nf-core

type access

nf-core is a collection of analysis pipelines built using Nextflow.

For more information, check here.

Supported pipelines

Pipeline

Release

Description

airrflow

B-cell and T-cell Adaptive Immune Receptor Repertoire (AIRR) sequencing analysis pipeline using the Immcantation framework

ampliseq

16S rRNA amplicon sequencing analysis workflow using QIIME2

atacseq

ATAC-seq peak-calling, QC and differential analysis pipeline

bacass

Simple bacterial assembly and annotation pipeline

bactmap

A mapping-based pipeline for creating a phylogeny from bacterial whole genome sequences

cageseq

CAGE-sequencing analysis pipeline with trimming, alignment and counting of CAGE tags

chipseq

ChIP-seq peak-calling, QC and differential analysis pipeline

clipseq

CLIP sequencing analysis pipeline for QC, pre-mapping, genome mapping, UMI deduplication, and multiple peak-calling options

coproid

Coprolite host Identification pipeline

cutandrun

Analysis pipeline for CUT&RUN and CUT&TAG experiments that includes QC, support for spike-ins, IgG controls, peak calling and downstream analysis

deepvariant

A Nextflow pipeline for running the Google DeepVariant variant caller.

diaproteomics

Automated quantitative analysis of DIA proteomics mass spectrometry measurement

dualrnaseq

Analysis of Dual RNA-seq data - an experimental method for interrogating host-pathogen interactions through simultaneous RNA-seq

eager

A fully reproducible and state-of-the-art ancient DNA analysis pipeline

epitopeprediction

A bioinformatics best-practice analysis pipeline for epitope prediction and annotation

fetchngs

Pipeline to fetch metadata and raw FastQ files from public and private databases

hic

Analysis of Chromosome Conformation Capture data (Hi-C)

hlatyping

Precision HLA typing from next-generation sequencing data

imcyto

Image Mass Cytometry analysis pipeline

mag

Assembly and binning of metagenomes

metaboigniter

Pre-processing of mass spectrometry-based metabolomics data with quantification and identification based on MS1 and MS2 data

methylseq

Methylation (Bisulfite-Sequencing) analysis pipeline using Bismark or bwa-meth + MethylDackel

mhcquant

Identify and quantify MHC eluted peptides from mass spectrometry raw

nanoseq

Nanopore demultiplexing, QC and alignment pipeline

nascent

Nascent transcription processing pipeline

neutronstar

De novo assembly pipeline for 10X linked-reads using Supernova

pgdb

The ProteoGenomics database generation workflow creates different protein databases for ProteoGenomics data analysis

proteomicslfq

Proteomics label-free quantification (LFQ) analysis pipeline

rnafusion

RNA-seq analysis pipeline for detection gene-fusions

rnaseq

RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control

sarek

Analysis pipeline to detect germline or somatic variants (pre-processing, variant calling and annotation) from WGS / targeted sequencing

scrnaseq

A single-cell RNAseq pipeline for 10X genomics data

slamseq

SLAMSeq processing and analysis pipeline

smrnaseq

A small-RNA sequencing analysis pipeline

viralrecon

Assembly and intrahost/low-frequency variant calling for viral samples

Install dependencies

Software dependencies can be installed inside the application container using the parameter: Additional dependencies. The list of dependencies can be specified either via a text file (.txt) or a YAML file (*.yml/*.yaml). The installation is done via the Conda command line package and environment manager. Alternatively, it is possible to load a Bash script (*.sh) with the list of shell commands to be used for the installation. The example below shows three equivalent ways to install dependencies:

bwa==0.7.17
samtools==1.6
java-jdk==7.0.91
bedtools==2.29.2
PyYAML==5.1.2
multiqc==1.8
name: base
channels:
  - bioconda
  - conda-forge
  - defaults

dependencies:
  - bioconda::bwa=0.7.17
  - bioconda::samtools=1.6
  - bioconda::java-jdk=7.0.91
  - bioconda::bedtools=2.29.2
  - pip:
    - PyYAML==5.1.2
    - multiqc==1.8
#!/usr/bin/env bash

set -eux

conda install -y -n base bwa=0.7.17 samtools=1.6 java-jdk=7.0.91 bedtools=2.29.2
pip install PyYAML==5.1.2 multiqc==1.8

The user can also install the required software dependencies via Conda by specifyifg the packages or the path(s) to the configuration YAML file(s) directly in the pipeline script. In this case the user must use the option --enable_conda.

Interactive mode

The Interactive mode parameter is used to start an interactive job session where the user can open a terminal window from the job progress page and execute shell commands.

Pipelines are executed as follows:

$ nextflow run ~/nf-core-<pipeline>-<release>/workflow/ --max_cpus $CORES --max_memory $MEMORY <options>

where $CORES and $MEMORY correspond to the maximum number of cores and total memory of the selected machine type.