FARmiR: Framework for Analysis and Refinement of miRNA profiles

FARmiR is a Snakemake-based pipeline for processing and analyzing miRNAs/isomiRs from small RNA sequencing data. It includes steps for quality control, adapter prediction and trimming, annotation and quantification with isoMiRmap, evaluation of miRNA candidates using non-coding RNA databases and expression thresholds, and refinement of miRNA profiles. The workflow supports high-performance computing environments with Slurm integration and provides structured outputs for downstream analysis and visuazliation with AIMEE.

Dependencies

• Python
• Mamba
• Snakemake
• Snakemake-Profiles
• Apptainer/Singularity

Initial setup

1. Install dependencies

If you do not have conda already installed, the Snakemake developers recommend to install via Mambaforge.

NOTE: In January of 2024, Snakamake underwent a major update to version 8 which introduced a number of breaking changes. Until these are addressed, we suggest using version 7 which can be specified using version number as below.

# download Mambaforge installer (assuming Unix-like platform)
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh"
bash Mambaforge-$(uname)-$(uname -m).sh

# updata Mamba
mamba update mamba -c conda-forge

# create a Snakemake environment which includes all Snakemake dependencies in
# addition to the miscellaneous modules above
mamba create \
    -c conda-forge -c bioconda \
    -n snakem \ # name of the environment
    python=3.10 snakemake=7.19

Alternatively, if you are already familiar with conda and creating environments, it is suggested to install mamba in your base environment and use that to build your environment.

# install mamba
conda install -n base -c conda-forge mamba

# create a Snakemake environment
mamba create \
    -c conda-forge -c bioconda \
    -n snakemake \ # name of the environment
    python=3.10 snakemake=7.19

2. Download the container

wget https://s3.msi.umn.edu/se-smallseq/public/sif/hoof.sif

This should take ~5 minutes.

3. Clone this repo

git clone git@github.com:jonahcullen/FARmiR.git
cd FARmiR

Input files

1. Copy and edit the config file

cp config/config.yaml.template config/config.yaml

Then open config/config.yaml and update

• Update the path to the container under the sif: field

sif: /absolute/path/to/hoof.sif

• The path to your sample sheet

samples: /absolute/path/to/samples.tsv

By default, FARmiR is set up for

• Single- or paired-end small RNA-seq
• The EquCab3 reference genome from Ensembl realease 103

These can be modified but doing so may require changes to how the pipeline handles alignment, annotation parsing, and additional processing steps.

💡 Need a different genome or annotation (e.g., the upcoming Equine T2T assembly)? Please open an issue or start a discussion — I’m happy to help guide integration of new references!

2. Prepare a sample sheet

You samples.tsv should be a tab-delimited file with (at minimum) the follwoing columns

sample	tissue	year	layout	fastq_1
H001	lamina	2024	single	/absolute/path/to/H001_lamina_R1_001.fastq.gz

Ensure the layout column is set to single or paired

Runing the pipeline

FARmiR includes a partially preconfigured Snakemake Slurm profile located in

profiles/slurm.farmir/

Before running the pipeline, copy the snakemake profile submission script

cp profile/slurm.farmir/slurm-submit.py.template profile/slurm.farmir/slurm-submit.py

Then open profile/slurm.farmir/slurm-submit.py and update the SBATCH_DEFAULTS section

SBATCH_DEFAULTS = """
                  ntasks=1 
                  mem=16g 
                  time=02:00:00 
                  partition=HPC_PARTITIONS
                  account=HPC_ACCOUNT
                  """

Replace

• HPC_PARTITIONS with the Slurm partition(s) available on your system separated by commas (e.g. general,bigmem)
• HPC_ACCOUNT with your user or group account name

Dry run

To check what would be executed without submitting any jobs

snakemake -s small_hoof.smk --configfile config/config.yaml -np

Full run (with Singularity on Slurm)

Before launching the pipeline, set the DATA_DIR env varaible to the top-level direcctory that contains all reference FASTQ files, either directly or in nested subdiretories.

This directory will be mounted into the container at runtime so that hoof.sif has access to the input files specific in your samples.tsv.

export DATA_DIR=/absolute/path/to/data

NOTE: your FASTQs do not need to be in the same folder - they just need to be accessible from somewhere within the DATA_DIR path

Then run the pipeline

snakemake -s small_hoof.smk \
    --use-singularity \
    --singularity-args "-B $PWD,$DATA_DIR" \
    --profile profile/slurm.farmir \
    --configfile config/config.yaml \
    --rerun-triggers mtime