A Nextflow pipeline to describe and compare genomes across species. It also performs gene expansion and contraction analysis using CAFE.
It works with any set of species that have a genome (fasta) and annotation (gff) file. (minimum of 5 species ideally up to around 30).
You can also run GO annotation and analysis using eggnogmapper. You must provide a database yourself with --eggnog_data_dir, else everytime you run the pipeline, it will download the DB for you. So be careful, it is ~7GB. Please run it once, and save the DB somewhere handy to point to.
This is then used to check what GO terms are associated with expanded or contracted gene sets (from CAFE).
The general pipeline logic is as follows:
- Downloads genome and annotation files from NCBI
[NCBIGENOMEDOWNLOAD], or you provide your own. - Standardises and filters GFF annotations
[AGAT_CONVERTSPGXF2GXF][AGAT_SPKEEPLONGESTISOFORM]. - Extracts longest protein fasta sequences
[GFFREAD]. - Optionally describes genome assembly and annotation:
[BUSCO_BUSCO]: Completeness of the genome compared to expected gene set.[QUAST]: Assembly contiguity statistics (N50 etc).[AGAT_SPSTATISTICS]: Gene, exon, and intron statistics.
- Finds orthologous genes across species
[ORTHOFINDER_CAFE]. - Rescales species tree branch lengths
[RESCALE_TREE]. - Runs gene family evolution analysis
[CAFE]and plots results[CAFE_PLOT]. - Optionally assigns GO terms to genes using
[EGGNOGMAPPER]. - Optionally plots GO enrichment for expanded/contracted gene families
[CAFE_GO]. - Optionally plots GO enrichment of genes by chromosome
[CHROMO_GO].
Nextflow pipelines require a few prerequisites. There is further documentation on the nf-core webpage here, about how to install Nextflow.
- Docker or Singularity.
- Java and openJDK >= 8 (Please Note: When installing Java versions are
1.VERSIONsoJava 8isJava 1.8). - Nextflow >=
v25.10.0. - When running nextflow with this pipeline, ideally run
NXF_VER=25.10.0beforehand, to ensure functionality on this version.
To install the pipeline please use the following commands but replace VERSION with a release.
wget https://github.com/Eco-Flow/excon/archive/refs/tags/VERSION.tar.gz -O - | tar -xvf -
or
curl -L https://github.com/Eco-Flow/excon/archive/refs/tags/VERSION.tar.gz --output - | tar -xvf -
This will produce a directory in the current directory called excon-VERSION which contains the pipeline.
--input /path/to/csv/file- A singular csv file as input in one of the two formats stated below.
This csv can take 2 forms:
- A 2 field csv where each row is a unique species name followed by a Refseq genome reference ID (NOT a Genbank reference ID) i.e.
data/input_small-s3.csv. The pipeline will download the relevant genome fasta file and annotation gff3 (or gff augustus) file. - A 3 field csv where each row is a unique species name, followed by an absolute path to a genome fasta file, followed by an absolute path to an annotation gff3 (or gff augustus) file. Input can be gzipped (.gz) or not.
Please Note: The genome has to be chromosome level not contig level.
2 fields (Name,Refseq_ID):
Drosophila_yakuba,GCF_016746365.2
Drosophila_simulans,GCF_016746395.2
Drosophila_santomea,GCF_016746245.2
3 fields (Name,genome.fna,annotation.gff):
Drosophila_yakuba,data/Drosophila_yakuba/genome.fna.gz,data/Drosophila_yakuba/genomic.gff.gz
Drosophila_simulans,data/Drosophila_simulans/genome.fna.gz,data/Drosophila_simulans/genomic.gff.gz
Drosophila_santomea,data/Drosophila_santomea/genome.fna.gz,data/Drosophila_santomea/genomic.gff.gz
Note: Genomes should be chromosome-level, not contig-level. RefSeq IDs must be used (not GenBank IDs).
| Parameter | Description | Default |
|---|---|---|
--input |
Path to input CSV file | Required |
--outdir |
Output directory | results |
--groups |
NCBI taxonomy group for genome download (e.g. insects, bacteria) |
insects |
--help |
Display help message | false |
--custom_config |
Path to a custom Nextflow config file | null |
| Parameter | Description | Default |
|---|---|---|
--stats |
Run BUSCO, QUAST and AGAT statistics on genomes | null |
--busco_lineage |
BUSCO lineage database (e.g. insecta_odb10) |
null |
--busco_mode |
BUSCO mode (genome, proteins, transcriptome) |
null |
--busco_lineages_path |
Path to local BUSCO lineage databases | null |
--busco_config |
Path to BUSCO config file | null |
| Parameter | Description | Default |
|---|---|---|
--skip_cafe |
Skip CAFE analysis | null |
--cafe_max_differential |
Maximum gene count differential for CAFE filtering on retry | 50 |
--tree_scale_factor |
Scale factor for rescaling species tree branch lengths | 1000 |
| Parameter | Description | Default |
|---|---|---|
--run_eggnog |
Run EggNOG-mapper GO annotation | false |
--eggnog_data_dir |
Path to pre-downloaded EggNOG database directory | null |
Note: The EggNOG database is ~7GB. If
--eggnog_data_diris not provided, the database will be downloaded automatically on each run. We strongly recommend downloading it once and reusing it:mkdir eggnog_data wget http://eggnog5.embl.de/download/emapperdb-5.0.2/eggnog.db.gz -O eggnog_data/eggnog.db.gz wget http://eggnog5.embl.de/download/emapperdb-5.0.2/eggnog_proteins.dmnd.gz -O eggnog_data/eggnog_proteins.dmnd.gz wget http://eggnog5.embl.de/download/emapperdb-5.0.2/eggnog.taxa.tar.gz -O eggnog_data/eggnog.taxa.tar.gz gunzip eggnog_data/eggnog.db.gz gunzip eggnog_data/eggnog_proteins.dmnd.gz tar -xzf eggnog_data/eggnog.taxa.tar.gz -C eggnog_data/ && rm eggnog_data/eggnog.taxa.tar.gzThen pass
--eggnog_data_dir /path/to/eggnog_datato the pipeline.
| Parameter | Description | Default |
|---|---|---|
--chromo_go |
Run GO enrichment analysis by chromosome | null |
--go_cutoff |
P-value cutoff for GO enrichment | 0.05 |
--go_type |
GO test type (e.g. none) |
none |
--go_max_plot |
Maximum number of GO terms to plot | 10 |
| Parameter | Description | Default |
|---|---|---|
--max_memory |
Maximum memory per job | 128.GB |
--max_cpus |
Maximum CPUs per job | 16 |
--max_time |
Maximum runtime per job | 240.h |
| Profile | Description |
|---|---|
docker |
Run with Docker containers |
singularity |
Run with Singularity containers |
conda |
Run with Conda environments |
test_bacteria |
Test run with small bacterial genomes |
test_small |
Test run with small insect genomes |
This pipeline is designed to run in various modes that can be supplied as a comma separated list i.e. -profile profile1,profile2.
Please select one of the following profiles when running the pipeline.
docker- This profile uses the container software Docker when running the pipeline. This container software requires root permissions so is used when running on cloud infrastructure or your local machine (depending on permissions). Please Note: You must have Docker installed to use this profile.singularity- This profile uses the container software Singularity when running the pipeline. This container software does not require root permissions so is used when running on on-premise HPCs or you local machine (depending on permissions). Please Note: You must have Singularity installed to use this profile.apptainer- This profile uses the container software Apptainer when running the pipeline. This container software does not require root permissions so is used when running on on-premise HPCs or you local machine (depending on permissions). Please Note: You must have Apptainer installed to use this profile.
local- This profile is used if you are running the pipeline on your local machine.aws_batch- This profile is used if you are running the pipeline on AWS utilising the AWS Batch functionality. Please Note: You must use theDockerprofile with with AWS Batch.test_small- This profile is used if you want to test running the pipeline on your infrastructure, running from predownloaded go files. Please Note: You do not provide any input parameters if this profile is selected but you still provide a container profile.test_biomart- This profile is used if you want to test running the pipeline on your infrastructure, running from the biomart input. Please Note: You do not provide any input parameters if this profile is selected but you still provide a container profile.
If you want to run this pipeline on your institute's on-premise HPC or specific cloud infrastructure then please contact us and we will help you build and test a custom config file. This config file will be published to our configs repository.
Please note: The -resume flag uses previously cached successful runs of the pipeline.
- Example run the full test example data:
NXF_VER=24.10.1
nextflow run main.nf -resume -profile docker,test_small
Settings in test_small: input = "input_small-s3.csv" predownloaded_fasta = "s3://excon/data/Insect_data/fasta/" predownloaded_gofiles = "s3://excon/data/Insect_data/gofiles/"
For the fastest run use: nextflow run main.nf -resume -profile docker,test_bacteria
- To run on your own data (minimal run), cafe only.
# NXF_VER=25.04.8 - Is latest it is tested on
nextflow run main.nf -resume -profile docker --input data/input_small-s3.csv
- To run on your own data with GO enrichment analysis (using predownloaded fasta/go files for GO assignment)
# NXF_VER=25.04.8 - Is latest it is tested on
nextflow run main.nf -resume -profile docker --input data/input_small-s3.csv \|
--predownloaded_fasta 's3://excon/data/Insect_data/fasta/*' --predownloaded_gofiles 's3://excon/data/Insect_data/gofiles/*'
- To run on your own data with GO enrichment analysis + retrieval of GO assignment species
If you do not have GO files to run GO enrichment, you can run the following code to semi-auto download them from NCBI biomart.
You first need to go to Ensembl Biomart to find the species IDs you want to use to assign GO terms to your species. Ideally you should choose one or more species that are closely related and have good GO annotations.
i) To check what species are present and their species name codes you need to download the biomaRt library in R (for metazoa):
library(biomaRt)
ensembl <- useEnsembl(biomart = "metazoa_mart", host="https://metazoa.ensembl.org")
datasets <- listDatasets(ensembl)
datasets
You will see something like:
dataset
1 aagca019059575v1_eg_gene
2 aagca914969975_eg_gene
3 aagca933228735v1_eg_gene
4 aalbimanus_eg_gene
5 aalbopictus_eg_gene
6 aalvpagwg_eg_gene
The dataset IDs are what you need to enter into the Nextflow script.
For mammals:
ensembl <- useEnsembl(biomart = "genes", host="https://ensembl.org")
Then you can run the excon script as follows:
# NXF_VER=25.04.8 - Is latest it is tested on
nextflow run main.nf -resume -profile <apptainer/docker/singularity> --input data/input_small-s3.csv --ensembl_biomart "metazoa_mart" --ensembl_dataset "example.txt"
where example.txt is a file of dataset IDs from ensembl biomart (as shown above), separated by newline characters.
e.g.:
aagca019059575v1_eg_gene
aagca914969975_eg_gene
aagca933228735v1_eg_gene
This pipeline is not yet published. Please contact us if you wish to use our pipeline, we are happy to help you run the pipeline.
If you need any support do not hesitate to contact us at any of:
c.wyatt [at] ucl.ac.uk
ecoflow.ucl [at] gmail.com