---
title: "From a few sequences to a complex map in minutes (old version)"
author: "Thomas Hackl"
date: "`r format(Sys.Date())`"
resource_files:
- emales/emales-p1.png
- emales/emales-p2.png
- emales/emales-p3.png
- emales/emales-p4.png
- emales/emales-p5.png
- emales/emales-p6.png
- emales/emales-p7.png
vignette: >
%\VignetteIndexEntry{Emales}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
**DISCLAIMER: I've created this demo with an early version of gggenomes. Not all of the code will function with current releases. However, the general workflow both with respect to the external tools and gggenomes code is still valid and this demo should therefore be understood as an hopefully inspiring guide - not as an exactly reproducible code example (Those kind of examples you can find in the examples sections of the documentation).**
This is a real-life example demonstrating the use of `gggenomes` to explore
viral genomes. We start with just a bunch of viral contigs and ask: Is there
anything interesting going on here?
We use a few bioinformatics commandline tools to run some analyzes, and
visualize the results using `gggenomes`. By successively adding new data as new
tracks we build a rich plot that ultimatly reveals novel insights into an
exciting system.
This example is bundled `data(package="gggenomes")`
To rerun the this example including the bioinformatics analyzes download the
[raw data](https://github.com/thackl/gggenomes/raw/master/data-raw/emales.tgz)
from github.
### Read in the genomes
We start with a fasta file of 33 viral genomes. We read sequence length
and some metadata from the header lines using \`read~fai~\`...
``` {r eval=FALSE}
library(gggenomes)
# parse sequence length and some metadata from fasta file
emale_seqs <- read_fai("emales.fna") %>%
tidyr::extract(seq_desc, into = c("emale_type", "is_typespecies"), "=(\\S+) \\S+=(\\S+)",
remove=F, convert=T) %>%
dplyr::arrange(emale_type, length)
# plot the genomes - first six only to keep it simple for this example
emale_seqs_6 <- emale_seqs[1:6,]
p1 <- gggenomes(emale_seqs_6) +
geom_seq() + geom_bin_label()
p1
```
### Annotate genes
``` {bash eval=FALSE}
# https://github.com/thackl/seq-scripts
seq-join -n emales-concat < emales.fna > emales-concat.fna
# Annotate genes | https://github.com/hyattpd/Prodigal
prodigal -n -t emales-prodigal.train -i emales-concat.fna
prodigal -t emales-prodigal.train -i emales.fna -o emales-prodigal.gff -f gff
# A little help to clean up the prodigal gff | https://github.com/thackl/seq-scripts
gff-clean emales-prodigal.gff > emales.gff
```
``` {r eval=FALSE}
emale_genes <- read_gff("emales.gff") %>%
dplyr::rename(feature_id=ID) %>% # we'll need this later
dplyr::mutate(gc_cont=as.numeric(gc_cont)) # per gene GC-content
p2 <- gggenomes(emale_seqs_6, emale_genes) +
geom_seq() + geom_bin_label() +
geom_gene(aes(fill=gc_cont)) +
scale_fill_distiller(palette="Spectral")
p2
```
### Find terminal inverted repeats
It is known that these type of viruses often have linear genomes with
terminal inverted repeats (TIRs). So let's look for those next.
``` {bash eval=FALSE}
# split into one genome per file | https://bioinf.shenwei.me/seqkit/
seqkit split -i emales.fna
# self-align opposite strands
for fna in `ls emales.fna.split/*.fna`; do
minimap2 -c -B5 -O6 -E3 --rev-only $fna $fna > $fna.paf;
done;
cat emales.fna.split/*.paf > emales-tirs.paf
```
``` {r eval=FALSE}
# prefilter hits by minimum length and maximum divergence
emale_tirs_paf <- read_paf("emales-tirs.paf") %>%
dplyr::filter(seq_id1 == seq_id2 & start1 < start2 & map_length > 99 & de < 0.1)
emale_tirs <- bind_rows(
dplyr::select(emale_tirs_paf, seq_id=seq_id1, start=start1, end=end1, de),
dplyr::select(emale_tirs_paf, seq_id=seq_id2, start=start2, end=end2, de))
p3 <- gggenomes(emale_seqs_6, emale_genes, emale_tirs) +
geom_seq() + geom_bin_label() +
geom_feature(size=5) +
geom_gene(aes(fill=gc_cont)) +
scale_fill_distiller(palette="Spectral")
p3
```
### Compare genome synteny
``` {bash eval=FALSE}
# All-vs-all alignment | https://github.com/lh3/minimap2
minimap2 -X -N 50 -p 0.1 -c emales.fna emales.fna > emales.paf
```
``` {r eval=FALSE}
emale_links <- read_paf("emales.paf")
p4 <- gggenomes(emale_seqs_6, emale_genes, emale_tirs, emale_links) +
geom_seq() + geom_bin_label() +
geom_feature(size=5, data=use_features(features)) +
geom_gene(aes(fill=gc_cont)) +
geom_link() +
scale_fill_distiller(palette="Spectral")
p4 <- p4 %>% flip_bins(3:5)
p4
```
### GC-content
``` {bash eval=FALSE}
# https://github.com/thackl/seq-scripts (bedtools & samtools)
seq-gc -Nbw 50 emales.fna > emales-gc.tsv
```
``` {r eval=FALSE}
emale_gc <- thacklr::read_bed("emales-gc.tsv") %>%
dplyr::rename(seq_id=contig_id)
p5 <- p4 %>% add_features(emale_gc)
p5 <- p5 + geom_ribbon(aes(x=(x+xend)/2, ymax=y+.24, ymin=y+.38-(.4*score),
group=seq_id, linetype="GC-content"), use_features(emale_gc),
fill="blue", alpha=.5)
p5
```
### cluster protein sequences into orthogroups
``` {bash eval=FALSE}
gff2cds --aa --type CDS --source Prodigal_v2.6.3 --fna emales.fna emales.gff > emales.faa
mmseqs easy-cluster emales.faa emales-mmseqs /tmp -e 1e-5 -c 0.7;
cluster-ids -t "cog%03d" < emales-mmseqs_cluster.tsv > emales-cogs.tsv
```
``` {r eval=FALSE}
emale_cogs <- read_tsv("emales-cogs.tsv", col_names = c("feature_id", "cluster_id", "cluster_n"))
emale_cogs %<>% dplyr::mutate(
cluster_label = paste0(cluster_id, " (", cluster_n, ")"),
cluster_label = fct_lump_min(cluster_label, 5, other_level = "rare"),
cluster_label = fct_lump_min(cluster_label, 15, other_level = "medium"),
cluster_label = fct_relevel(cluster_label, "rare", after=Inf))
emale_cogs
p6 <- gggenomes(emale_seqs_6, emale_genes, emale_tirs, emale_links) %>%
add_features(emale_gc) %>%
add_clusters(genes, emale_cogs) %>%
flip_bins(3:5) +
geom_seq() + geom_bin_label() +
geom_feature(size=5, data=use_features(features)) +
geom_gene(aes(fill=cluster_label)) +
geom_link() +
geom_ribbon(aes(x=(x+xend)/2, ymax=y+.24, ymin=y+.38-(.4*score),
group=seq_id, linetype="GC-content"), use_features(emale_gc),
fill="blue", alpha=.5) +
scale_fill_brewer("Conserved genes", palette="Set3")
p6
```
### Blast hits and integrated transposons
``` {bash eval=FALSE}
# mavirus.faa - published
blastp -query emales.faa -subject mavirus.faa -outfmt 7 > emales_mavirus-blastp.tsv
perl -ne 'if(/>(\S+) gene=(\S+) product=(.+)/){print join("\t", $1, $2, $3), "\n"}' \
mavirus.faa > mavirus.tsv
```
``` {r eval=FALSE}
emale_blast <- read_blast("emales_mavirus-blastp.tsv")
emale_blast %<>%
dplyr::filter(evalue < 1e-3) %>%
dplyr::select(feature_id=qaccver, start=qstart, end=qend, saccver) %>%
dplyr::left_join(read_tsv("mavirus.tsv", col_names = c("saccver", "blast_hit", "blast_desc")))
# manual annotations by MFG
emale_transposons <- read_gff("emales-manual.gff", types = c("mobile_element"))
p7 <- gggenomes(emale_seqs_6, emale_genes, emale_tirs, emale_links) %>%
add_features(emale_gc) %>%
add_clusters(genes, emale_cogs) %>%
add_features(emale_transposons) %>%
add_subfeatures(genes, emale_blast, transform="aa2nuc") %>%
flip_bins(3:5) +
geom_feature(aes(color="integrated transposon"),
use_features(emale_transposons), size=7) +
geom_seq() + geom_bin_label() +
geom_link(offset = c(0.3, 0.2), color="white", alpha=.3) +
geom_feature(aes(color="terminal inverted repeat"), use_features(features),
size=4) +
geom_gene(aes(fill=cluster_label)) +
geom_feature(aes(color=blast_desc), use_features(emale_blast), size=2,
position="pile") +
geom_ribbon(aes(x=(x+xend)/2, ymax=y+.24, ymin=y+.38-(.4*score),
group=seq_id, linetype="GC-content"), use_features(emale_gc),
fill="blue", alpha=.5) +
scale_fill_brewer("Conserved genes", palette="Set3") +
scale_color_viridis_d("Blast hits & Features", direction = -1) +
scale_linetype("Graphs") +
ggtitle(expression(paste("Endogenous mavirus-like elements of ",
italic("C. burkhardae"))))
p7
```
