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Introduction

eresearchqut/VirReport is a bioinformatics pipeline based upon the scientific workflow manager Nextflow. It was designed to help phytosanitary diagnostics of viruses and viroid pathogens in quarantine facilities. It takes small RNA-Seq fastq files as input. These can either be in raw format (currently only samples specifically prepared with the QIAGEN QIAseq miRNA library kit can be processed this way) or quality-filtered.

The pipeline can either perform blast homology searches against a virus database or/and a local copy of NCBI nr and nt databases.

Nextflow is a workflow management software which enables the writing of scalable and reproducible scientific workflows. It can integrate various software package and environment management systems such as Docker, Singularity, and Conda. It allows for existing pipelines written in common scripting languages, such as Python and R, to be seamlessly coupled together. It implements a Domain Specific Language (DSL) that simplifies the implementation and running of workflows on cloud or high-performance computing (HPC) infrastructures. For a good introduction to Nextflow please refer to the following training materials:

https://www.nextflow.io/docs/latest/getstarted.html

https://training.seqera.io/

https://carpentries-incubator.github.io/workflows-nextflow/

Pipeline summary

The VirReport workflow will perform the following steps by default:

  • Retain reads of a given length (21-22 nt long by default) from fastq file(s) provided in index.csv file (READPROCESSING)

  • De novo assembly using both Velvet and SPAdes. The contigs obtained are collapsed into scaffolds using cap3. By default, only contigs > 30 bp will be retained (DENOVO_ASSEMBLY)

  • Run megablast homology search against either a local virus database or NCBI NT/NR databases:

  • Searches against a local virus database:

    • Run megablast homology searches on de novo assembly against local virus and viroid database. Homology searches against blastn are also run in parallel for comparison with the megablast algorithm (BLAST_NT_VIRAL_DB_CAP3)

    • Retain top megablast hit and restrict results to virus and viroid matches. Summarise results by grouping all the de novo contigs matching to the same viral hit and deriving the cumulative blast coverage and percent ID for each viral hit (FILTER_BLAST_NT_VIRAL_DB_CAP3)

    • Align reads to top hit, derive coverage statistics, consensus sequence and VCF matching to top blast hit (FILTER_BLAST_NT_VIRAL_DB_CAP3, COVSTATS_VIRAL_DB)

    • Run tblastn homolgy search on predicted ORF >= 90 bp derived using getORF (TBLASTN_VIRAL_DB)

The pipeline can perform additional optional steps, which include:

  • Searches against local NCBI NT and NR databases:

    • Retain top 5 megablast hits and restrict results to virus and viroid matches. Summarise results by grouping all the de novo contigs matching to the same viral hit and deriving the cumulative blast coverage and percent ID for each viral hit (BLATN_NT_CAP3)

    • Align reads to top hit, derive coverage statistics, consensus sequence and VCF matching to top blast hits (COVSTATS_NT)

    • Run blastx homolgy search on contigs >= 90 bp long for which no match was obtained in the megablast search. Summarise the blastx results and restrict to virus and viroid matches (BLASTX)

  • A quality filtering step on raw fastq files (currently the workflow only processes samples prepared using QIAGEN QIAseq miRNA library kit). After performing quality filtering (FASTQC_RAW, ADAPTER_AND_QUAL_TRIMMING, QC_POST_QUAL_TRIMMING, DERIVE_USABLE_READS). the pipeline will also derive a qc report (QCREPORT). An RNA souce profile can be included as part of this step (RNA_SOURCE_PROFILE, RNA_SOURCE_PROFILE_REPORT)

  • VirusDetect version 1.8 can also be run in parallel. A summary of the top virus/viroid blastn hits will be separately output (VIRUS_DETECT, VIRUS_IDENTIFY, VIRUS_DETECT_BLASTN_SUMMARY, VIRUS_DETECT_BLASTN_SUMMARY_FILTERED)

Pipeline prerequisites

Installing Java and Nextflow

You can follow the steps outlined in the Nexflow documentation to install Java and Nextflow on your local machine or server:https://www.nextflow.io/docs/latest/getstarted.html

This link specifically describes the steps to take to load Java and install Nextflow on our local HPC at QUT (Lyra): Nextflow

Installing a suitable environment management system

To run the VirReport pipeline, you will need to install a suitable environment management system such as Docker, Singularity or Conda to suit your environment.

We use conda or miniconda on our HPC.

Installing VirReport

The open-source VirReport code is available at https://github.com/eresearchqut/VirReport

Run the following command to get a copy of the source code:

git clone https://github.com/eresearchqut/VirReport.git

Running the pipeline

You can either invoke the pipeline by pointing to the location of main.nf in the version of VirReport you cloned, for example:

nextflow run ~/code/github/clean/VirReport/main.nf

or run directly eresearchqut/VirReport

nextflow run eresearchqut/VirReport

You will have to specify a profile to use to run the pipeline

nextflow run eresearchqut/VirReport -profile {docker, singularity or conda}

On our HPC you can either specify singularity or conda as profile.

Depending on the one selected, cached environment will be built in your home directory under either the cached singularity or conda directory. This step will take some time the first time you run the pipeline.

Testing the pipeline on minimal test dataset:

Running these test datasets requires 2 cpus and 8 Gb mem and should take less than 5 mins to complete.

Make sure you have your nextflow config file set to “local" mode to run these tests:

process {
  executor = 'local'
  beforeScript = {
    """
    source $HOME/.bashrc
    source $HOME/.profile
    """
    }

This first command will test your installation using a single quality filtered fastq file (called test.fastq.gz) derived from a sample infected with citrus exocortis viroid and will run VirReport using a mock ncbi database:

nextflow -c conf/test.config run eresearchqut/VirReport -profile test,{docker, singularity or conda}

This second command will test your installation using a pair of raw fastq files (called test_pair_1.fastq.gz and test_pair_2.fastq.gz) derived from a sample infected with citrus tristeza virus and will run VirReport using a mock viral database:

nextflow -c conf/test2.config run eresearchqut/VirReport -profile test2,{docker, singularity or conda}

If both of these tests finish successfully, this means that the pipeline was set up properly.

You can have a look at the files that have been created under the results folder.

You are now all set to analyse your own samples.

Running the pipeline with your own data

Provide an index.csv file

Create a TAB delimited text file that will be the input for the workflow to run. By default the pipeline will look for a file called “index.csv” in the base directory but you can specify any file name using the --indexfile [filename] in the nextflow run command. This text file requires the following columns (which needs to be included as a header): sampleid,samplepath

sampleid will be the sample name that will be given to the files created by the pipeline
samplepath is the full path to the fastq files that the pipeline requires as starting input

An index_example.csv is included in the base directory:

sampleid,samplepath
MT212,/work/diagnostics/2021/MT212_21-22bp.fastq
MT213,/work/diagnostics/2021/MT213_21-22bp.fastq

If you need to set additional parameters, you can either include these in your nextflow run command:

nextflow run eresearchqut/VirReport -profile {singularity, docker or conda} --indexfile index_example.csv --contamination_detection

or set them to true in the nextflow.config file.

params {
  contamination_detection = true
}

Provide a database

  • By default, the pipeline is set to run homology blast searches against a local plant virus/viroid database (this is set in the nextflow.config file with parameter --virreport_viral_db = true. You will need to provide this database to run the pipeline. You can either provide your own or use a curated database provided at https://github.com/maelyg/PVirDB.git . Ensure you use NCBI BLAST+ makeblastdb to create the database. For instance, to set up this database, you would take the following steps:

    git clone https://github.com/maelyg/PVirDB.git
cd PVirDB
gunzip PVirDB_v1.fasta.gz
makeblastdb -in PVirDB_v1.fasta -parse_seqids -dbtype nucl

    Then specify the full path to the database files including the prefix in the nextflow.config file. For example:

    params {
  blast_local_db_path = '/path_to_viral_DB/viral_DB_name'
}

  • If you also want to run homology searches against public NCBI databases, you need to set the parameter virreport_ncbi in the nextflow.config file to true:

    params {
  virreport_ncbi = true
}

    or add it in your nextflow command:

    nextflow run eresearchqut/VirReport -profile {docker, singularity or conda} --virreport_ncbi

    Download these locally, following the detailed steps available at https://www.ncbi.nlm.nih.gov/books/NBK569850/ . Create a folder where you will store your NCBI databases. It is good practice to include the date of download. For instance:

    mkdir blastDB/30112021

    You will need to use the update_blastdb.pl script from the blast+ version used with the pipeline.
    For example:

    perl update_blastdb.pl --decompress nt [*]
perl update_blastdb.pl --decompress nr [*]
perl update_blastdb.pl taxdb
tar -xzf taxdb.tar.gz

    Make sure the taxdb.btd and the taxdb.bti files are present in the same directory as your blast databases.
    Specify the path of your local NCBI blast nt and nr directories in the nextflow.config file.
    For instance:

    params {
  blast_db_dir = '/work/hia_mt18005_db/blastDB/20220408'
}
Run nextflow

You can either provide raw or pre-filtered fastq files to the pipeline. If you want to provide raw fastq files, samples have to be specifically prepared with the QIAGEN QIAseq miRNA library kit.

  • If you want to run the initial quality filtering step on your raw fastq files, you will need to set the --qualityfilter paramater to true in the config.file and specify the path to the directory which holds the required bowtie indices (using the --bowtie_db_dir parameter) to: 1) filter non-informative reads (using the blacklist bowtie indices for the DERIVE_USABLE_READS process) and 2) optionally derive the origin of the filtered reads obtained (RNA_SOURCE_PROFILE process).

    The required fasta files are available at https://github.com/maelyg/bowtie_indices.git and bowtie indices can be built from these using the command:

    git clone https://github.com/maelyg/bowtie_indices.git
gunzip blacklist_v2.fasta.gz
#you might need to activate your environment cached in either your conda or singularity environment in order to run bowtie
#for example
conda activate /path_to_cached_environment/virreport-77d02f3abe1d8ba5f8dfdff194142de9
#then run the bowtie command
bowtie-build -f blacklist_v2.fasta blasklist

    The location of the directory in which the bowtie indices are located will need to be specified in the nextflow.config file:

    params {
  bowtie_db_dir = '/path_to_bowtie_idx_directory'
}

    If you are interested to derive an RNA source profile of your fastq files you will need to specify:

    params {
  rna_source_profile = true
}

    And build the other indices from the fasta files included in https://github.com/maelyg/bowtie_indices.git (i.e. rRNA, plant_tRNA, plant_noncoding, plant_pt_mt_other_genes, artefacts, plant_miRNA, virus).

    The quality filtering step will create the 00_quality_filtering folder under the results folder:

    results/
├── 00_quality_filtering
    └── sample_name
    │   ├── sample_name_18-25nt_cutadapt.log
    │   ├── sample_name_fastqc.html
    │   ├── sample_name_fastqc.zip
    │   ├── sample_name_21-22nt_cutadapt.log
    │   ├── sample_name_21-22nt.fastq.gz
    │   ├── sample_name_24nt_cutadapt.log
    │   ├── sample_name_blacklist_filter.log
    │   ├── sample_name_fastp.html
    │   ├── sample_name_fastp.json
    │   ├── sample_name_qual_filtering_cutadapt.log
    │   ├── sample_name_quality_trimmed_fastqc.html
    │   ├── sample_name_quality_trimmed_fastqc.zip
    │   ├── sample_name_quality_trimmed.fastq.gz
    │   ├── sample_name_read_length_dist.pdf
    │   ├── sample_name_read_length_dist.txt
    │   ├── sample_name_truseq_adapter_cutadapt.log
    │   └── sample_name_umi_tools.log
    └── qc_report
        ├── read_origin_counts.txt
        ├── read_origin_detailed_pc.txt
        ├── read_origin_pc_summary.txt
        ├── read_origin_pc_summary.txt
        ├── run_qc_report.txt
        └── run_read_size_distribution.pdf

  • If your sequencing run was split between several lanes, you might have several raw fastq files per sample, and you can directly feed these to the pipeline and specify the --merge-lane parameter. The fastq files will be collapsed to one fastq file before performing downstream analysis. The sample name used will be the sampleid provided in the index.csv file. In the example below 2 fastq files were generated for 1 sample named CT103: 

sampleid,samplepath
CT103,/path_to_fastq_files_directory/CT_103_S10_L001_R1_001.fastq.gz
CT103,/rpath_to_fastq_files_directory/CT_103_S10_L002_R1_001.fastq.gz

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