nf-core/kmermaid

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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 run nf-core/kmermaid --reads '*_R{1,2}.fastq.gz' -profile standard,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/kmermaid

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/kmermaid 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
  • A generic configuration profile to be used with Docker
  • Pulls software from dockerhub: nfcore/kmermaid
• singularity
  • A generic configuration profile to be used with Singularity
  • Pulls software from DockerHub: nfcore/kmermaid
• 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

If left unspecified, no reads are used.

--csv_pairs

Use this to specify the location of a csv containing the columns sample_id, read1, read2 to your input paired-end FastQ files. For example:

--csv_pairs samples.csv

Please note the following requirements:

1. The CSV contains a header, typically sample_id, read1, read2, but the names aren’t used.
2. The first column contains a sample ID
3. The second column contains the full path to the R1 read
4. The third column contains the full path to the R2 read

If left unspecified, no reads are used.

--csv_singles

Use this to specify the location of a csv containing the columns sample_id, read1 to your input single-end FastQ files. For example:

--csv_singles samples.csv

Please note the following requirements:

1. The CSV contains a header, typically sample_id, read1 but the names aren’t used.
2. The first column contains a sample ID
3. The second column contains the full path to the R1 read

If left unspecified, no reads are used.

--fastas

Use this to specify the location of fasta sequence files. Multiple inputs can be separated by seimcolons (;). For example:

--fastas 'path/to/data/elephant.fasta;more/data/*.fasta'

Please note the following requirements:

1. The path must be enclosed in quotes
2. The path may have at one or more * wildcard character

If left unspecified, no samples are used.

--protein_fastas

Use this to specify the location of protein fasta sequence files. No trimming, subsampling, or protein translation is done on these. Multiple inputs can be separated by seimcolons (;). For example:

--protein_fastas 'path/to/data/elephant.fasta;more/data/*.fasta'

Please note the following requirements:

1. The path must be enclosed in quotes
2. The path may have at one or more * wildcard character

If left unspecified, no samples are used.

--sra

Use this to specify the location of fasta sequence files. Multiple inputs can be separated by seimcolons (;). For example:

--sra 'SRR4050379;SRR4050380;SRP016501'

Please note the following requirements:

1. The path must be enclosed in quotes
2. Any of the SRR, SRP, or PRJNA ids can be used

If left unspecified, no samples are used.

Adapter Trimming

If specific additional trimming is required (for example, from additional tags), you can use any of the following command line parameters. These affect the command used to launch fastp!

--skip_trimming

This allows to skip the trimming process to save time when re-analyzing data that has been trimmed already.

Ribosomal RNA removal

If rRNA removal is desired (for example, metatranscriptomics), add the following command line parameters. Please be adviced that by default these steps make use of the SILVA v119 database that requires licencing for commercial/non-academic entities.

--removeRiboRNA

Instructs to use SortMeRNA to remove reads related to ribosomal RNA (or any patterns found in the sequences defined by --rRNA_database_manifest).

--saveNonRiboRNAReads

By default, non-rRNA FastQ files will not be saved to the results directory. Specify this flag (or set to true in your config file) to copy these files when complete.

--rRNA_database_manifest

By default, rRNA databases in github biocore/sortmerna/rRNA_databases are used. Here the path to a text file can be provided that contains paths to fasta files (one per line, no ’ or ” for file names) that will be used for database creation for SortMeRNA instead of the default ones. You can see an example in the directory assets/rrna-default-dbs.txt. Consequently, similar reads to these sequences will be removed. Be aware that commercial/non-academic entities require licensing for SILVA with these default databases.

K-merization/Sketching program Options

By default, the k-merization and sketch creation program is sourmash.

--split_kmer

If --split_kmer is specified, then the Split K-mer Analysis (SKA) program (publication) is used to obtain k-mers from the data. This allows for a SNP to be present in the middle of a k-mer which can be advantageous for metagenomic analyses or with single-cell ATAC-seq data.

What does --ksize mean when --split_kmer is set

The meaning of ksize is different with split k-mers, so now the value specified by --ksize is just under half of the total sampled sequence size, where the middle base can be any base (N) [---ksize---]N[---ksize---]. When --split_kmer is set, then the default k-mer sizes are 9 and 15, for a total sequence unit size of 2*15+1 = 31 and 2*9+1 = 19 which is as if you specified on the command line --split_kmer --ksize 9,15. Additionally k-mer sizes with --split_kmer must be divisible by 3 (yes, this is inconvenient) and between 3 and 60 (inclusive). So the “total” 2*k+1 sizes can be:

• k = 3 —> 2*3 + 1 = 7 total length
• k = 6 —> 2*6 + 1 = 13 total length
• k = 9 —> 2*9 + 1 = 18 total length
• k = 12 —> 2*12 + 1 = 25 total length
• k = 15 —> 2*15 + 1 = 31 total length
• …
• k = 60 —> 2*60 + 1 = 121 total length

--subsample reads when --split_kmer is set

The subsample command is often necessary because the ska tool uses ALL the reads rather than a MinHash subsampling of them. If your input files are rather big, then the ska sketching command (ska fastq) runs out of memory, or it takes so long that it’s untenable. The --subsample command specifies the number of reads to be used. When e.g. --subsample 1000 is set, then 1000 reads (or read pairs) are randomly subsampled from the data using seqtk.

Which --molecules are valid when --split_kmer is set

Currently, --split_kmer only works with DNA sequence and not protein sequence, and thus will fail if protein or dayhoff is specified in --molecules.

--bam

For bam/10x files, Use this to specify the location of the bam file. For example:

--bam /path/to/data/10x-example/possorted_genome_bam

--barcodes_file

For bam/10x files, Use this to specify the location of tsv (tab separated file) containing cell barcodes. For example:

--barcodes_file /path/to/data/10x-example/barcodes.tsv

If left unspecified, barcodes are derived from bam are used.

--rename_10x_barcodes

For bam/10x files, Use this to specify the location of your tsv (tab separated file) containing map of cell barcodes and their corresponding new names(e.g row in the tsv file: AAATGCCCAAACTGCT-1 lung_epithelial_cell|AAATGCCCAAACTGCT-1). For example:

--rename_10x_barcodes /path/to/data/10x-example/barcodes_renamer.tsv

If left unspecified, barcodes in bam as given in barcodes_file are not renamed.

Sketch parameters

K-mer MinHash sketches are defined by three parameters:

1. The molecule used to create k-mer words from each sample
2. The ksize used to extract k-mer words from each sample
3. The number of k-mer words specified by the log2 sketch size

--molecule

The molecule can be either dna, protein, or dayhoff, and if all of them are desired, then they can be separated by columns.

• dna indicates to use the raw nucleotide sequence from each input file to create k-mers
• protein indicates to translate each DNA k-mer into protein using 6-frame translation, and hash the translated peptide fragment k-mers
• dayhoff, developed by Margaret Oakley Dayhoff is an extension of protein, where in addition to translating each amino acid k-mer in six frames, each amino acid is remapped to a degenerate amino acid encoding. This degenerate encoding doesn’t penalize large evolutionary distances as a k-mer changing. For example, an small residue change of an Alanine (A) to a Glycine (G) doesn’t hash to the same value and thus do not match, but a Dayhoff-encoded amino acid would allow for these small changes.
• While translating protein sequences both coding and non-coding nucleotide sequences are written to fasta files by sencha translate

• Default:
  • --molecule dna,protein,dayhoff
• DNA only:
  • --molecule dna

--ksize

The fundamental unit of the sketch is a hashed k-mer. The --ksize parameter determines how long of a DNA word to use to create the k-mers. When the molecule is protein or dayhoff, then the ksize/3 is used to create each k-mer.

NB: if either protein or dayhoff is specified, the k-mer size must be divisible by 3 Example parameters

• Default:
  • --ksize 21,27,33,51
• k-mer size of 21 only:
  • --ksize 21

--track_abundance

• Tracking abundance - add this parameter if we want to keep track of the number of times a hashed kmer appears.
  • --track_abundance

Sketch Size Parameters

--sketch_num_hashes / --sketch_num_hashes_log2

The log2 sketch size specifies the number of hashes (approximately the same as the number of k-mers) to use for the sketch. We have the option of using the log2 of the sketch size instead of the raw number of k-mers to be compatible for comparison with dashing that uses HyperLogLog instead of MinHash. Example parameters

• Default:
  • --sketch_num_hashes_log2 10,12,14,16
• Only a log2 sketch size of 8 (2^8 = 256):
  • --sketch_num_hashes_log2 8
• Only compute one signature for each sample, at 500 hashes each
  • --sketch_num_hashes 500
• Compute three signatures for each sample, at 500, 1000, and 2000 hashes each
  • --sketch_num_hashes 500,1000,2000

--sketch_scaled / --sketch_scaled_log2

Unique to sourmash, the --scaled option is another way to subsample k-mers, but instead of taking a “flat rate” of the same number of k-mers per sample, this subsamples every 1/N k-mers, where N is the --scaled parameter. Example parameters

• Compute three signatures for each sample, subsampling 1/500, 1/1000 and 1/10 total k-mers:
  • --sketch_scaled 500,1000,10
• Compute a single signature for each sample, subsampling 1/500 k-mers:
  • --sketch_scaled 500
• Compute two signatures for each sample, subsampling 1/(2^8) = 1/256 and 1/(2^10) = 1/1024, 1/(2^14) of total k-mers:
  • --sketch_scaled_log2 8,10
• Compute one signature for each sample, subsampling 1/(2^2) = 1/4 total k-mers
  • --sketch_scaled_log2 2

--skip_compare

This allows to skip the sourmash or SKA compare process when there are:

1. Too many samples to compare so it’ll take too long/run out of memory anyway
2. Won’t actually use the compare result that has been trimmed already.

--skip_compute

If one wants to only translate protein sequences or extract per-cell fastqs from single-cell .bam files, then the --skip_compute option may be useful. This allows the user to skip the sourmash process you won’t actually use the compute result. If --skip_compute is true, then --skip_compare must be specified as true as well. While translating protein sequences both coding and non-coding nucleotide sequences are written to fasta files by sencha translate

--save_fastas

1. The save_fastas used to save the sequences of each unique barcode in the bam file. It is a path relative to outdir to save unique barcodes to files namely {CELL_BARCODE}.fasta. These fastas are computed once for one permutation of ksize, molecule, and log2_sketch_size, further used to compute the signatures and compare signature matrix for all permutations of ksizes, molecules, and log2_sketch_size. This is done to save the time on saving the computational time and storage in obtaining unique barcodes, sharding the bam file. Example parameters

• Default: Save fastas in a directory called fastsas inside outdir:
  • --save_fastas "fastas"

Bam optional parameters

--write_barcode_meta_csv

This creates a CSV containing the barcode and number of UMIs per barcode, written in a path relative to ${params.outdir}/barcode_metadata. This csv file is empty with just header when the tenx_min_umi_per_cell is zero i.e reads and UMIs per barcode are calculated only when the barcodes are filtered based on tenx_min_umi_per_cell

This creates a CSV containing the number of reads and number of UMIs per barcode, written in a path relative to ${params.outdir}/barcode_metadata. This csv file is empty with just header when the tenx_min_umi_per_cell is zero i.e reads and UMIs per barcode are calculated only when the barcodes are filtered based on tenx_min_umi_per_cell Example parameters

• Default: barcode metadata is not saved
• Save fastas in a file cinside outdir/barcode/metadata:
  • --write_barcode_meta_csv "barcodes_counts.csv"

--tenx_min_umi_per_cell

The parameter --tenx_min_umi_per_cell ensures that a barcode is only considered a valid barcode read and its sketch is written if number of unique molecular identifiers (UMIs, aka molecular barcodes) are greater than the value specified. Example parameters

• Default: tenx_min_umi_per_cell is 0
• Set minimum UMI per cellular barcode as 10:
  • --tenx_min_umi_per_cell 10

--shard_size

The parameter --shard_size specifies the number of alignments/lines in each bam shard. Example parameters

• Default: shard_size is 350
• Save fastas in a directory called fastas inside outdir:
  • --shard_size 400

Reference Genomes

The pipeline config files come bundled with paths to the illumina iGenomes reference index files. If running with docker or AWS, the configuration is set up to use the AWS-iGenomes resource.

--genome (using iGenomes)

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

You can find the keys to specify the genomes in the iGenomes config file. Common genomes that are supported are:

• Human
  • --genome GRCh37
• Mouse
  • --genome GRCm38
• Drosophila
  • --genome BDGP6
• S. cerevisiae
  • --genome 'R64-1-1'

There are numerous others - check the config file for more.

Note that you can use the same configuration setup to save sets of reference files for your own use, even if they are not part of the iGenomes resource. See the Nextflow documentation for instructions on where to save such a file.

The syntax for this reference configuration is as follows:

params {
  genomes {
    'GRCh37' {
      fasta   = '<path to the genome fasta file>' // Used if no star index given
    }
    // Any number of additional genomes, key is used with --genome
  }
}

--fasta

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

--fasta '[path to Fasta reference]'

--igenomes_ignore

Do not load igenomes.config when running the pipeline. You may choose this option if you observe clashes between custom parameters and those supplied in igenomes.config.

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.