"])

The XP_... IDs can be found in the Name column. Note also that the gene IDs, which are what we need for syntenet, are in the gene column. To collapse protein IDs to gene IDs with collapse_protein_ids(), we will need to create a list of 2-column data frames containing the correspondence between protein IDs and gene IDs for each species. In this example, we can do that by extracting the columns Name and gene from rows that represent CDS ranges.

# Create a list of data frames containing protein-to-gene ID correspondences
protein2gene <- lapply(annot, function(x) {

    # Extract only CDS ranges
    cds_ranges <- x[x$type == "CDS"]

    # Create the ID correspondence data frame
    df <- data.frame(
        protein_id = cds_ranges$Name,
        gene_id = cds_ranges$gene
    )

    # Remove duplicate rows
    df <- df[!duplicated(df$protein_id), ]

    return(df)
})

# Taking a look at the list
protein2gene

Finally, we can pass the list of sequences and the list of ID correspondences to collapse_protein_ids(), which will return a list of AAStringSet objects with gene IDs in sequence names.

# Collapse protein IDs to gene IDs in list of sequences
new_seq <- collapse_protein_ids(seqs, protein2gene)

# Looking at the new sequences
new_seq

As you can see, protein IDs have been replaced with gene IDs. If there are multiple proteins for the same gene (i.e., different isoforms), the function keeps only the longest sequence (also known as protein products of the primary transcript). This way, the number of sequences will never be greater than the number of genes, which is what syntenet expects.

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This document was created under the following conditions:

sessionInfo()

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almeidasilvaf/syntenet documentation built on Dec. 23, 2024, 6:26 a.m.