nf-core/pathogensurveillance
Surveillance of pathogens using population genomics and sequencing
Introduction
nf-core/pathogensurveillance is a population genomic pipeline for pathogen diagnosis, variant detection, and biosurveillance. The pipeline accepts the paths to raw reads for one or more organisms (in the form of a CSV file) and creates reports in the form of interactive HTML reports or PDF documents. Significant features include the ability to analyze unidentified eukaryotic and prokaryotic samples, creation of reports for multiple user-defined groupings of samples, automated discovery and downloading of reference assemblies from NCBI RefSeq, and rapid initial identification based on k-mer sketches followed by a more robust core genome phylogeny and SNP-based phylogeny.
The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world data sets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources.The results obtained from the full-sized test can be viewed on the nf-core website.
Pipeline summary
Quick Start
-
Install
Nextflow
(>=21.10.3
) -
Install any of
Docker
,Singularity
(you can follow this tutorial),Podman
,Shifter
orCharliecloud
for full pipeline reproducibility (you can useConda
both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs). -
Download the pipeline and test it on a minimal dataset with a single command:
nextflow run nf-core/pathogensurveillance -r dev -profile RUN_TOOL,xanthomonas_small -resume --out_dir test_output
Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (
RUN_TOOL
in the example command above). You can chain multiple config profiles in a comma-separated string.- The pipeline comes with config profiles called
docker
,singularity
,podman
,shifter
,charliecloud
andconda
which instruct the pipeline to use the named tool for software management. For example,-profile xanthomonas_small,docker
. - Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use
-profile <institute>
in your command. This will enable eitherdocker
orsingularity
and set the appropriate execution settings for your local compute environment. - If you are using
singularity
, please use thenf-core download
command to download images first, before running the pipeline. Setting theNXF_SINGULARITY_CACHEDIR
orsingularity.cacheDir
Nextflow options enables you to store and re-use the images from a central location for future pipeline runs. - If you are using
conda
, it is highly recommended to use theNXF_CONDA_CACHEDIR
orconda.cacheDir
settings to store the environments in a central location for future pipeline runs.
- The pipeline comes with config profiles called
-
Start running your own analysis!
nextflow run nf-core/pathogensurveillance -r dev -profile RUN_TOOL -resume --sample_data <CSV> --out_dir <OUTDIR> --download_bakta_db
Documentation
Documentation is currently under development, but can be found here:
https://grunwaldlab.github.io/pathogensurveillance_documentation
Input format
The primary input to the pipeline is a CSV (comma comma-separated value) file, specified using the --sample_data
option.
This can be made in a spreadsheet program like LibreOffice Calc or Microsoft Excel by exporting to CSV.
Columns can be in any order and unneeded columns can be left out or left blank.
Column names are case insensitive and spaces are equivalent to underscores.
Only a single column containing either paths to raw sequence data, SRA (Sequence Read Archive) accessions, or NCBI queries to search the SRA is required and each sample can have values in different columns.
Any columns not recognized by pathogensurveillance
will be ignored, allowing users to adapt existing sample metadata table by adding new columns.
Below is a description of each column used by pathogensurveillance
:
- sample_id: The unique identifier for each sample. This will be used in file names to distinguish samples in the output. Each sample ID must correspond to a single source of sequence data (e.g. the
path
andncbi_accession
columns), although the same sequence data can be used by different IDs. Any values supplied that correspond to different sources of sequence data or contain characters that cannot appear in file names (/:*?”<>| .) will be modified automatically. If not supplied, it will be inferred from thepath
,ncbi_accession
, orname
columns. - name: A human-readable label for the sample that is used in plots and tables. If not supplied, it will be inferred from
sample_id
. - path: Path to input sequence data, typically gzipped FASTQ files. When paired end sequencing is used, this is used for the forward read’s data and
path_2
is used for the reverse reads. This can be a local file path or a URL to an online location. Thesequence_type
column must have a value. - path_2: Path to the FASTQ files for the reverse read when paired-end sequencing is used. This can be a local file path or a URL to an online location. The
sequence_type
column must have a value. - ncbi_accession: An SRA accession ID for reads to be downloaded and used as samples. Values in the
sequence_type
column will be looked up if not supplied. - ncbi_query: A valid NCBI search query to search the SRA for reads to download and use as samples. This will result in an unknown number of samples being analyzed. The total number downloaded is limited by the
ncbi_query_max
column. Values in thesample_id
,name
, anddescription
columns will be append to that supplied by the user. Values in thesequence_type
column will be looked up and does not need to be supplied by the user. - ncbi_query_max: The maximum number or percentage of samples downloaded for the corresponding query in the
ncbi_query
column. Adding a%
to the end of a number indicates a percentage of the total number of results instead of a count. A random of subset of results will be downloaded ifncbi_query_max
is less than “100%” or the total number of results. - sequence_type: The type of sequencing used to produce reads for the
reads_1
andreads_2
columns. Valid values include anything containing the words “illumina”, “nanopore”, or “pacbio”. Will be looked up automatically forncbi_accession
andncbi_query
inputs but must be supplied by the user forpath
inputs. - report_group_ids: How to group samples into reports. For every unique value in this column a report will be generated. Samples can be assigned to multiple reports by separating group IDs by ”;“. For example
all;subset
will put the sample in bothall
andsubset
report groups. Samples will be added to a default group if this is not supplied. - color_by: The names of other columns that contain values used to color samples in plots and figures in the report. Multiple column names can be separated by ”;“. Specified columns can contain either categorical factors or specific colors, specified as a hex code. By default, samples will be one color and references another.
- ploidy: The ploidy of the sample. Should be a number. Defaults to “1”.
- enabled: Either “TRUE” or “FALSE”, indicating whether the sample should be included in the analysis or not. Defaults to “TRUE”.
- ref_group_ids: One or more reference group IDs separated by ”;“. These are used to supply specific references to specific samples. These IDs correspond to IDs listed in the
ref_group_ids
orref_id
columns of the reference metadata CSV.
Additionally, users can supply a reference metadata CSV that can be used to assign custom references to particular samples using the --reference_data
option.
If not provided, the pipeline will download and choose references to use automatically.
References are assigned to samples if they share a reference group ID in the ref_group_ids
columns that can appear in both input CSVs.
The reference metadata CSV or the sample metadata CSV can have the following columns:
- ref_group_ids: One or more reference group IDs separated by ”;“. These are used to group references and supply an ID that can be used in the
ref_group_ids
column of the sample metadata CSV to assign references to particular samples. * ref_id: The unique identifier for each user-defined reference genome. This will be used in file names to distinguish samples in the output. Each reference ID must correspond to a single source of reference data (Theref_path
,ref_ncbi_accession
, andref_ncbi_query
columns), although the same reference data can be used by multiple IDs. Any values that correspond to different sources of reference data or contain characters that cannot appear in file names (/:*?”<>| .) will be modified automatically. If not supplied, it will be inferred from thepath
,ref_name
columns or supplied automatically whenref_ncbi_accession
orref_ncbi_query
are used. - ref_id: The unique identify for each reference input. This will be used in file names to distinguish references in the output. Each sample ID must correspond to a single source of reference data (e.g. the
ref_path
andref_ncbi_accession
columns), although the same sequence data can be used by different IDs. Any values supplied that correspond to different sources of reference data or contain characters that cannot appear in file names (/:*?”<>| .) will be modified automatically. If not supplied, it will be inferred from theref_path
,ref_ncbi_accession
, orref_name
columns. - ref_name: A human-readable label for user-defined reference genomes that is used in plots and tables. If not supplied, it will be inferred from
ref_id
. It will be supplied automatically when theref_ncbi_query
column is used. - ref_description: A longer human-readable label for user-defined reference genomes that is used in plots and tables. If not supplied, it will be inferred from
ref_name
. It will be supplied automatically when theref_ncbi_query
column is used. - ref_path: Path to user-defined reference genomes for each sample. This can be a local file path or a URL to an online location.
- ref_ncbi_accession: RefSeq accession ID for a user-defined reference genome. These will be automatically downloaded and used as input.
- ref_ncbi_query: A valid NCBI search query to search the assembly database for genomes to download and use as references. This will result in an unknown number of references being downloaded. The total number downloaded is limited by the
ref_ncbi_query_max
column. Values in theref_id
,ref_name
, andref_description
columns will be append to that supplied by the user. - ref_ncbi_query_max: The maximum number or percentage of references downloaded for the corresponding query in the
ref_ncbi_query
column. Adding a%
to the end of a number indicates a percentage of the total number of results instead of a count. A random of subset of results will be downloaded ifncbi_query_max
is less than “100%” or the total number of results. - ref_primary_usage: Controls how the reference is used in the analysis in cases where a single “best” reference is required, such as for variant calling. Can be one of “optional” (can be used if selected by the analysis), “required” (will always be used), “exclusive” (only those marked “exclusive” will be used), or “excluded” (will not be used).
- ref_contextual_usage: Controls how the reference is used in the analysis in cases where multiple references are required to provide context for the samples, such as for phylogeny. Can be one of “optional” (can be used if selected by the analysis), “required” (will always be used), “exclusive” (only those marked “exclusive” will be used), or “excluded” (will not be used).
- ref_color_by: The names of other columns that contain values used to color references in plots and figures in the report. Multiple column names can be separated by ”;“. Specified columns can contain either categorical factors or specific colors, specified as a hex code. By default, samples will be one color and references another.
- ref_enabled: Either “TRUE” or “FALSE”, indicating whether the reference should be included in the analysis or not. Defaults to “TRUE”.
Credits
nf-core/pathogensurveillance was originally written by Zachary S.L. Foster, Camilo Parada-Rojas, Martha Sudermann, Nicholas C. Cauldron, Fernanda I. Bocardo, Ricardo Alcalá-Briseño, Hung Phan, Jeff H. Chang, Niklaus J. Grünwald.
We thank the following people for their extensive assistance in the development of this pipeline:
Contributions and Support
If you would like to contribute to this pipeline, please see the contributing guidelines.
For further information or help, don’t hesitate to get in touch on the Slack #pathogensurveillance
channel (you can join with this invite).
Citations
An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md
file.
You can cite the nf-core
publication as follows:
The nf-core framework for community-curated bioinformatics pipelines.
Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.