Gene-gene interaction screening pipeline

scbirlab/nf-ggi is a Nextflow pipeline to screen gene-gene interactions from a specified organism (or set of organisms).

Table of contents

• Processing steps
• Requirements
• Quick start
• Inputs
• Outputs
• Credit
• Issues, problems, suggestions
• Further help

Processing steps

1. Download Rhea DB in preparation for searching.

For each organism ID provided:

1. Download its STRING database and tidy data.
2. Download FASTA sequences of its proteins from UniProt
   • Where possible, reference proteomes are used.
3. Find reactions in Rhea DB and connect products with reactants between enzymes in the proteome.

For each FASTA sequence in each organism:

4. Generate a multiple sequence alignment with hhblits.

Then within each organism:

5. Generate all unique pairs of proteins.

Then for each protein pair:

6. Calculate the co-evolutionary signal with DCA.
7. Predict the interface contact map with yunta rf2t (RosettaFold-2track).
8. Predict the protein-protein complex structure map with yunta af2 (AlphaFold2).

Requirements

Databases

To generate multiple-sequence alignments (MSAs) for co-evolutionary analysis, hhblits databases of pre-clustered sequences is required. Unfortunately, these are extremely large, so cannot be downlaoded as part of the pipeline. You should download the UniClust and BFD databases, then set the --uniclust and --bfd parameters of the pipeline (see below).

If you're at the Crick, these databases already reside on NEMO.

Software

You need to have Nextflow and conda installed on your system.

First time using Nextflow?

If you're at the Crick or your shared cluster has it already installed, try:

module load Nextflow

Otherwise, if it's your first time using Nextflow on your system, you can install it using conda:

conda install -c bioconda nextflow 

You may need to set the NXF_HOME environment variable. For example,

mkdir -p ~/.nextflow
export NXF_HOME=~/.nextflow

To make this a permanent change, you can do something like the following:

mkdir -p ~/.nextflow
echo "export NXF_HOME=~/.nextflow" >> ~/.bash_profile
source ~/.bash_profile

Quick start

Make a sample sheet (see below) and, optionally, a nextflow.config file in the directory where you want the pipeline to run. Then run Nextflow.

nextflow run scbirlab/nf-ont-call-variants

Each time you run the pipeline after the first time, Nextflow will use a locally-cached version which will not be automatically updated. If you want to ensure that you're using the very latest version of the pipeline, use the -latest flag.

nextflow run scbirlab/nf-ont-call-variants -latest

If you want to run a particular tagged version of the pipeline, such as v0.0.1, you can do so using

nextflow run scbirlab/nf-ont-call-variants -r v0.0.2

For help, use nextflow run scbirlab/nf-ont-call-variants --help.

The first time you run the pipeline on your system, the software dependencies in environment.yml will be installed. This may take several minutes.

Inputs

The following parameters are required:

• sample_sheet: path to a CSV with information about the samples and FASTQ files to be processed
• uniclust: Path to hhblits UniClust database. This is very large, so you need to have it already downlaoded on your system.
• bfd: Path to hhblits BFD database. This is very large, so you need to have it already downlaoded on your system.

The following parameters have default values which can be overridden if necessary.

• rhea_url = "https://ftp.expasy.org/databases/rhea": URL to download Rhea reaction database
• outputs = "outputs": Output folder
• batch_size = 100: How many protein-protein interactions to group into one job at a time.
• test = false: Whether to run in test mode. If so, only 3 proteins per organism will be analyzed.
• non_self = false: Whether to run in non-self mode. This is where a whole proteome is run against a single bait protein (rather than all pairwise from the proteome).

The parameters can be provided either in the nextflow.config file or on the nextflow run command.

Here is an example of the nextflow.config file:

params {
    sample_sheet = "/path/to/sample-sheet.csv"
}

Alternatively, you can provide the parameters on the command line:

nextflow run scbirlab/nf-ggi --sample_sheet /path/to/sample-sheet.csv 

Sample sheet

The sample sheet is a CSV file indicating which organisms you want to analyze.

The file must have a header with the column names below, and one line per organism to be processed.

• organism_id: the NCBI Taxonomic ID for your organism. This can be found at NCBI Taxonomy
• proteome_name: This can be anything, but should be an informative description

Here is an example of the sample sheet:

organism_id	proteome_name
243273	"Mycoplasma genitalium"

If running with --non-self, to do a pulldown against a single bait protein, add another column with the bait UniProt ID.

organism_id	proteome_name	bait
243273	"Mycoplasma genitalium"	P47259

Outputs

Outputs are saved in the same directory as sample_sheet. They are organised under three directories:

• coexpression: STRING co-expression values
• metabolites: Reconstructed metabolic network
• msa: All MSA files
• ppi: All protein-protein interaction data
• sequences: Each organism's proteome sequence

Credit

The idea of using DCA, RoseTTAFold-2track, and AlphaFold2 in a cascade of increasingly expensive and specific PPI detection methods has been explored in a series of papers from David Baker's lab:

• Cong et al., Protein interaction networks revealed by proteome coevolution. Science, 2019
• Humpreys et al., Computed structures of core eukaryotic protein complexes. Science, 2021
• Humpreys et al., Protein interactions in human pathogens revealed through deep learning. Nature Microbiology, 2024

scbirlab/nf-ggi applies these algorithms in a Nextflow pipeline to allow easy scaling. It also reconstructs metabolic networks, and pulls known interactions from the STRING database.

Issues, problems, suggestions

Add to the issue tracker.

Further help

Here are the pages of the software and databases used by this pipeline.

Databases:

• STRING for co-expression
• Rhea for enzyme reactions
• UniProt for protein sequences
• NCBI Genbank for taxonomy

Software:

• hhblits for generating MSAs
• rdkit for cheminformatics of enzyme reactants and products
• yunta for running DCA, RosettaFold-2track, and AlphaFold2 on MSAs