neurogenomics/jacquard

Warning

jacquard is incomplete and under active development. Its inputs, parameters, outputs and defaults may change without notice between versions. For the latest changes, see the dev branch.

Introduction

neurogenomics/jacquard is a bioinformatics pipeline for scMultiome libraries that carry both a single-cell RNA readout and one or more scTIP target indices in the same pool. It prepares the reads with carmack, splits each sample into one arm per target index plus an scRNA arm, gates each arm on its read count, and then quantifies the scRNA arm with STARsolo and aligns and deduplicates every scTIP arm. It produces per-arm count matrices or deduplicated BAMs and a single MultiQC report over the whole run.

1. Read QC (FastQC)
2. Barcode, UMI and target-index extraction, and per-arm read preparation (carmack)
3. Arm fan-out and a per-arm read-count gate
4. scRNA arm: per-arm FastQC and quantification (STARsolo)
5. scTIP arm: adapter trimming against the chemistry's own sequences (fastp), per-arm FastQC, alignment (bowtie2) and per-cell deduplication, by UMI (UMI-tools) and then by insertion site (carmack)
6. Aggregate report (MultiQC)

Planned additions

The following are planned but not yet part of the pipeline:

• Cell calling, jointly across the scRNA and scTIP arms
• Peak calling on the scTIP arms
• scRNA filtering: doublet detection, ambient RNA removal and similar clean-up
• Late-stage QC

Usage

Note

If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

sample,fastq_1,fastq_2
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
CONTROL_REP2,AEG588A2_S2_L001_R1_001.fastq.gz,AEG588A2_S2_L001_R2_001.fastq.gz
CONTROL_REP2,AEG588A2_S2_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz

Each row is one R1/R2 pair and all three columns are mandatory. Repeat a sample name to list the flowcell lanes it was sequenced across, as CONTROL_REP2 does; they are concatenated into one pair before read preparation — see usage.md for the full contract.

Now, you can run the pipeline using:

nextflow run neurogenomics/jacquard \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --fasta genome.fa \
   --gtf genes.gtf \
   --outdir <OUTDIR>

Warning

Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

Credits

neurogenomics/jacquard was originally written by Hiranyamaya (Hiru) Dash and Chris Cheshire.

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

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

This pipeline uses code and infrastructure developed and maintained by the nf-core community, reused here under the MIT license.

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.