Introduction Usage Output Results Releases

Usage

Table of contents

General Nextflow info

Nextflow handles job submissions on SLURM or other environments, and supervises running the jobs. Thus the Nextflow process must run until the pipeline is finished. We recommend that you put the process running in the background through screen / tmux or similar tool. Alternatively you can run nextflow within a cluster job submitted your job scheduler.

It is recommended to limit the Nextflow Java virtual machines memory. We recommend adding the following line to your environment (typically in ~/.bashrc or ~./bash_profile):

NXF_OPTS='-Xms1g -Xmx4g'

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/ampliseq \
    -profile singularity \
    --reads "data" \
    --FW_primer GTGYCAGCMGCCGCGGTAA \
    --RV_primer GGACTACNVGGGTWTCTAAT \
    --metadata "data/Metadata.tsv"

This will launch the pipeline with the singularity configuration profile. See below -profile 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
results         # Finished results (configurable, see below)
.nextflow_log   # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

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/ampliseq

Reproducibility

It’s 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/ampliseq releases page and find the latest version number - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1.

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.

Main arguments

-profile

Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. Note that multiple profiles can be loaded, for example: -profile docker - the order of arguments is important!

If -profile is not specified at all the pipeline will be run locally and expects all software to be installed and available on the PATH.

  • awsbatch
    • A generic configuration profile to be used with AWS Batch.
  • conda
    • A generic configuration profile to be used with conda
    • Pulls most software from Bioconda
  • docker
  • singularity
  • test
    • A profile with a complete configuration for automated testing
    • Includes links to test data so needs no other parameters

--reads

Use this to specify the location of your input paired-end FastQ files.

For example:

--reads 'path/to/data'

Example for input data organization from one sequencing run with two samples:

data
  |-sample1_1_L001_R1_001.fastq.gz
  |-sample1_1_L001_R2_001.fastq.gz
  |-sample2_1_L001_R1_001.fastq.gz
  |-sample2_1_L001_R2_001.fastq.gz

Please note the following requirements:

  1. The path must be enclosed in quotes
  2. The folder must contain gzip compressed paired-end demultiplexed fastq files. If the file names do not follow the default ("/*_R{1,2}_001.fastq.gz"), please check --extension.
  3. If your data is scattered, a directory with symlinks to your actual data might be a solution.
  4. All sequencing data should originate from one sequencing run, because processing relies on run-specific error models that are unreliable when data from several sequencing runs are mixed. Sequencing data originating from multiple sequencing runs requires additionally the parameter --multipleSequencingRuns and a specific folder structure, see here.

--FW_primer and --RV_primer

In amplicon sequencing methods, PCR with specific primers produces the amplicon of intrest. These primer sequences need to be trimmed from the reads before further processing and are also required for producing an appropriate classifier. For example:

--FW_primer GTGYCAGCMGCCGCGGTAA --RV_primer GGACTACNVGGGTWTCTAAT

--metadata

This is optional, but for performing downstream analysis such as barplots, diversity indices or differential abundance testing, a metadata file is essential. For example:

--metadata "path/to/metadata.tsv"

Please note the following requirements:

  1. The path must be enclosed in quotes
  2. The metadata file has to follow the QIIME2 specifications
  3. In case of multiple sequencing runs, specific naming of samples are required, see here

Other input options

--extension

Indicates the naming of sequencing files (default: "/*_R{1,2}_001.fastq.gz").

Please note:

  1. The prepended slash (/) is required
  2. The star (*) is the required wildcard for sample names
  3. The curly brackets ({}) enclose the orientation for paired end reads, seperated by a comma (,).
  4. The pattern must be enclosed in quotes

For example for one sample (name: 1) with forward (file: 1_a.fastq.gz) and reverse (file: 1_b.fastq.gz) reads in folder data:

--reads "data" --extension "/*_{a,b}.fastq.gz"

--multipleSequencingRuns

If samples were sequenced in multiple sequencing runs. Expects one subfolder per sequencing run in the folder specified by --reads containing sequencing data of the specific run. Also, fastQC and MultiQC are skipped because multiple sequencing runs might create overlapping file names that crash MultiQC.

To prevent overlapping sample names from multiple sequencing runs, sample names obtained from the sequencing files will be renamed automatically by adding the folder name as prefix seperated by a string specified by --split. Accordingly, the sample name column in the metadata file --metadata require values following subfolder-samplename.

Example for input data organization:

data
  |-run1
  |  |-sample1_1_L001_R{1,2}_001.fastq.gz
  |  |-sample2_1_L001_R{1,2}_001.fastq.gz
  |
  |-run2
     |-sample3_1_L001_R{1,2}_001.fastq.gz
     |-sample4_1_L001_R{1,2}_001.fastq.gz

In this example the first column in the metadata file requires the values run1-sample1 … run2-sample4 (instead of sample1, …, sample4).

Example command to analyze this data in one pipeline run:

nextflow run nf-core/ampliseq \
    -profile singularity \
    --reads "data" \
    --FW_primer GTGYCAGCMGCCGCGGTAA \
    --RV_primer GGACTACNVGGGTWTCTAAT \
    --metadata "data/Metadata.tsv" \
    --multipleSequencingRuns

--split

A string that will be used between the prepended run/folder name and the sample name. Only used with --multipleSequencingRuns (default: "-").

For example using the string link:

--split "link"

Please note:

  1. Run/folder names may not contain the string specified by --split
  2. No underscore(s) allowed
  3. Must be enclosed in quotes
  4. The metadata sheet has to be adjusted, instead of using run-sample in the first column, in this example runlinksample is required

--phred64

If the sequencing data has PHRED 64 encoded quality scores (default: PHRED 33)

Cutoffs

--trunclenf and --trunclenr

Read denoising by DADA2 creates an error profile specific to a sequencing run and uses this to correct sequencing errors. This method requires all reads to have the same length and as high quality as possible while maintaining at least 20 bp overlap for merging. One cutoff for the forward read --trunclenf and one for the reverse read --trunclenr truncate all longer reads at that position and drop all shorter reads. These cutoffs are usually chosen visually using --untilQ2import, inspecting the quality plots in “results/demux”, and resuming analysis with --Q2imported. If not set, these cutoffs will be determined automatically for the position before the mean quality score drops below --trunc_qmin.

For example:

--trunclenf 180 --trunclenr 120

Please note:

  1. Too agressive truncation might lead to insufficient overlap for read merging
  2. Too less truncation might reduce denoised reads
  3. The code choosing these values automatically cannot take the points above into account, therefore setting --trunclenf and --trunclenr is recommended

--trunc_qmin

Automatically determine --trunclenf and --trunclenr before the mean quality score drops below --trunc_qmin (default: 25). For example:

--trunc_qmin 35

Please note:

  1. The code choosing --trunclenf and --trunclenr using --trunc_qmin automatically cannot take amplicon length or overlap requirements for merging into account, therefore setting --trunclenf and --trunclenr is preferred
  2. The default value of 25 is recommended. However, very good quality data with large paired sequence overlap might justify a higher value (e.g. 35). Also, very low quality data might require a lower value.

Other options

--untilQ2import

Computes all steps until quality plots aiding the choosing of --trunclenf and --trunclenr.

--Q2imported

Analysis starting with a QIIME2 artefact with trimmed reads, typically produced before with --untilQ2import. This is only supported for data from a single sequencing run.

For data from multiple sequencing runs with --multipleSequencingRuns the pipeline can be first run with --untilQ2import and next run without --untilQ2import but with -resume. For more details see here.

--keepIntermediates

Keep additional intermediate files, such as trimmed reads or various QIIME2 archives.

Visually choosing sequencing read truncation cutoffs

While --untilQ2import with --multipleSequencingRuns is currently supported, --Q2imported is not. The pipeline can be first run with --untilQ2import, than --trunclenf and --trunclenr are visually chosen, and than the pipeline can be continued without --untilQ2import but with --trunlenf, --trunlenr, and -resume.

For example:

(1) To produce quality plots and choose truncation values:

nextflow run nf-core/ampliseq \
    -profile singularity \
    --reads "data" \
    --FW_primer GTGYCAGCMGCCGCGGTAA \
    --RV_primer GGACTACNVGGGTWTCTAAT \
    --metadata "data/Metadata.tsv" \
    --multipleSequencingRuns \
    --untilQ2import

(2) To finish analysis:

nextflow run nf-core/ampliseq \
    -profile singularity \
    --reads "data" \
    --FW_primer GTGYCAGCMGCCGCGGTAA \
    --RV_primer GGACTACNVGGGTWTCTAAT \
    --metadata "data/Metadata.tsv" \
    --multipleSequencingRuns \
    --trunclenf 200 \
    --trunclenr 180 \
    -resume

Reference database

By default, the workflow downloads SILVA v132 and extracts reference sequences and taxonomy clustered at 99% similarity and trains a Naive Bayes classifier to assign taxonomy to features.

--classifier

If you have trained a compatible classifier before, from sources such as SILVA, Greengenes or RDP. For example:

--classifier "FW_primer-RV_primer-classifier.qza"

Please note the following requirements:

  1. The path must be enclosed in quotes
  2. The cassifier is a Naive Bayes classifier produced by “qiime feature-classifier fit-classifier-naive-bayes” (e.g. by this pipeline or from QIIME2 resources)
  3. The primer pair for the amplicon PCR and the computing of the classifier are exactly the same
  4. The classifier has to be trained by the same version of scikit-learn as this version of the pipeline uses (0.19.1)

--classifier_removeHash

Remove all hash signs from taxonomy strings, resolves a rare ValueError during classification (process classifier).

Statistics

--metadata_category

Here columns in the metadata sheet can be chosen with groupings that are used for diversity indices and differential abundance analysis. By default, all suitable columns in the metadata sheet will be used if this option is not specified. Suitable are columns which are categorical (not numerical) and have multiple different values which are not all unique. For example:

--metadata_category "treatment1,treatment2"

Please note the following requirements:

  1. Comma seperated list enclosed in quotes
  2. May not contain whitespace characters
  3. Each comma seperated term has to match exactly one column name in the metadata sheet

Filters

--retain_untrimmed

When read sequences are trimmed, untrimmed read pairs are discarded routinely. Use this option to retain untrimmed read pairs. This is usually not recommended and is only of advantage for specific protocols that prevent sequencing PCR primers. For example:

--retain_untrimmed

--exclude_taxa

Depending on the primers used, PCR might amplify unwanted or off-target DNA. By default sequences originating from mitochondria or chloroplasts are removed. The taxa specified are excluded from further analysis.

For example to exclude any taxa that contain mitochondria, chloroplast, or archea:

--exclude_taxa "mitochondria,chloroplast,archea"

If you prefer not filtering the data, specify:

--exclude_taxa "none"

Please note the following requirements:

  1. Comma seperated list enclosed in quotes
  2. May not contain whitespace characters
  3. Features that contain one or several of these terms in their taxonomical classification are excluded from further analysis
  4. The taxonomy level is not taken into consideration

--min_frequency

Remove entries from the feature table below an absolute abundance threshold (default: 1, meaning filter is disabled). Singletons are often regarded as artifacts, choosing a value of 2 removes sequences with less than 2 total counts from the feature table.

For example to remove singletons choose:

--min_frequency 2

--min_samples

Filtering low prevalent features from the feature table, e.g. keeping only features that are present in at least two samples can be achived by choosing a value of 2 (default: 1, meaning filter is disabled). Typically only used when having replicates for all samples.

For example to retain features that are present in at least two sample:

--min_samples 2

Please note this is independent of abundance.

Skipping steps

--onlyDenoising

Skip all steps after denoising, produce only sequences and abundance tables on ASV level.

--skip_fastqc

Skip FastQC, minor time saving.

--skip_alpha_rarefaction

Skip alpha rarefaction, minor time saving.

--skip_taxonomy

Skip taxonomic classification, will essentially truncate the workflow after denoising.

--skip_barplot

Skip producing barplot, minor time saving.

--skip_abundance_tables

Skip producing most relative abundance tables, minor time saving.

--skip_diversity_indices

Skip alpha and beta diversity analysis, large time saving.

--skip_ancom

Skip differential abundance testing, large time saving.

Job Resources

Automatic resubmission

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 an error code of 143 (exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.

Custom resource requests

Wherever process-specific requirements are set in the pipeline, the default value can be changed by creating a custom config file. See the files hosted at nf-core/configs for examples.

If you are likely to be running nf-core pipelines 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 (see definition below). 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.

If you have any questions or issues please send us a message on Slack.

AWS Batch specific parameters

Running the pipeline on AWS Batch requires a couple of specific parameters to be set according to your AWS Batch configuration. Please use the -awsbatch profile and then specify all of the following parameters.

--awsqueue

The JobQueue that you intend to use on AWS Batch.

--awsregion

The AWS region to run your job in. Default is set to eu-west-1 but can be adjusted to your needs.

Please make sure to also set the -w/--work-dir and --outdir parameters to a S3 storage bucket of your choice - you’ll get an error message notifying you if you didn’t.

Other command line parameters

--outdir

The output directory where the results will be saved.

--email

Set this parameter to your e-mail address to get a summary e-mail with details of the run sent to you when the workflow exits. If set in your user config file (~/.nextflow/config) then you don’t need to specify this on the command line for every run.

-name

Name for the pipeline run. If not specified, Nextflow will automatically generate a random mnemonic.

This is used in the MultiQC report (if not default) and in the summary HTML / e-mail (always).

NB: Single hyphen (core Nextflow option)

-resume

Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.

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.

NB: Single hyphen (core Nextflow option)

-c

Specify the path to a specific config file (this is a core NextFlow command).

NB: Single hyphen (core Nextflow option)

Note - you can use this to override pipeline defaults.

--custom_config_version

Provide git commit id for custom Institutional configs hosted at nf-core/configs. This was implemented for reproducibility purposes. Default is set to master.

## Download and use config file with following git commid id
--custom_config_version d52db660777c4bf36546ddb188ec530c3ada1b96

--custom_config_base

If you’re running offline, nextflow will not be able to fetch the institutional config files from the internet. If you don’t need them, then this is not a problem. If you do need them, you should download the files from the repo and tell nextflow where to find them with the custom_config_base option. For example:

## Download and unzip the config files
cd /path/to/my/configs
wget https://github.com/nf-core/configs/archive/master.zip
unzip master.zip
 
## Run the pipeline
cd /path/to/my/data
nextflow run /path/to/pipeline/ --custom_config_base /path/to/my/configs/configs-master/

Note that the nf-core/tools helper package has a download command to download all required pipeline files + singularity containers + institutional configs in one go for you, to make this process easier.

--max_memory

Use to set a top-limit for the default memory requirement for each process. Should be a string in the format integer-unit. eg. --max_memory '8.GB'

--max_time

Use to set a top-limit for the default time requirement for each process. Should be a string in the format integer-unit. eg. --max_time '2.h'

--max_cpus

Use to set a top-limit for the default CPU requirement for each process. Should be a string in the format integer-unit. eg. --max_cpus 1

--plaintext_email

Set to receive plain-text e-mails instead of HTML formatted.

--monochrome_logs

Set to disable colourful command line output and live life in monochrome.

--multiqc_config

Specify a path to a custom MultiQC configuration file.
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