usage.md

neurogenomics/jacquard: Usage

Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files.

Introduction

jacquard prepares scMultiome reads with carmack, then fans each sample out into one scRNA arm and one scTIP arm per target index found in the library, gating each arm on its read count before aligning it. See output.md for what each arm writes.

Samplesheet input

A comma-separated file with three columns and a header row, one row per FastQ pair:

--input '[path to samplesheet file]'

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

Column	Description
sample	Sample name. Repeat it to list one sample's lanes. Cannot contain spaces.
fastq_1	Full path to the reads 1 FastQ. Must be gzipped, with extension .fastq.gz or .fq.gz.
fastq_2	Full path to the reads 2 FastQ, with the same requirements.

All three columns are mandatory. Single-end input is not supported: carmack reads the barcode, UMI and target index off R1 and carries the raw R2 through to prepare-reads, so a row with an empty fastq_2 is rejected during schema validation, before any process is submitted.

Samples sequenced across several lanes

A library split over several flowcell lanes goes in as one row per lane, all carrying the same sample value, as CONTROL_REP2 does above. The rows are concatenated into a single R1/R2 pair — in the order they appear in the samplesheet, R1 and R2 alike, so the two files stay mate-synchronised — before read preparation begins, so barcodes, UMIs and target indices are counted once over the whole library rather than once per lane. A sample given on a single row is used as it stands and no concatenation runs for it.

A FASTQ belongs to exactly one row: a file listed twice, in either column or under two sample names, is rejected at validation, since concatenating a lane with itself would double every read count the run reports, and a repeated R2 would leave the merged R2 out of step with its R1. Every sample whose rows are merged is named in the run's log, so two libraries given one name by mistake show up there.

An example samplesheet has been provided with the pipeline.

Pipeline parameters

nextflow run neurogenomics/jacquard --help_full prints every parameter with its full help text. The parameters specific to this pipeline are below.

Read preparation

Parameter	Default	Description
--chemistry	carmack_custom_seq_1_0	carmack chemistry describing the barcode, UMI and target-index layout of R1. One of carmack_custom_seq_1_0, carmack_custom_seq_1_0_primd, hydrop; the list tracks the vendored carmack build.
--carmack_fast	false	Pass --fast to carmack extract-barcodes, dropping its local-alignment fallback. Faster, at the cost of the barcodes only that stage recovers.

Arm gate

Parameter	Default	Description
--min_arm_reads	1000	Reads an arm must carry to be processed. An arm below the threshold is dropped, not failed — see Arm gate.
--fail_on_no_arms	false	Fail the run when every arm of a sample falls below --min_arm_reads, instead of dropping that sample and continuing.

--fail_on_no_arms does not fail fast. A sample's verdict is only complete once its read preparation has finished, and the check waits for every sample to reach that point, by which time arms that cleared the gate earlier may already be aligning. Expect it to abort a run partway through rather than before any alignment work starts; what it buys you is that the failure is reported rather than left as a sample quietly missing from the results.

Trimming

Parameter	Default	Description
--skip_trimming	false	Align and quantify the reads prepare-reads wrote, untrimmed. Every downstream step still runs.

Each arm that clears the gate is trimmed with fastp against an adapter FASTA built from the sequences the selected --chemistry declares, in both orientations — read-through reaches R2 as the reverse complement, and fastp does not reverse-complement a FASTA entry for you. No adapter sequence is ever configured; a chemistry declaring none falls back to fastp's read-pair overlap detection.

Trimming can leave an arm below --min_arm_reads. Such an arm is dropped exactly as the gate drops one, and its row in arm_gate.tsv is replaced with a FAIL_TRIM verdict carrying the post-trim count — see Arm gate.

Deduplication

Parameter	Default	Description
--skip_linear_dedup	false	Leave the scTIP arm's alignments as umi_tools dedup left them, with no second deduplication pass.

The scTIP arm deduplicates twice. umi_tools dedup --paired keys on the UMI together with the coordinates of both mates, which collapses PCR duplicates but not the copies linear (T7 IVT) amplification made of one template: those share R1's 5' end and terminate independently, so they carry different mate ends and different UMIs and read as distinct molecules, inflating per-cell signal at each Tn5 insertion site. carmack's linear-dedup pass keys on that shared start alone — per cell, R1's strand-aware fragment position — and keeps the highest-scoring pair of each group.

--skip_linear_dedup ends the arm on the umi_tools BAM instead, which samtools stats then reports on; the two linear-dedup MultiQC sections are absent — see scTIP arm.

scRNA arm

Parameter	Default	Description
--solo_features	GeneFull	Features STARsolo counts UMIs against, space-separated — one or more of Gene, GeneFull, GeneFull_ExonOverIntron, GeneFull_Ex50pAS, SJ, Velocyto, one count matrix each. GeneFull counts intronic reads too, which are a large fraction of real signal in Tn5-derived multiome material. Gene is always counted alongside whatever is asked for.
--solo_multimappers	Unique	How STARsolo counts reads mapping to more than one gene, space-separated: one or more of Unique, Uniform, PropUnique, EM, Rescue. Unique counts only reads that map to one gene. Each other mode shares multimappers out between the genes they hit and adds a UniqueAndMult-<mode>.mtx beside the unique matrix. Nuclear RNA carries many intronic reads in repeats and paralogous loci, which EM recovers.
--solo_bam	false	Also publish a coordinate-sorted, indexed BAM whose records carry CB/UB (corrected cell barcode and UMI), CR/UR (as read) and GX/GN (gene), for genotype demultiplexing and RNA velocity. STAR sorts it in-process, since that is the only way it writes the corrected tags, so it costs extra memory.

References

Parameter	Description
--fasta	Genome FASTA. The scRNA arm builds its STAR index from this and --gtf; the scTIP arm builds its bowtie2 index from this alone.
--gtf	Gene annotation. Required alongside --fasta when --star_index is not given, since STARsolo can only assign a read to a gene against an annotation.
--star_index	Prebuilt STAR index directory; skips index building for the scRNA arm. It must carry the annotation you intend to count against, which STARsolo reads out of the index rather than off --gtf.
--bowtie2_index	Prebuilt bowtie2 index directory — the directory bowtie2-build wrote, holding the *.bt2 files, not a path prefix. Skips index building for the scTIP arm.

A reference is only demanded by the arm that would use it, and only once that arm has cleared the gate: a run whose scTIP arms were all dropped needs no bowtie2 index at all.

Stage control

--stop_after <stage> runs the pipeline as far as one stage and then reports. Unset, the whole pipeline runs. MultiQC assembles a report from whichever stages ran, so every stop point leaves one behind.

Stage	Stops after
barcodes	carmack extract-barcodes
umis	carmack extract-umis
targets	carmack assign-targets
readprep	carmack prepare-reads
gate	the arm fan-out and its gate report — no aligner runs, so no reference is needed

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run neurogenomics/jacquard --input ./samplesheet.csv --outdir ./results --fasta genome.fa --gtf genes.gtf -profile docker

This will launch the pipeline with the docker configuration profile. See below for more information about profiles.

Note that the pipeline will create the following files in your working directory:

work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

Warning

Do not use -c <file> to specify parameters as this will result in errors. Custom config files specified with -c must only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).

The above pipeline run specified with a params file in yaml format:

nextflow run neurogenomics/jacquard -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
fasta: 'genome.fa'
gtf: 'genes.gtf'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

Updating the pipeline

When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:

nextflow pull neurogenomics/jacquard

Reproducibility

It is a good idea to specify the pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.

First, go to the neurogenomics/jacquard releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Tip

If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.

Core Nextflow arguments

Note

These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen)

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.

Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Apptainer, Conda) - see below.

Important

We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.

The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to check if your system is supported, please see the nf-core/configs documentation.

Note that multiple profiles can be loaded, for example: -profile test,docker - the order of arguments is important! They are loaded in sequence, so later profiles can overwrite earlier profiles.

If -profile is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH. This is not recommended, since it can lead to different results on different machines dependent on the computer environment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• apptainer
  • A generic configuration profile to be used with Apptainer
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see this blog post.

You can also supply a run name to resume a specific run: -resume [run-name]. Use the nextflow log command to show previous run names.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and customise process resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c parameter. You can then create a pull request to the nf-core/configs repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs), and amending nfcore_custom.config to include your custom profile.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.

The Nextflow -bg flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.

Alternatively, you can use screen / tmux or similar tool to create a detached session which you can log back into at a later time. Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).

Nextflow memory requirements

In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. We recommend adding the following line to your environment to limit this (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'