nf-core/nascent

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Introduction

Samplesheet input

You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

Multiple runs of the same sample

The sample identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes:

sample,fastq_1,fastq_2
CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz
CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz
CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz

Full samplesheet

The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 3 columns to match those defined in the table below.

A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 6 samples, where TREATMENT_REP3 has been sequenced twice.

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_L002_R1_001.fastq.gz,AEG588A2_S2_L002_R2_001.fastq.gz
CONTROL_REP3,AEG588A3_S3_L002_R1_001.fastq.gz,AEG588A3_S3_L002_R2_001.fastq.gz
TREATMENT_REP1,AEG588A4_S4_L003_R1_001.fastq.gz,
TREATMENT_REP2,AEG588A5_S5_L003_R1_001.fastq.gz,
TREATMENT_REP3,AEG588A6_S6_L003_R1_001.fastq.gz,
TREATMENT_REP3,AEG588A6_S6_L004_R1_001.fastq.gz,

An example samplesheet has been provided with the pipeline.

Alignment Options

By default, the pipeline uses BWA (i.e. --aligner bwa) to map the raw FastQ reads to the reference genome. Research as to which aligner works best with Nascent Transcript and Transcription Start Site assays is pending.

Reference genome files

The minimum reference genome requirements are a FASTA and GTF file, all other files required to run the pipeline can be generated from these files. However, it is more storage and compute friendly if you are able to re-use reference genome files as efficiently as possible. It is recommended to use the --save_reference parameter if you are using the pipeline to build new indices (e.g. those unavailable on AWS iGenomes) so that you can save them somewhere locally. The index building step can be quite a time-consuming process and it permits their reuse for future runs of the pipeline to save disk space. You can then either provide the appropriate reference genome files on the command-line via the appropriate parameters (e.g. --star_index '/path/to/BWA/index/') or via a custom config file.

• If --genome is provided then the FASTA and GTF files (and existing indices) will be automatically obtained from AWS-iGenomes unless these have already been downloaded locally in the path specified by --igenomes_base.
• If --gff is provided as input then this will be converted to a GTF file, or the latter will be used if both are provided.

Quantification Options

Currently only featureCounts is supported for quantification. It counts both the genes, and the predicted transcripts.

Transcript Identification Options

The current options for transcript identification include GroHMM, HOMER, and PINTS.

The default transcript identification option is PINTS, and HOMER if the transcript assay_type is GROseq but this may change in future releases.

PINTS

PINTS handles the majority of the transcript identification, since it covers all of the supported assays.

PINTS can use a lot of memory while running, so a scatter-gather pattern was implemented.

It splits the identification up by the chromosomes available in the provided FASTA file. Some of the chromosomes are skipped because PINTS throws an error when it doesn’t find any regions. If this causes an issue with your analysis please open an issue.

GroHMM

groHMM is split into two steps: parameter tuning and transcript identification.

When running the pipeline with groHMM as a transcript identification method, the pipeline will automatically perform a parameter tuning process. This process is unique to the groHMM transcript identification method and is designed to select the optimal hold-out parameters for the groHMM algorithm. See this issue for more information.

In the groHMM vignette, the code is ran using a single mclapply call, which is a scatter gather approach. This is not ideal for large datasets, because it ends up being bottlenecked by the memory available on your local machine. To improve this, we have written a Nextflow script that runs the pipeline with a scatter gather approach. This is done by running the pipeline with a single hold-out parameter, and then the next parameter, and so on. This is more memory efficient and scales better to larger datasets. The results are then combined then combined in the end as intended and used in the transcript identification process.

groHMM Parameters

The detectTranscripts function also uses two hold-out parameters. These parameters, specified by the arguments LtProbB and UTS, represents the log-transformed transition probability of switching from transcribed state to non-transcribed state and variance of the emission probability for reads in the non-transcribed state, respectively. Holdout parameters are used to optimize the performance of HMM predictions on known genes.

In the pipeline, the parameters are specified as follows: grohmm_min_uts = 5 grohmm_max_uts = 45 grohmm_min_ltprobb = -100 grohmm_max_ltprobb = -400

Which will then create a job for each parameter combination. For example (5,-100), (5,-150), (10,-100), (10,-150)…

If you have indentified a good set of parameters, you can run the pipeline with those parameters by specifying, all 4 values.

For example if you have indentified that the best parameters for your data are 15,-200:

{
  "grohmm_min_uts": 15,
  "grohmm_max_uts": 15,
  "grohmm_min_ltprobb": -200,
  "grohmm_max_ltprobb": -200
}

Homer

HOMER is used for transcript identification when the assay_type is set to GROseq. HOMER’s GRO-seq analysis capabilities include:

• De novo transcript identification from GRO-seq data
• Support for analyzing nascent RNA production
• Detection of various RNA species including:
  • Protein coding transcripts
  • Promoter anti-sense transcripts
  • Enhancer templated transcripts (eRNAs)
  • Long and short non-coding RNAs
  • miRNA transcripts
  • Pol III and Pol I transcripts

HOMER uses uniquely mappable regions to improve transcript detection in repetitive regions. The pipeline can automatically download the appropriate uniqmap files for supported genomes:

• Human: hg19, hg38
• Mouse: mm10
• Fly: dm6

This setting is off by default

To find the full list of uniqmaps supplied by the author check http://homer.ucsd.edu/homer/data/uniqmap/. To build a uniqmap for a genome that isn’t supported, check out homer-uniqmap-nf.

The transcript detection algorithm:

1. Tracks along each strand looking for continuous GRO-seq signal
2. Starts transcripts when encountering high read density
3. Stops transcripts when signal decreases significantly
4. Creates new transcripts when signal increases sustainably
5. Filters out artifactual spikes that don’t extend over distance

Key parameters that can be tuned:

• tssFold: Fold change required at transcript start (default: 4)
• bodyFold: Fold change required in transcript body (default: 3)
• minBodySize: Minimum transcript body size (default: 600bp)
• maxBodySize: Maximum transcript body size (default: 10000bp)

withName: HOMER_FINDPEAKS {
    ext.args = "-style groseq -tssFold 4 -bodyFold 3"
}

For more info check the Homer GRO-seq Tutorial.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/nascent --input ./samplesheet.csv --outdir ./results --genome GRCh37 -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>.

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

nextflow run nf-core/nascent -profile docker -params-file params.yaml

with:

input: './samplesheet.csv'
outdir: './results/'
genome: 'GRCh37'
<...>

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 nf-core/nascent

Reproducibility

It is a good idea to specify a 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 nf-core/nascent 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 reproducbility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow arguments

-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.

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 see if your system is available in these configs 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 enviroment.

• 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 steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (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 tuning workflow resources section of the nf-core website.

Custom Containers

In some cases you may wish to change which container or conda environment a step of the pipeline uses 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'