nf-core/genomeqc

Edit

Introduction

nf-core/genomeqc is a pipeline build to aid in the diagnosis of the quality of genome assemblies. It can input several genomes and/or their annotations, and generates metrics such as completeness, contiguity, GC% or number of overlapping genes, which can be later used to assess their quality. Additionally, it outputs a phylogenetic tree plot with the summary metrics. The tree building method uses orthologous genes/BUSCO markers for a quick comparision of metrics across species/samples. This pipeline should not be used for phylogenetic inference.

Samplesheet input

Before running the pipeline, you will need to create a samplesheet with information about the assemblies you want to process. Use this parameter to specify its location. It has to be a comma-separated file with 5 columns, and a header row as shown in the examples below.

--input '[path to samplesheet file]'

The pipeline can be run using NCBI accessions (RefSeq or GenBank) or local files. It needs at least a genome (GenBank accession or local file in FASTA format) per species to run. If annotations (RefSeq accession or local file in GTF/GFF format) are added, the pipeline will run on both genomes and annotations. If you provide local FASTQ reads for an assembly, the pipeline will run Merqury to evaluate completness (see Running Merqury). If you provide taxids in NCBI format and an FCS-GX database, it will run the decontamination subworkflow (see Running the Decontamination subworkflow).

If running the pipeline using local files, point to the location these files using the fasta and/or gff fields:

species,fasta,gff
species_1,/path/to/genome1.fasta,/path/to/annotation1.gxf
species_2,/path/to/genome2.fasta,/path/to/annotation2.gxf
species_3,/path/to/genome3.fasta,/path/to/annotation3.gxf

If running the pipeline using ncbi accessions (GenBank and/or RefSeq), indicate the corresponding ID using the ncbi field:

species,ncbi
species_1,GCF_000000001.1
species_2,GCF_000000002.1
species_3,GCF_000000003.1

This is what the complete samplesheet would look like if using NCBI accessions, and adding FASTQ paths and tax IDs:

species,fasta,gff,fastq,taxid
species_1,/path/to/genome.fasta,/path/to/annotation.gxf,/path/to/reads.fastq,1234
species_2,/path/to/genome.fasta,/path/to/annotation.gxf,/path/to/reads.fastq,1245
species_3,/path/to/genome.fasta,/path/to/annotation.gxf,/path/to/reads.fastq,4321

This is what the complete samplesheet would look like if using local files, and adding FASTQ paths and tax IDs:

species,ncbi,fastq,taxid
species_1,GCF_000000001.1,/path/to/reads.fastq,1234
species_2,GCF_000000002.1,/path/to/reads.fastq,1245
species_3,GCF_000000003.1,/path/to/reads.fastq,4321

You can mix different input types in the same samplesheet. If a specific field doesn’t apply to a row, leave it empty (as shown below). The pipeline will automatically detect the input type for each row and run the appropiate subworkflow:

species,ncbi,fasta,gff,fastq,taxid
species_1,,/path/to/genome.fasta,/path/to/annotation.gxf,/path/to/reads.fastq,1234
species_2,,/path/to/genome.fasta,/path/to/annotation.gxf,,4321
species_3,,/path/to/genome.fasta,/path/to/annotation.gxf,
species_4,,/path/to/genome.fasta,,/path/to/reads.fastq,1245
species_5,,/path/to/genome.fasta,,
species_6,,/path/to/genome.fassta,,
species_7,GCF_000000007.1,,,/path/to/reads.fastq
species_8,GCF_000000008.1,,,
species_9,GCA_000000009.1,,,/path/to/reads.fastq
species_10,GCA_000000010.1,,,,1324

As for now, the pipeline doesn’t support SRA accession for Merqury. We will consider this option the future.

An example samplesheet is provided with the pipeline.

Running the pipeline

The typical command for running the pipeline is as follows:

nextflow run nf-core/genomeqc --input ./samplesheet.csv --outdir ./results -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/genomeqc -profile docker -params-file params.yaml

with:

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

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

Modes

Genome only

This is the minimal run. The pipeline will run on genome only mode if these inputs are provided in the samplesheet:

1. Path to fasta OR
2. ncbi GenaBank accession.

The pipeline will produce a tree plot summary that can be modified in real time using a packaged shiny app, as well as a MultiQC report with quality statistics.

Genome and annotation

The pipeline will run on genome and annotation mode if these inputs are provided in the samplesheet:

1. Path to fasta AND
2. Path to gff OR
3. ncbi RefSeq accession.

The pipeline will produce a tree plot summary that can be modified in real time using a packaged shiny app, as well as a MultiQC report with quality statistics.

Running tidk

The pipeline will run tidk by default. Users can provide a DNA string correspoding to the telomeric repeat of the assemblies of interest using the --repeat flag. If no DNA string is provided, tidk will explore the genome and try to compute the telomeric repeat in its cannonical form. E.g.:

nextflow run nf-core/genomeqc \
  --input ./samplesheet.csv \
  --outdir ./results \
  --repeat ATCG
  -profile docker

Users can skip tidk using the --skip_tidk flag.

Running Merqury

Users can also run the pipeline using Merqury by supplying the path to sequencing reads under the fastq field. Merqury needs both fasta and fastq to run. Refer the GitHub page for more information on Merqury.

The k-mer size used to build the Meryl database can be tuned with --kvalue (default: 21). For highly heterozygous or large genomes, a larger k (e.g. 31) may give better completeness estimates.

Running Decontamination

If an NCBI taxid is provided for an assembly in the samplesheet through the taxid field, and the path to the FCS-GX database or a manifest to download and build it is given via --gxdb or --gxdb_manifiest respectively, the pipeline will run the decontamination subworkflow. E.g.:

nextflow run nf-core/genomeqc \
  --input ./samplesheet.csv \
  --outdir ./results \
  --gxdb /patho/to/fcs-gx/all
  -profile docker

or

nextflow run nf-core/genomeqc \
  --input ./samplesheet.csv \
  --outdir ./results \
  --gxdb_manifest 'https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/FCS/database/latest/latest.manifest'
  -profile docker

The decontamination subsworkflow consists of three modules:

• FCS-GX: Detection and removal of foreign organisms contamination. Requires the FCS-GX database.
• FCS-adaptor: Detection and removal of adaptor and vector contamination.
• Tiara: For DNA sequence classification in two stages:
  1. The sequences are classified to either archaea, bacteria, prokarya, eukarya, organelle or unknown.
  2. The sequences labeled as organelle in the first stage are classified to either mitochondria, plastid or unknown.

Running TE annotation

The pipeline supports two optional methods for transposable element (TE) annotation, selected with the --te parameter. TE annotation is skipped by default.

--te hite

Runs HiTE, a fast alignment-free TE identification and masking tool. It is the recommended option for quick runs or plant genomes.

nextflow run nf-core/genomeqc \
   --input samplesheet.csv \
   --outdir results \
   --te hite \
   -profile docker

For plant genomes, also pass --is_plant true:

nextflow run nf-core/genomeqc \
   --input samplesheet.csv \
   --outdir results \
   --te hite \
   --is_plant true \
   -profile docker

--te repeatmasker

Runs a curated TE masking pipeline using the DFAM repeat library:

1. famdb.py – extracts a curated repeat library from DFAM h5 partition files (downloaded automatically by default).
2. Clustering – deduplicates the library using MMseqs2 easy-linclust by default (see Clustering options below).
3. RepeatMasker – masks the genome. Runs in rush mode (-qq) by default for speed (see RepeatMasker speed below).

nextflow run nf-core/genomeqc \
   --input samplesheet.csv \
   --outdir results \
   --te repeatmasker \
   -profile docker

To restrict the curated library to a specific taxonomic lineage (strongly recommended — speeds up both the extraction and the masking step), use --famdb_lineage:

nextflow run nf-core/genomeqc \
   --input samplesheet.csv \
   --outdir results \
   --te repeatmasker \
   --famdb_lineage hymenoptera \
   -profile docker

By default, the pipeline downloads partition 0 of the DFAM full database. To download additional partitions (required for full taxonomic coverage beyond the root lineage), pass them via --RM_db:

--RM_db "['https://www.dfam.org/releases/current/families/FamDB/dfam39_full.0.h5.gz',\
           'https://www.dfam.org/releases/current/families/FamDB/dfam39_full.1.h5.gz']"

If you already have DFAM h5 partition files on disk, use --famdb_library to point at them. The download step is skipped automatically.

--famdb_library expects decompressed .h5 files — .h5.gz files must be extracted first. Only decompress the partitions relevant to your lineage; decompressing the entire database is unnecessary and expensive (each partition is 10–30 GB uncompressed).

Partition 0 always contains the taxonomy metadata and is required. For most lineages, partitions 0 and 1 are sufficient — check the DFAM partition guide if you need to identify which partition covers your clade.

# Decompress only the partitions you need (once, on the cluster)
gunzip -k /path/to/dfam38-1_full.0.h5.gz
gunzip -k /path/to/dfam38-1_full.1.h5.gz

Then pass them via a glob:

nextflow run nf-core/genomeqc \
   --input samplesheet.csv \
   --outdir results \
   --te repeatmasker \
   --famdb_library "/path/to/dfam38-1_full.*.h5" \
   --famdb_lineage hymenoptera \
   -profile docker

Adding de novo repeat discovery with RepeatModeler

By default, --te repeatmasker only uses the curated DFAM library. To also run RepeatModeler for de novo discovery, add --run_repeatmodeler:

nextflow run nf-core/genomeqc \
   --input samplesheet.csv \
   --outdir results \
   --te repeatmasker \
   --run_repeatmodeler \
   --famdb_lineage hymenoptera \
   -profile docker

The RepeatModeler de novo library is merged with the famdb curated library before masking, giving broader coverage at the cost of significant runtime.

Repeat library clustering options

Before RepeatMasker runs, the repeat library is deduplicated by a clustering step. Three tools are available via --te_clusterer:

Two thresholds can be tuned:

• --te_cluster_identity – minimum sequence identity (default 0.8). Passed as -c to CD-HIT-EST and --min-seq-id to MMseqs2.
• --te_cluster_coverage – minimum alignment coverage of the shorter sequence (default 0.8). Passed as -aS to CD-HIT-EST and -c --cov-mode 1 to MMseqs2.

RepeatMasker speed

RepeatMasker sensitivity can be controlled with --repeatmasker_speed:

For TE quantification in a comparative genomics context, qq is usually sufficient. Use default if you need a publication-quality masked assembly.

The Shiny App

The pipeline outputs an executable that will open a shiny app in your web browser once executed. The app allows the user to change the tree plot parameters in real time, as well as append and remove summary plot statistics. The modified plot can be saved as a png/svg file.

To open the shiny app, do:

bash ./results_folder/shiny/app/shiny_app.sh

You will need docker to run the shiny app, as it comes packaged in a docker container.

Running tests

The pipeline can be ran using different test profiles:

1. -profile test — Runs genome and annotation with Merqury using RefSeq accessions and local fastqs.
2. -profile test_local — Runs genome and annotation on local files (fasta and gff).
3. -profile test_genomeonly — Runs genome only on local files (fasta).
4. -profile test_nofastq — Runs genome and annotation using RefSeq accessions (no fastq/Merqury).
5. -profile test_decon — Tests the decontamination subworkflow (FCS-GX, FCS-Adaptor, Tiara) using the NCBI FCS test-only database.
6. -profile test_te — Tests TE annotation with --te repeatmasker and --run_repeatmodeler on a minimal genome.
7. -profile test_full — Runs the full pipeline on a set of Hymenoptera genomes.

Test files are stored in the genomeqc branch of the test-dataset repository.

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/genomeqc

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 nf-core/genomeqc 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.

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 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'