HEImiRA Pipeline

Host environment intersection miRNA annotation pipeline

Introduction

HEImiRA (Host Environment Intersection miRNA Annotation) pipeline annotates microRNAs (miRNAs) from sRNA-Seq in relation to the taxonomy of a given host and target organism.

The pipeline was designed to be used for quantifying cross-kingdom miRNA transfer but could be directly applied to host-pathogen sample miRNA annotation, and possibly other areas.

HEImiRA pipeline is built with Nextflow using Singularity containers from the [Galaxy Project] and a custom Python container, to annotate miRNAs represented in sRNA-Seq data against the miRBase miRNA mature reference sequences.

Summary

Input

sRNA-Seq reads, FASTQ format, optionally gzipped.

Preprocessing

Preparing the sRNA-Seq read data for alignment.

• Pre and post quality checking - FASTQC + MultiQC
• Adaptor clipping - cutadapt
• Filtering - BBDUK

miRBase Tagging

Prepare the miRBase reference for alignment - prepare_reference.py (HEImiRA script).

• miRBase reference sequences collapsed
• Taxonomic groups ascribed to collapsed sequences
• Sequences tagged by target, host, environment
• Create collapsed-sequence reference FASTA heimeria_collapsed_reference.fa.gz
• Create collapsed-sequence + taxonomy table heimeria_metadata.csv.gz

NOTE: MiRNAs not found in either the target or host species are tagged as from the ‘environment’, while miRNAs that map to both the host and target species are tagged as ‘ambiguous’.

Alignment

Align preprocessed sRNA-Seq reads to the HEImiRA collapsed miRBase reference.

• Align the reads to the reference - STAR + Samtools

Analysis

Apply HEImiRA tags and combine taxonomic metadata with alignment counts - process_counts.py (HEImiRA script).

• Generate alignment counts
• Apply HEImiRA target, host, and environment tags
• Combine taxonomic metadata

Output

HEImiRA pipeline outputs are in CSV format compatible with Python Pandas for easy direct use or downstream analysis.

The three output tables contain the HEImiRA tags and taxonomic metadata for each reference sequence. The alignment counts are represented differently in each table, as follows.

• HEImiRA counts summary table
  • raw counts
• HEImiRA normalised counts summary table
  • counts normalised by total counts per sample
• HEImiRA host-normalised counts summary table
  • counts normalised by total host-tagged counts per sample

Quick Start

Set up and run the pipeline in four steps.

1. Prerequisites

Hardware

You will need at least 16GB of available memory to run the pipeline (required for indexing the miRBase reference).

Software

Install Nextflow (>=21.10.3)

Install Singualrity (see this tutorial )

2. Download HEImiRA Pipeline

Download and extract the latest release of the pipeline from GitHub.

e.g. on the Linux command line, you can use curl and tar.

curl -L https://github.com/PlantandFoodResearch/HEImiRA-pipeline/releases/download/v1.0/heimira-pipeline-v1.0.tar.gz | tar xzv

cd heimira-pipeline

3. Obtain BioPandas

BioPandas is a singularity container with Python-3.10, BioPython, PySam, and Pandas.

You have two options, the first is by far the quickest.

a. Use the container asset provided with the release

download size ~60MB

curl -L https://github.com/PlantandFoodResearch/HEImiRA-pipeline/releases/download/v1.0/heimira-biopandas-v1.0.tar.gz | tar xzv -C singularity

b. Build the container

WARNING: the build process takes ~40 minutes

The build script will build the biopandas.sif singularity image inside the ./singularity directory.

./singularity/build.sh

4. Run the pipeline

Run the pipeline, with a Nextflow report.

--input_files defines the Fastq files to use as input to the pipeline.

--host_organism and --target_organism can be a Genus species, e.g. 'homo sapiens', or a miRBase organism code, e.g. 'hsa'. They need to be present in miRBase.

These parameters (and many more) are all set in the nextflow.config file, and can be overridden at run-time (but make sure to 'single-quote' them), e.g.:

nextflow run -with-report heimira-run-report.html main.nf --input_files './my-fastq-data/*.fastq.gz' --host_organism 'homo sapiens' --target_organism 'Zea mays'

Results

Outputs are written to ./results by default (you can override the outdir parameter or change it in the nextflow.config).

Running on HPC (Slurm)

You can run the pipeline on a Slurm cluster with the preset slurm Nextflow profile. You can alter this profile in the nextflow.config too.

nextflow run -with-report heimira-big-run-report.html -profile slurm main.nf --input_files './my-big-fastq-data/*.fastq.gz'

Testing

You can run the pipeline with the test data as shown below. The ./test-data is tiny so the test should run quickly.

nextflow run -with-report heimira-test-report.html main.nf --outdir 'test-results' --input_files './test-data/*.fastq.gz' --host_organism 'homo sapiens' --target_organism 'Malus domestica'