nf-core/eqtl is a bioinformatics best-practice analysis pipeline for eqtl analysis.

This pipeline is running TensorQTL and/or LIMIX on bulk and single cell RNA seq datasets and assessed the overlap of the eGenes identified by both methodologies. While TensorQTL is very fast, this methodology uses linear regression which may not be capable in adequately represent the underlying population structure and other covariates, whereas Limix, while very computationally intensive is based on the linear mixed models (LMM) where the kinship matrices can be provided and hence accounting for random effects in a better manner.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

1. Genotype preperation, filtering and subsetting (bcftools)
2. Genotype conversion to PLINK format and filtering (PLINK2)
3. Genotype kinship matrix calculation (PLINK2)
4. Genotype and Phenotype PC calculation and QTL mapping with various number of PCs (PLINK2)
5. LIMIX eqtl mapping (LIMIX)
6. TensorQTL eqtl mapping (TensorQTL)

1. Install Nextflow (>=21.04.0)

2. Install any of Docker, Singularity

3. Download the pipeline and test it on a minimal dataset with a single command:

   nextflow run /path/to/cloned/QTLight -profile test_bulk,<docker/singularity/institute>

4. Prepeare the input.nf parameters file:

   params{
       method= 'single_cell' //or a [bulk | single_cell] (if single cell used the *phenotype_file* is a h5ad file)
       input_vcf ='/path/to/genotype/vcf/file.vcf'
       genotype_phenotype_mapping_file = '' //if bulk RNA seq data is fed in then need a tsv file with 3 columns - [Genotype	RNA	Sample_Category]
       annotation_file = './assets/annotation_file.txt'
       phenotype_file = 'path/to/adata.h5ad' //this should point to h5ad file in a single cell experiments or a star counts matrices for the bulk rna seq data
       aggregation_collumn='Azimuth:predicted.celltype.l2' // for scRNA seq data since we feed in the h5ad file we specify here which obs entry to account for for aggregating data.
   }

   example genotype_phenotype_mapping_file

   Genotype	RNA	Sample_Category
   HPSI0713i-aehn_22	MM_oxLDL7159503	M0_Ctrl
   HPSI0713i-aehn_22	MM_oxLDL7159504	M0_oxLDL
   HPSI0713i-aehn_22	MM_oxLDL7159505	M1_oxLDL

5. Start running your own analysis!

   nextflow run /path/to/cloned/QTLight -profile sanger -resume -c input.nf

The nf-core/eqtl pipeline comes with documentation about the pipeline usage and output.

nf-core/QTLight was developed by Matiss Ozols, Anna Cuomo, Marc Jan Bonder, Hannes Ponstingl, Tobi Alegbe, Bradley Harris.

Currently pipeline has not been published but we would really appreciate if you could please acknowlage the use of this pipeline in your work:

Ozols, M. et al. 2023. eqtl (Quantitative Trait Loci mapping pipeline): GitHub. https://github.com/wtsi-hgi/eqtl.

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.