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nf-kallistoViral

This workflow is a wrapper around the method described by Luebbert et al., 20241 in which Kallisto is used to quantify reads of viral origin in RNA-seq data (bulk + single-cell).
This wrapper is currently bulk only.

Requirements

This workflow uses a containerised version of kallisto | bustools v0.28.22 running kallisto v0.50.13 and bustools v0.43.22.

Set up

This workflow requires several reference files providing information on both viral and 'host' species to be downloaded prior to running. Here, 'host' species refers to the species of origin of the sequenced samples.

Firstly, the reference genome and transcriptome (cDNA) of the host species is required in FASTA format, along with a GTF annotation.
An example for the GENCODE v46 release for Humans is shown below.

# Genome
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/GRCh38.primary_assembly.genome.fa.gz
# cDNA
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/gencode.v46.transcripts.fa.gz
# GTF
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_46/gencode.v46.annotation.gtf.gz

Also required are viral reference files (modified PalmDB files) which can be found here and downloaded as shown below

# fasta
wget https://raw.githubusercontent.com/pachterlab/LSCHWCP_2023/main/PalmDB/palmdb_rdrp_seqs.fa
# T2G
wget https://raw.githubusercontent.com/pachterlab/LSCHWCP_2023/main/PalmDB/palmdb_clustered_t2g.txt

Instructions

This workflow is written in Nextflow - see here for installation info.
Alternatively, Nextflow can be installed into a conda/mamba environments (my preferred method).

micromamba create -n nextflow nextflow

Nextflow configurations are important for optimal functioning - more info here.
An example config file for use with Newcastle University's Rocket HPC is provided - rocket.config

Workflow parameters

This workflow requires essential parameters described in the table below.

  • ${projectDir} describes the directory where kallistoViral.nf is located
  • FASTQ files should be concatenated across lanes if necessary
    • In the example above, the FASTQ files are in the format:
      • SampleA_R1.fastq.gz, SampleA_R2.fastq.gz,
      • SampleB_R1.fastq.gz, SampleB_R2.fastq.gz
Parameter Description Example
fastq Path to FASTQ files "${projectDir}/data/fastq/*_{R1,R2}.fastq.gz"
outdir Name of output folder to be created in the current directory output
strandedness Whether RNA-seq library preparation retains strand information <forward / reverse / unstranded> reverse
readType Whether sequencing generated single- or paired-end reads <single / paired> paired
viral_fasta Path to viral FASTA file "${projectDir}/data/ref/palmdb_rdrp_seqs.fa"
viral_t2g Path to viral T2G file "${projectDir}/data/ref/palmdb_clustered_t2g.txt"
cdna Path to host transcriptome reference. "${projectDir}/data/ref/gencode.v46.transcripts.fa.gz"
genome Path to host genomic reference "${projectDir}/data/ref/GRCh38.primary_assembly.genome.fa.gz"
gtf Path to GTF annotation. "${projectDir}/data/ref/gencode.v46.annotation.gtf.gz"

The above parameters should be contained with a parameters file. An example is given in params.json for the file structure shown below.

.
├── data
│   ├── fastq
│   │   ├── SampleA_R1.fastq.gz
│   │   ├── SampleA_R2.fastq.gz
│   │   ├── SampleB_R1.fastq.gz
│   │   └── SampleB_R2.fastq.gz
│   └── ref
│       ├── gencode.v46.annotation.gtf.gz
│       ├── gencode.v46.transcripts.fa.gz
│       ├── GRCh38.primary_assembly.genome.fa.gz
│       ├── palmdb_clustered_t2g.txt
│       └── palmdb_rdrp_seqs.fa
├── kallistoViral.nf
├── launcher_slurm.sh
├── params.json
├── README.md
└── rocket.config

Run workflow

The basic command for running the workflow is

micromamba activate nextflow
nextflow run kallistoViral.nf -config <config_file> -params-file <params_file>

However, it is advised to submit the nextflow job as a batch job to save resources on the login/head node. An example of this for use with the SLURM workload manager is given in launcher_slurm.sh which can be run using

sbatch launcher_slurm.sh

Output

When finished successfully, the specified output folder should contain host (transcript + gene) and viral abundances for each sample.
Information regarding the run + quantification is contained within JSON files.

output
├── host
│   ├── SampleA
│   │   ├── inspect.json
│   │   ├── kb_info.json
│   │   ├── quant_unfiltered
│   │   │   ├── abundance_1.tsv
│   │   │   └── abundance.gene_1.tsv
│   │   └── run_info.json
│   └── SampleB
│       ├── inspect.json
│       ├── kb_info.json
│       ├── quant_unfiltered
│       │   ├── abundance_1.tsv
│       │   └── abundance.gene_1.tsv
│       └── run_info.json
└── viral
    ├── SampleA
    │   ├── inspect.json
    │   ├── kb_info.json
    │   ├── quant_unfiltered
    │   │   ├── abundance_1.tsv
    │   │   ├── abundance_2.tsv
    │   │   ├── abundance.gene_1.tsv
    │   │   └── abundance.gene_2.tsv
    │   └── run_info.json
    └── SampleB
        ├── inspect.json
        ├── kb_info.json
        ├── quant_unfiltered
        │   ├── abundance_1.tsv
        │   ├── abundance_2.tsv
        │   ├── abundance.gene_1.tsv
        │   └── abundance.gene_2.tsv
        └── run_info.json

Paired-end sequencing produces two abundance files when quantifying viral reads as the use of amino acid sequences in pseudoalignment (kb count -aa) is not currently supported in paired-end reads.

Mapping the viral IDs to their respective taxonomies can be performed using this file for downstream analysis.

To do

  • Parse JSON log outputs
  • Automatically map viral IDs to taxonomies
  • Produce formatted count matrices
  • Develop single cell workflow wrapper

References

1 Luebbert, L., Sullivan, D.K., Carilli, M., Hjörleifsson, K.E., Winnett, A.V., Chari, T. & Pachter, L. (2024) ‘Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression’, bioRxiv: The Preprint Server for Biology, p. 2023.12.11.571168.

2 Melsted, P., Booeshaghi, A.S., Liu, L., Gao, F., Lu, L., Min, K.H. (Joseph), da Veiga Beltrame, E., Hjörleifsson, K.E., Gehring, J. & Pachter, L. (2021) ‘Modular, efficient and constant-memory single-cell RNA-seq preprocessing’, Nature Biotechnology, 39(7), pp. 813–818.

3 Bray, N.L., Pimentel, H., Melsted, P. & Pachter, L. (2016) ‘Near-optimal probabilistic RNA-seq quantification’, Nature Biotechnology, 34(5), pp. 525–527.