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nf-core/predictorthologs: Usage

Table of contents

Introduction

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/predictorthologs --reads '*_R{1,2}.fastq.gz' -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
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/predictorthologs

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/predictorthologs 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.

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

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 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.

  • docker
  • singularity
  • conda
    • Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker or Singularity.
    • A generic configuration profile to be used with Conda
    • Pulls most software from Bioconda
  • 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 FastQ files. For example:

--reads 'path/to/data/sample_*_{1,2}.fastq'

Please note the following requirements:

  1. The path must be enclosed in quotes
  2. The path must have at least one * wildcard character
  3. When using the pipeline with paired end data, the path must use {1,2} notation to specify read pairs.

If left unspecified, a default pattern is used: data/*{1,2}.fastq.gz

--single_end

By default, the pipeline expects paired-end data. If you have single-end data, you need to specify --single_end on the command line when you launch the pipeline. A normal glob pattern, enclosed in quotation marks, can then be used for --reads. For example:

--single_end --reads '*.fastq'

It is not possible to run a mixture of single-end and paired-end files in one run.

--csv

Input a csv of sample ids and fasta filenames

Simple fasta input

The simplest input of fastas is a csv that looks like:

sample_id,fasta
sample1,sample1.fasta
sample2,sample2.fasta

Bam input

Pipeline expects data in .fastq format. If using a bam instead you need to specify the -bam parameter. Bam deduplication is run by default with sambamba, if you wish to skip deduplication supply the -skip_remove_duplicates_bam flag. Deduplication is also skipped if -bai flag is specified with index file

Differential hash expression

To do differential hash expression and then search for the enriched hashes in a database, the csv needs to contain the following columns:

  • sample_id: a uniquely identifying name
  • fasta: path to (translated protein) fasta file for the sample
  • sig: path to a sourmash signature file for the sample
  • group: a filepath-friendly name (no weird characters like / or |) of the group, to subset the data on

Additionally, the parameters --sourmash_ksize and --sourmash_molecule must be provided.

Here is an example signature:

sample_id,fasta,group,sig
sample1,sample1__coding_reads_peptides.fasta,Mostly marrow unaligned,sample1_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample2,sample2__coding_reads_peptides.fasta,Mostly marrow unaligned,sample2_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample3,sample3__coding_reads_peptides.fasta,Mostly marrow unaligned,sample3_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample4,sample4__coding_reads_peptides.fasta,Mostly marrow unaligned,sample4_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample5,sample5__coding_reads_peptides.fasta,Mostly marrow unaligned,sample5_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample6,sample6__coding_reads_peptides.fasta,Liver unaligned,sample6_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample7,sample7__coding_reads_peptides.fasta,Liver unaligned,sample7_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample8,sample8__coding_reads_peptides.fasta,Liver unaligned,sample8_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample9,sample9__coding_reads_peptides.fasta,Liver unaligned,sample9_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig
sample10,sample10__coding_reads_peptides.fasta,Liver unaligned,sample10_molecule-dayhoff_ksize-45_log2sketchsize-14_trackabundance-true.sig

Reference proteomes

There are two different kinds of reference proteomes used in this pipeline:

  • Reference proteome for translation, --proteome_translate_fasta
    • In general, it is desirable for this first proteome used for translation is a very conservative set of highly curated protein sequences, such as manually curated from UniProt/SwissProt.
    • The reason for this is that it is not desirable to have false positives when translating the sequences, and to only have highly trustworthy translated sequences for downstream processing
  • Reference proteome for searching translated proteins, --proteome_search_fasta
    • This proteome for searching can be more permissive (a superset of above) as this is used for searching, and we're interested in casting the widest net for finding potential matches, thus we recommend RefSeq over UniProt as their submission guidelines are more permissive.
    • A caveat is that in RefSeq, there are also many dubious sequences, and fortunately these are easy to find with the identifier. Sequence IDs that start with NP_ are the most trustworthy as they have an associated NM_ (protein-coding RNA transcirpt) or NC_ (complete genomic molecule) accessions. Read more about the wild, wild world of NCBI accession ids here

Proteomes for translating

--proteome_translate_fasta

We recommend using manually curated sequences from UniProt/SwissProt. By default, we use the Human reference proteome ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/reference_proteomes/Eukaryota/UP000005640_9606.fasta.gz. If your organism of interest is closely related to any of the organisms in the Reference Proteomes dataset, we recommend using that.

If you are using a broad dataset of many species, we recommend combining their manually curated proteomes into one, or using UniProt's website to download all the manually curated sequences for that clade.

--translate_peptide_ksize & --translate_peptide_molecule

These parameters influence how the translated sequences are pulled out.

Here are our recommendations for a variety of divergence times:

  • Closely related (<100 million years diverged), e.g. human and mouse:
    • --translate_peptide_molecule protein
    • --translate_peptide_ksize 9
  • Medium-diverged (100 million years ago < x < 500 million years ago), e.g. human and zebrafish:
    • --translate_peptide_molecule dayhoff
    • --translate_peptide_ksize 15
  • Largely-diverged (500 million years ago < x < 1000 million years ago), e.g. Bilateria:
    • --translate_peptide_molecule hp
    • --translate_peptide_ksize 45

Proteomes for searching

--refseq_release (using NCBI RefSeq)

There are 31 different species supported in the iGenomes references. To run the pipeline, you must specify which to use with the --genome flag.

Common proteomes that are supported are valid terms from NCBI RefSeq Releases. We recommend using the narrowest group for your particular search of interest. E.g. if you are searching within mammals, use the "vertebrate_mammalian" group

  • RefSeq Complete
    • --refseq_release complete
  • Archea
    • --refseq_release archea
  • Bacteria
    • --refseq_release bacteria
  • Fungi
    • --refseq_release fungi
  • Invertebrate
    • --refseq_release invertebrate
  • Mitochondria
    • --refseq_release mitochondrion
  • Other
    • --refseq_release other
  • Plant
    • --refseq_release plant
  • Plasmid
    • --refseq_release plasmid
  • Plastid
    • --refseq_release plastid
  • Vertebrate (Mammals)
    • --refseq_release vertebrate_mammalian
  • Vertebrate (Other)
    • --refseq_release vertebrate_other
  • Viral
    • --refseq_release viral

--proteome_search_fasta

If you prefer, you can specify the full path to your reference genome when you run the pipeline:

--proteome_search_fasta '[path to Proteome Fasta reference]'

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 -profile awsbatch and then specify all of the following parameters.

--awsqueue

The JobQueue that you intend to use on AWS Batch.

--awsregion

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

--awscli

The AWS CLI path in your custom AMI. Default: /home/ec2-user/miniconda/bin/aws.

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.

--email_on_fail

This works exactly as with --email, except emails are only sent if the workflow is not successful.

--max_multiqc_email_size

Threshold size for MultiQC report to be attached in notification email. If file generated by pipeline exceeds the threshold, it will not be attached (Default: 25MB).

-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: 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.