phamb

A Phage from metagenomic bins (phamb) discovery approach used to isolate metagenome derived viromes and High-quality viral genomes.

The repository contains scripts and workflows used in our viral follow up study on the binning tool VAMB where we have benchmarked not only the quality and quantity of viral MAGs but also the viral overlap with metaviromes. In our analysis, CheckV has been important for assessing the actual gain of using viral MAGs relative to single-contig evaluation, a big kudos to Nayfach et al. for this great tool.

We have applied this approach to 3 different datasets and recovered up to 6,077 High-quality genomes from 1,024 viral populations, this is 200% more compared to only evaluation single-contigs. Similar to what we have observed for Bacterial bins, VAMB achieves high intra-VAMB-cluster ANI (>97.5%) also for viral bins, our best example here is accurate clustering of crAss-like bins found in the IBD Human Microbiome Project 2 dataset.

Our (recommended) workflow is to isolate the virome search space prior to running viral evaluation/prediction tools. For this, we have trained a Random Forest model on viral bins established using paired metagenomic and metavirome datasets. This massively helps in reducing computational time especially on larger datasets.
We strongly advise to only use Medium-quality and High-quality viral bins evaluated using the AAI-model in CheckV. We found the HMM-model is not currently well-suited for viral MAGs. Low-quality viral bins may likely represent fragmented/incomplete viruses or novel ones.
Bacterial MAGs and viral MAGs from the same metagenome can be efficiently associated using crispr-spacer approaches and sequence alignment (recommended cutoffs can be found in the article). From this, Host-viral abundance dynamics and bacterial pangenome modulation can be studied. Downstream viral proteome analysis should be based on the viral regions found in the contamination.tsv file produced by CheckV to prevent contaminating bacterial genes to influence the analysis.

We are working on seperate Snakemake workflows and scripts to make this approach more available.

MAG annotation for isolating Metagenomic derived viromes
Bacterial MAG and viral MAG association
MAG & VMAG Abundance

Prerequisites - Snakemake

In order to run most of it, you need conda installed in order to install snakemake. Most snakemake workflows comes with conda-environments, thus dependencies and programmes are automatcally installed.

conda install -n snakemake snakemake pygraphviz python=3.8 cython scikit-learn==0.21.3

1. MAG annotation for isolating Metagenomic derived viromes

Database and file requirements

VAMB bins and concatenated assemblies.

contigs.fna.gz #Concatenated assembly 
vamb/clusters.tsv   #Clustered contigs based on the above contigs.fna.gz file

Furthermore.

VOGdb - untar vog.hmm.tar.gz to get all hmm files. Concatenate these into an AllVOG.hmm file [File path needs to be specified in config.yaml]
Micomplete Bacterial HMMs [File path needs to be specified in config.yaml]
Clone DeepVirFinder git clone https://github.com/jessieren/DeepVirFinder

How to Run

Copy repository, extract the mag_annotation workflow and split contigs to allow annotation to be run in parallel.

mkdir -p projectdir 
cd projectdir 
git clone the repository https://github.com/RasmussenLab/phamb.git
cp -r phamb/workflows/mag_annotation .
python mag_annotation/scripts/split_contigs.py -c contigs.fna.gz

Now the contigs.fna.gz is splitted into individual assemblies i.e. assembly/{sample}/{sample}.fna In addition, a sample_table.txt file is created with a line for each sample. Check that sample_table.txt contains sample identifiers corresponding to the ones you expect. The number of lines should correspond to the number of samples used to make the concatenated assembly

Now, Specify paths for databases, vamb directory, location of assembly and computational resouces in mag_annotation/config.yaml

If everything good and set, you can run the snakemake pipeline.

# Local 
snakemake -s mag_annotation/Snakefile --use-conda -j 1

Dependent on the number of samples, it may be relevant to run the Snake-flow on a HPC server.

# HPC - this won't work unless you specify a legit group on your HPC in `config.yaml`
snakemake -s Snakefile --cluster qsub -j 32 --use-conda

Workflow content

The workflow does the following.

Splits the combined assembly into separate sample-specific ones
Predicts proteins de novo using Prodigal meta
Searches proteins with VOGdb (PVOG) and miComplete bacterial hallmarks db
Scans each contig using DeepVirFinder
Aggregates the above into Bin-wise annotation summaries used as input for Viral Decontamination Random Forrest model

The RF model automates filtering of VAMB bins that are most likely bacterial and therefore provides a space of plausible viral entities for further validation. The RF-model has been trained on paired Metaviromes and Metagenomes to make precisde decisions based on simple parameteres as the ones below. Compared to a single contig viral prediction model, the RF approach is extremely accurate. The increased performance is likely explained by the RF model evaluating on bin-level where one sequence with a low viral score does lead to a misprediction of the whole bin. Aggregated information (assuming the binning is really good!) from multiple-contigs simplifies prediction compared to single-contigs.

The RF model requires very few variables to make an accurate distinction.

binsize (bp)	nhallm	nVOGs	cluster_DVF_score
2.000.000	100	0.2	0.3
60.000	3	1.3	0.7

Outputs

Running this script, the virome bins are written to a fasta file and bin-annotations are summarised in vambbins_aggregated_annotation.txt

python mag_annotation/scripts/parse_annotation_minimal.py -v vamb \
-s sample_table.txt \
-a sample_annotation \
-o mag_viral_summaries \
-f contigs.fna.gz \
--decontaminate

sample_annotation/annotation_summaries/VAMB.Viral_RF_predictions.bins.fna.gz
sample_annotation/annotation_summaries/VAMB.Viral_RF_predictions.contigs.fna.gz

The VAMB.Viral_RF_predictions.bins.fna.gz file provides the concatenated-assemblies of VAMB bins while the VAMB.Viral_RF_predictions.contigs.fna.gz contains the individual contigs

Both files can be evaluated with dedicated Viral evaluation tools like CheckV to identify HQ assemblies.

i.e.

checkv end_to_end VAMB.Viral_RF_predictions.bins.fna.gz` checkv_vamb_bins

2. Bacterial MAG and viral MAG association [In Progress]

Using CRISPR-cas-typer (CCtyper) to extract CRISPR-arrays from MAGs generated using VAMB
Alignment of putative Viruses to MAGs
Summarise Viral-MAG connections

3. MAG VMAG Abundance [In Progress]

Code for making abundance matrices of MAGs and Viruses

Kenkeni-ZJU/phamb