nf-core/scnanoseq

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Introduction

nf-core/scnanoseq is a bioinformatics best-practice analysis pipeline for 10X Genomics single-cell/nuclei RNA-seq for data derived from Oxford Nanopore Q20+ chemistry (R10.4 flow cells (>Q20)). Due to the expectation of >Q20 quality, the input data for the pipeline is not dependent on Illumina paired data. Please note scnanoseq can also process Oxford data with older chemistry, but we encourage usage of the Q20+ chemistry.

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.

Pipeline summary

1. Raw read QC (FastQC, NanoPlot, NanoComp and ToulligQC)
2. Unzip and split FastQ (gunzip)
   1. Optional: Split fastq for faster processing (split)
3. Trim and filter reads. (Nanofilt)
4. Post trim QC (FastQC, NanoPlot and ToulligQC)
5. Barcode detection using a custom whitelist or 10X whitelist. BLAZE
6. Extract barcodes. Consists of the following steps:
   1. Parse FASTQ files into R1 reads containing barcode and UMI and R2 reads containing sequencing without barcode and UMI (custom script ./bin/pre_extract_barcodes.py)
   2. Re-zip FASTQs (pigz)
7. Post-extraction QC (FastQC, NanoPlot and ToulligQC)
8. Alignment (minimap2)
9. SAMtools processing including (SAMtools):
   1. SAM to BAM
   2. Filtering of mapped only reads
   3. Sorting, indexing and obtain mapping metrics
10. Post-mapping QC in unfiltered BAM files (NanoComp, RSeQC)
11. Barcode tagging with read quality, BC, BC quality, UMI, and UMI quality (custom script ./bin/tag_barcodes.py)
12. Barcode correction (custom script ./bin/correct_barcodes.py)
13. Post correction QC for corrected bams (SAMtools)
14. UMI-based deduplication UMI-tools
15. Gene and transcript level matrices generation. IsoQuant
16. Preliminary matrix QC (Seurat)
17. Compile QC for raw reads, trimmed reads, pre and post-extracted reads, mapping metrics and preliminary single-cell/nuclei QC (MultiQC)

Usage

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

samplesheet.csv:

sample,fastq,cell_count
CONTROL_REP1,AEG588A1_S1.fastq.gz,5000
CONTROL_REP1,AEG588A1_S2.fastq.gz,5000
CONTROL_REP2,AEG588A2_S1.fastq.gz,5000
CONTROL_REP3,AEG588A3_S1.fastq.gz,5000
CONTROL_REP4,AEG588A4_S1.fastq.gz,5000
CONTROL_REP4,AEG588A4_S2.fastq.gz,5000
CONTROL_REP4,AEG588A4_S3.fastq.gz,5000

Each row represents a single-end fastq file. Rows with the same sample identifier are considered technical replicates and will be automatically merged. cell_count refers to the expected number of cells you expect.

nextflow run nf-core/scnanoseq \
   -profile <docker/singularity/.../institute> \
   --input samplesheet.csv \
   --outdir <OUTDIR>

see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

This pipeline produces feature barcode matrices at both the gene and transcript level and can retain introns within the counts themselves. These files are able to be ingested directly by most packages used for downstream analyses such as Seurat. In addition the pipeline produces a number of quality control metrics to ensure that the samples processed meet expected metrics for single-cell/nuclei data.

Troubleshooting

If you experience any issues, please make sure to submit an issue above. However, some resolutions for common issues will be noted below:

• One issue that has been observed is a recurrent node failure on slurm clusters that does seem to be related to submission of nextflow jobs. This issue is not related to this pipeline itself, but rather to nextflow itself. Our reserach computing are currently working on a resolution. But we have two methods that appear to help overcome should this issue arise:
  1. The first is to create a custom config that increases the memory request for the job that failed. This may take a couple attempts to find the correct requests, but we have noted that there does appear to be a memory issue occassionally with this errors.
  2. The second resolution is to request an interactive session with a decent amount of time and memory and cpus in order to run the pipeline on the single node. Note that this will take time as there will be minimal parallelization, but this does seem to resolve the issue.
• We acknowledge that analyzing PromethION is a common use case for this pipeline. Currently, the pipeline has been developed with defaults to analyze GridION and average sized PromethION data. For cases, where jobs have failed due for larger PromethION datasets, the defaults have been overwritten by a custom configuation file (provided by the -c Nextflow option) where resources were increased (substantially in some cases). Below are some of the overrides we have used, while these amounts may not work on every dataset, these will hopefully at least note which processes will need to have their resources increased:

 
process
{
    withName: '.*:BLAZE'
    {
        cpus = 24
        ext.args = '--threads 30'
    }
}
 
process
{
    withName: '.*:SAMTOOLS_SORT'
    {
        cpus = 20
    }
}
 
process
{
    withName: '.*:MINIMAP2_ALIGN'
    {
        cpus = 20
    }
}
 
process
{
    withName: '.*:ISOQUANT'
    {
        ext.args = {
            [
                "--threads 30",
                "--complete_genedb",
                params.stranded == "forward" ? "--stranded forward" : params.stranded == "reverse" ? "--stranded reverse" : "--stranded none",
            ].join(' ').trim()
        }
        time = '135.h'
    }
}

Credits

nf-core/scnanoseq was originally written by Austyn Trull, and Dr. Lara Ianov.

We would also like to thank the following people and groups for their support, including financial support:

• Dr. Elizabeth Worthey
• University of Alabama at Birmingham Biological Data Science Core (U-BDS), RRID
  , https://github.com/U-BDS
• Support from: 3P30CA013148-48S8

Contributions and Support

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

For further information or help, don’t hesitate to get in touch on the Slack #scnanoseq channel (you can join with this invite).

Citations

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

You can cite the nf-core publication as follows:

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.