Freyja Workflow Series

Quick Facts

Workflow Type	Applicable Kingdom	Last Known Changes	Command-line Compatibility	Workflow Level
Genomic Characterization	SARS-CoV-2, Viral	vX.X.X	Yes	Sample-level, Set-level

Freyja Overview

Freyja is a tool for analysing viral mixed sample genomic sequencing data. Developed by Joshua Levy from the Andersen Lab, it performs two main steps:

1. Variant Frequency Estimation: Freyja calculates the frequencies of single nucleotide variants (SNVs) in the genomic sequencing data.
2. Depth-Weighted Demixing: It separates mixed populations of viral subtypes using a depth-weighted statistical approach, estimating the proportional abundance of each subtype in the sample based on the frequencies of subtype-defining variants.

Additional post-processing steps can produce visualizations of aggregated samples.

Wastewater and more

The typical use case of Freyja is to analyze mixed SARS-CoV-2 samples from a sequencing dataset, most often wastewater, but the tool is not limited to this context. With the appropriate reference genomes and barcode files, Freyja can be adapted for other pathogens, including MPXV, Influenza, RSV, and Measles.

Default Values

The defaults included in the Freyja workflows reflect this use case but can be adjusted for other pathogens. See the Running Freyja on other pathogens section for more information. Please be aware this is an experimental feature and we cannot guarantee complete functionality at this time.

Figure 1: Workflow diagram for Freyja Suite of workflows

Figure 1

Depending on the type of data (Illumina or Oxford Nanopore), the Read QC and Filtering steps, as well as the Read Alignment steps use different software. The user can specify if the barcodes and lineages file should be updated with freyja update before running Freyja or if bootstrapping is to be performed with freyja boot.

Four workflows have been created that perform different parts of Freyja:

• Freyja_Update_PHB
• Freyja_FASTQ_PHB
• Freyja_Plot_PHB
• Freyja_Dashboard_PHB

The main workflow is Freyja_FASTQ_PHB (Figure 1). Depending on the type of input data (Illumina paired-end, Illumina single-end or ONT), it runs various QC modules before aligning the sample with either BWA (Illumina) or minimap2 (ONT) to the provided reference file, followed by iVar for primer trimming. After the preprocessing is completed, Freyja is run to generate relative lineage abundances (demix) from the sample. Optional bootstrapping may be performed.

Data Compatability

The Freyja_FASTQ_PHB workflow is compatible with the following input data types:

- Ilumina Single-End
- Illumina Paired-End
- Oxford Nanopore

Freyja_Update_PHB will copy the SARS-CoV-2 reference files that can then be used as input for the Freyja_FASTQ_PHB workflow.

Two options are available to visualize the Freyja results: Freyja_Plot_PHB and Freyja_Dashboard_PHB. Freyja_Plot_PHB aggregates multiple samples using output from Freyja_FASTQ_PHB to generate a plot that shows fractional abundance estimates for all samples. including the option to plot sample collection date information. Alternatively, Freyja_Dashboard_PHB aggregates multiple samples using output from Freyja_FASTQ_PHB to generate an interactive visualization. This workflow requires an additional input field called viral load, which is the number of viral copies per liter.

Freyja, Sequencing Platforms and Data Quality

The choice of sequencing platform and the quality of the data directly influence Freyja's performance. High-accuracy platforms like Illumina provide reliable SNV detection, enhancing the precision of lineage abundance estimates. In contrast, platforms with higher error rates, such as Nanopore, whilst it has improved greatly in the recent years, may introduce uncertainties in variant calling, affecting the deconvolution process. Sequencing depth requirements will increase as the quality of the sequencing data decreases. A rational target depth is 100X coverage for sequencing data with Q-scores in the range of 25-30.

Additionally, inadequate sequencing depth can hinder Freyja's ability to differentiate between lineages, leading to potential misestimations. Sequencing depth requirements will increase with the complexity of the sample composition and the diversity of lineages present. For samples containing multiple closely related lineages, higher sequencing depth is necessary to resolve subtle differences in genetic variation and accurately estimate lineage abundances. This is particularly important for pathogens with high mutation rates or a large number of cocirculating lineages, such as influenza, where distinguishing between lineages relies on detecting specific single nucleotide variants (SNVs) with high confidence.

Freyja Workflows

Freyja_Update_PHB

This workflow will copy the SARS-CoV-2 reference files (curated_lineages.json and usher_barcodes.feather) from the source repository to a user-specific Google Cloud Storage (GCP) location (often a Terra.bio workspace-associated bucket). These files can then be used as input for the Freyja_FASTQ_PHB workflow.

Warning

This workflow is compatible only with SARS-CoV-2 reference files! To download reference files for other organisms please see the following repository: Freyja Barcodes.

More information is available in the Running Freyja on other pathogens section.

Inputs

We recommend running this workflow with "Run inputs defined by file paths" selected since no information from a Terra data table is actually being used. We also recommend turning off call caching so new information is retrieved every time.

Terra Task Name	Variable	Type	Description	Default Value	Terra Status
freyja_update	gcp_uri	String	The path where you want the Freyja reference files to be stored. Include gs:// at the beginning of the string. Full example with a Terra workspace bucket: "gs://fc-87ddd67a-c674-45a8-9651-f91e3d2f6bb7"		Required
freyja_update_refs	cpu	Int	Number of CPUs to allocate to the task	1	Optional
freyja_update_refs	disk_size	Int	Amount of storage (in GB) to allocate to the task	25	Optional
freyja_update_refs	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.3	Optional
freyja_update_refs	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	10	Optional
transfer_files	cpu	Int	Number of CPUs to allocate to the task	4	Optional
transfer_files	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
transfer_files	docker_image	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/theiagen/utility:1.1	Optional
transfer_files	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional

Outputs

This workflow does not produce any outputs that appear in a Terra data table. The reference files will appear at the location specified with the gcp_uri input variable.

Freyja_FASTQ_PHB

Freyja measures SNV frequency and sequencing depth at each position in the genome to return an estimate of the true lineage abundances in the sample. The method uses lineage-defining "barcodes" that, for SARS-CoV-2, are derived from the UShER global phylogenetic tree as a base set for demixing. Freyja_FASTQ_PHB returns as output a TSV file that includes the lineages present and their corresponding abundances, along with other values.

The Freyja_FASTQ_PHB workflow is compatible with the multiple input data types: Ilumina Single-End, Illumina Paired-End and Oxford Nanopore. Depending on the type of input data, different input values are used.

Table 1: Freyja_FASTQ_PHB input configuration for different types of input data.

Table Columns	Illumina Paired-End	Illumina Single-End	Oxford Nanopore
read1	✅	✅	✅
read2	✅	❌	❌
ont	false	false	true

Inputs

This workflow runs on the sample level.

Illumina paired-end input dataIllumina single-end input dataONT input data

Terra Task Name	Variable	Type	Description	Default Value	Terra Status
freyja_fastq	read1	File	FASTQ file containing read1 sequences (Illumina or (ONT)		Required
freyja_fastq	reference_genome	File	The reference genome to use; should match the reference used for alignment (Wuhan-Hu-1)		Required
freyja_fastq	samplename	String	The name of the sample being analyzed		Required
freyja_fastq	freyja_lineage_metadata	File	File containing the lineage metadata; the "curated_lineages.json" file found https://github.com/andersen-lab/Freyja/tree/main/freyja/data can be used for this variable. Does not need to be provided if update_db is true or if the freyja_pathogen is provided.		Optional, Required
freyja	bootstrap	Boolean	Perform bootstrapping	FALSE	Optional
freyja	confirmed_only	Boolean	Include only confirmed SARS-CoV-2 lineages	FALSE	Optional
freyja	cpu	Int	Number of CPUs to allocate to the task	2	Optional
freyja	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
freyja	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.3	Optional
freyja	eps	Float	The minimum lineage abundance cut-off value	0.001	Optional
freyja	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	4	Optional
freyja	number_bootstraps	Int	The number of bootstraps to perform (only used if bootstrap = true)	100	Optional
freyja	update_db	Boolean	Updates the Freyja reference files (the usher barcodes and lineage metadata files) but will not save them as output (use Freyja_Update for that purpose). If set to true, the freyja_lineage_metadata and freyja_barcodes files are not required.	FALSE	Optional
freyja_fastq	depth_cutoff	Int	The minimum coverage depth with which to exclude sites below this value and group identical barcodes -- THIS MAY NOT WORK FOR NON-SARS-COV-2 ORGANISMS!	10	Optional
freyja_fastq	freyja_barcodes	String	Custom barcode file. Does not need to be provided if update_db is true if the freyja_pathogen is provided.		Optional
freyja_fastq	freyja_pathogen	String	Pathogen of interest, used if not providing the barcodes and lineage metadata files. Options: SARS-CoV-2, MPXV, H5NX, H1N1pdm, FLU-B-VIC, MEASLESN450, MEASLES, RSVa, RSVb		Optional
freyja_fastq	kraken2_target_organism	String	The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database.	Severe acute respiratory syndrome coronavirus 2	Optional
freyja_fastq	ont	Boolean	Indicates if the input data is derived from an ONT instrument.	FALSE	Optional
freyja_fastq	primer_bed	File	The bed file containing the primers used when sequencing was performed		Optional
freyja_fastq	read2	File	Illumina reverse read file in FASTQ file format (compression optional)		Optional
freyja_fastq	reference_gff	File	The GFF file for reference; should match the reference used for alignment (Wuhan-Hu-1)		Optional
freyja_fastq	trimmomatic_minlen	Int	The minimum length cut-off when performing read cleaning	25	Optional
get_fasta_genome_size	cpu	Int	Number of CPUs to allocate to the task	1	Optional
get_fasta_genome_size	disk_size	Int	Amount of storage (in GB) to allocate to the task	10	Optional
get_fasta_genome_size	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/biocontainers/seqkit:2.4.0--h9ee0642_0	Optional
get_fasta_genome_size	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	2	Optional
primer_trim	cpu	Int	Number of CPUs to allocate to the task	2	Optional
primer_trim	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
primer_trim	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan	Optional
primer_trim	keep_noprimer_reads	Boolean	Include reads with no primers	TRUE	Optional
primer_trim	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
read_QC_trim_pe	adapters	File	A FASTA file containing adapter sequences		Optional
read_QC_trim_pe	bbduk_memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
read_QC_trim_pe	call_kraken	Boolean	True/False variable that determines if the Kraken2 task should be called; for non-TheiaCoV workflows, the kraken_db variable must be provided.	FALSE	Optional
read_QC_trim_pe	call_midas	Boolean	True/False variable that determines if the MIDAS task should be called.	FALSE	Optional
read_QC_trim_pe	fastp_args	String	Additional arguments to use with fastp	--detect_adapter_for_pe -g -5 20 -3 20	Optional
read_QC_trim_pe	kraken_cpu	Int	Number of CPUs to allocate to the task	4	Optional
read_QC_trim_pe	kraken_db	File	A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database.		Optional
read_QC_trim_pe	kraken_disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
read_QC_trim_pe	kraken_memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
read_QC_trim_pe	midas_db	File	Database to use with MIDAS. Not required as one will be auto-selected when running the MIDAS task.		Optional
read_QC_trim_pe	phix	File	The file containing the phix sequence to be used during bbduk task		Optional
read_QC_trim_pe	read_processing	String	Options: "trimmomatic" or "fastp" to indicate which read trimming module to use	trimmomatic	Optional
read_QC_trim_pe	read_qc	String	Allows the user to decide between fastq_scan (default) and fastqc for the evaluation of read quality.	fastq_scan	Optional
read_QC_trim_pe	target_organism	String	The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database.		Optional
read_QC_trim_pe	trim_quality_trim_score	Int	The minimum quality score to keep during trimming	30	Optional
read_QC_trim_pe	trim_window_size	Int	The window size to use during trimming	4	Optional
read_QC_trim_pe	trimmomatic_args	String	Additional command-line arguments to use with trimmomatic		Optional

Terra Task Name	Variable	Type	Description	Default Value	Terra Status
freyja_fastq	read1	File	FASTQ file containing read1 sequences (Illumina or (ONT)		Required
freyja_fastq	reference_genome	File	The reference genome to use; should match the reference used for alignment (Wuhan-Hu-1)		Required
freyja_fastq	samplename	String	The name of the sample being analyzed		Required
freyja_fastq	freyja_lineage_metadata	File	File containing the lineage metadata; the "curated_lineages.json" file found https://github.com/andersen-lab/Freyja/tree/main/freyja/data can be used for this variable. Does not need to be provided if update_db is true or if the freyja_pathogen is provided.		Optional, Required
freyja	bootstrap	Boolean	Perform bootstrapping	FALSE	Optional
freyja	confirmed_only	Boolean	Include only confirmed SARS-CoV-2 lineages	FALSE	Optional
freyja	cpu	Int	Number of CPUs to allocate to the task	2	Optional
freyja	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
freyja	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.3	Optional
freyja	eps	Float	The minimum lineage abundance cut-off value	0.001	Optional
freyja	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	4	Optional
freyja	number_bootstraps	Int	The number of bootstraps to perform (only used if bootstrap = true)	100	Optional
freyja	update_db	Boolean	Updates the Freyja reference files (the usher barcodes and lineage metadata files) but will not save them as output (use Freyja_Update for that purpose). If set to true, the freyja_lineage_metadata and freyja_barcodes files are not required.	FALSE	Optional
freyja_fastq	depth_cutoff	Int	The minimum coverage depth with which to exclude sites below this value and group identical barcodes -- THIS MAY NOT WORK FOR NON-SARS-COV-2 ORGANISMS!	10	Optional
freyja_fastq	freyja_barcodes	String	Custom barcode file. Does not need to be provided if update_db is true if the freyja_pathogen is provided.		Optional
freyja_fastq	freyja_pathogen	String	Pathogen of interest, used if not providing the barcodes and lineage metadata files. Options: SARS-CoV-2, MPXV, H5NX, H1N1pdm, FLU-B-VIC, MEASLESN450, MEASLES, RSVa, RSVb		Optional
freyja_fastq	kraken2_target_organism	String	The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database.	Severe acute respiratory syndrome coronavirus 2	Optional
freyja_fastq	ont	Boolean	Indicates if the input data is derived from an ONT instrument.	FALSE	Optional
freyja_fastq	primer_bed	File	The bed file containing the primers used when sequencing was performed		Optional
freyja_fastq	read2	File	Illumina reverse read file in FASTQ file format (compression optional)		Optional
freyja_fastq	reference_gff	File	The GFF file for reference; should match the reference used for alignment (Wuhan-Hu-1)		Optional
freyja_fastq	trimmomatic_minlen	Int	The minimum length cut-off when performing read cleaning	25	Optional
get_fasta_genome_size	cpu	Int	Number of CPUs to allocate to the task	1	Optional
get_fasta_genome_size	disk_size	Int	Amount of storage (in GB) to allocate to the task	10	Optional
get_fasta_genome_size	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/biocontainers/seqkit:2.4.0--h9ee0642_0	Optional
get_fasta_genome_size	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	2	Optional
primer_trim	cpu	Int	Number of CPUs to allocate to the task	2	Optional
primer_trim	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
primer_trim	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan	Optional
primer_trim	keep_noprimer_reads	Boolean	Include reads with no primers	TRUE	Optional
primer_trim	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
read_QC_trim_se	adapters	File	A FASTA file containing adapter sequences		Optional
read_QC_trim_se	bbduk_memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
read_QC_trim_se	call_kraken	Boolean	True/False variable that determines if the Kraken2 task should be called; for non-TheiaCoV workflows, the kraken_db variable must be provided.	FALSE	Optional
read_QC_trim_se	call_midas	Boolean	True/False variable that determines if the MIDAS task should be called.	FALSE	Optional
read_QC_trim_se	fastp_args	String	Additional arguments to use with fastp	--detect_adapter_for_pe -g -5 20 -3 20	Optional
read_QC_trim_se	kraken_cpu	Int	Number of CPUs to allocate to the task	4	Optional
read_QC_trim_se	kraken_db	File	A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database.		Optional
read_QC_trim_se	kraken_disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
read_QC_trim_se	kraken_memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
read_QC_trim_se	midas_db	File	Database to use with MIDAS. Not required as one will be auto-selected when running the MIDAS task.		Optional
read_QC_trim_se	phix	File	The file containing the phix sequence to be used during bbduk task		Optional
read_QC_trim_se	read_processing	String	Options: "trimmomatic" or "fastp" to indicate which read trimming module to use	trimmomatic	Optional
read_QC_trim_se	read_qc	String	Allows the user to decide between fastq_scan (default) and fastqc for the evaluation of read quality.	fastq_scan	Optional
read_QC_trim_se	target_organism	String	The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database.		Optional
read_QC_trim_se	trim_quality_min_score	Int	The minimum quality score to keep during trimming	30	Optional
read_QC_trim_se	trim_window_size	Int	The window size to use during trimming	4	Optional
read_QC_trim_se	trimmomatic_args	String	Additional command-line arguments to use with trimmomatic		Optional

Terra Task Name	Variable	Type	Description	Default Value	Terra Status
freyja_fastq	read1	File	FASTQ file containing read1 sequences (Illumina or (ONT)		Required
freyja_fastq	reference_genome	File	The reference genome to use; should match the reference used for alignment (Wuhan-Hu-1)		Required
freyja_fastq	samplename	String	The name of the sample being analyzed		Required
freyja	bootstrap	Boolean	Perform bootstrapping	FALSE	Optional
freyja	confirmed_only	Boolean	Include only confirmed SARS-CoV-2 lineages	FALSE	Optional
freyja	cpu	Int	Number of CPUs to allocate to the task	2	Optional
freyja	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
freyja	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.3	Optional
freyja	eps	Float	The minimum lineage abundance cut-off value	0.001	Optional
freyja	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	4	Optional
freyja	number_bootstraps	Int	The number of bootstraps to perform (only used if bootstrap = true)	100	Optional
freyja	update_db	Boolean	Updates the Freyja reference files (the usher barcodes and lineage metadata files) but will not save them as output (use Freyja_Update for that purpose). If set to true, the freyja_lineage_metadata and freyja_barcodes files are not required.	FALSE	Optional
freyja_fastq	depth_cutoff	Int	The minimum coverage depth with which to exclude sites below this value and group identical barcodes -- THIS MAY NOT WORK FOR NON-SARS-COV-2 ORGANISMS!	10	Optional
freyja_fastq	freyja_barcodes	String	Custom barcode file. Does not need to be provided if update_db is true if the freyja_pathogen is provided.		Optional
freyja_fastq	freyja_pathogen	String	Pathogen of interest, used if not providing the barcodes and lineage metadata files. Options: SARS-CoV-2, MPXV, H5NX, H1N1pdm, FLU-B-VIC, MEASLESN450, MEASLES, RSVa, RSVb		Optional
freyja_fastq	kraken2_target_organism	String	The organism whose abundance the user wants to check in their reads. This should be a proper taxonomic name recognized by the Kraken database.	Severe acute respiratory syndrome coronavirus 2	Optional
freyja_fastq	ont	Boolean	Indicates if the input data is derived from an ONT instrument.	FALSE	Optional
freyja_fastq	primer_bed	File	The bed file containing the primers used when sequencing was performed		Optional
freyja_fastq	read2	File	Illumina reverse read file in FASTQ file format (compression optional)		Optional
freyja_fastq	reference_gff	File	The GFF file for reference; should match the reference used for alignment (Wuhan-Hu-1)		Optional
freyja_fastq	trimmomatic_minlen	Int	The minimum length cut-off when performing read cleaning	25	Optional
get_fasta_genome_size	cpu	Int	Number of CPUs to allocate to the task	1	Optional
get_fasta_genome_size	disk_size	Int	Amount of storage (in GB) to allocate to the task	10	Optional
get_fasta_genome_size	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/biocontainers/seqkit:2.4.0--h9ee0642_0	Optional
get_fasta_genome_size	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	2	Optional
read_QC_trim_ont	call_kraken	Boolean	True/False variable that determines if the Kraken2 task should be called; for non-TheiaCoV workflows, the kraken_db variable must be provided.	FALSE	Optional
read_QC_trim_ont	downsampling_coverage	Float	The depth to downsample to with Rasusa. Internal component. Do not modify.	150	Optional
read_QC_trim_ont	genome_length	Int	Internal component, do not modify		Optional
read_QC_trim_ont	kraken_cpu	Int	Number of CPUs to allocate to the task	4	Optional
read_QC_trim_ont	kraken_db	File	A kraken2 database to use with the kraken2 optional task. The file must be a .tar.gz kraken2 database.		Optional
read_QC_trim_ont	kraken_disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
read_QC_trim_ont	kraken_memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
read_QC_trim_ont	max_length	Int	Internal component, do not modify		Optional
read_QC_trim_ont	kraken2_recalculate_abundances_cpu	Int	Number of CPUs to allocate to the task	4	Optional
read_QC_trim_ont	kraken2_recalculate_abundances_disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
read_QC_trim_ont	kraken2_recalculate_abundances_docker	Int	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/theiagen/terra-tools:2023-08-28-v4	Optional
read_QC_trim_ont	kraken2_recalculate_abundances_memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
read_QC_trim_ont	min_length	Int	Internal component, do not modify		Optional
read_QC_trim_ont	run_prefix	String	Internal component, do not modify		Optional
read_QC_trim_ont	target_organism	String	This string is searched for in the kraken2 outputs to extract the read percentage		Optional

Analysis Tasks

Illumina paired-end input dataIllumina single-end input dataONT input data

read_QC_trim: Read Quality Trimming, Adapter Removal, Quantification, and Identification

read_QC_trim is a sub-workflow that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below. The differences between the PE and SE versions of the read_QC_trim sub-workflow lie in the default parameters, the use of two or one input read file(s), and the different output files.

HRRT: Human Host Sequence Removal

All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).

HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.

NCBI-Scrub Technical Details

	Links
Task	task_ncbi_scrub.wdl
Software Source Code	HRRT on GitHub
Software Documentation	HRRT on NCBI

Read quality trimming

Either trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.

If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:

Parameter	Explanation
-g	enables polyG tail trimming
-5 20	enables read end-trimming
-3 20	enables read end-trimming
--detect_adapter_for_pe	enables adapter-trimming only for paired-end reads

Adapter removal

The BBDuk task removes adapters from sequence reads. To do this:

• Repair from the BBTools package reorders reads in paired fastq files to ensure the forward and reverse reads of a pair are in the same position in the two fastq files.
• BBDuk ("Bestus Bioinformaticus" Decontamination Using Kmers) is then used to trim the adapters and filter out all reads that have a 31-mer match to PhiX, which is commonly added to Illumina sequencing runs to monitor and/or improve overall run quality.

What are adapters and why do they need to be removed?

Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.

Read Quantification

There are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. For paired-end data, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.

read_QC_trim Technical Details

	Links
Sub-workflow	wf_read_QC_trim_pe.wdl wf_read_QC_trim_se.wdl
Tasks	task_fastp.wdl task_trimmomatic.wdl task_bbduk.wdl task_fastq_scan.wdl task_midas.wdl task_kraken2.wdl
Software Source Code	fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2
Software Documentation	fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2
Original Publication(s)	Trimmomatic: a flexible trimmer for Illumina sequence data fastp: an ultra-fast all-in-one FASTQ preprocessor An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography Improved metagenomic analysis with Kraken 2

bwa Details

This task aligns the cleaned short reads (Illumina) to the reference genome provided by the user.

BWA Technical Details

	Links
Task	task_bwa.wdl
Software Source Code	https://github.com/lh3/bwa
Software Documentation	https://bio-bwa.sourceforge.net/
Original Publication(s)	Fast and accurate short read alignment with Burrows-Wheeler transform

primer_trim Details

This task trims the primer sequences from the aligned bam file with iVar. The optional input, keep_noprimer_reads, does not have to be modified.

Primer Trim Technical Details

	Links
Task	task_ivar_primer_trim.wdl
Software Source Code	https://github.com/andersen-lab/ivar
Software Documentation	https://andersen-lab.github.io/ivar/html/manualpage.html
Original Publication(s)	An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar

read_QC_trim: Read Quality Trimming, Adapter Removal, Quantification, and Identification

read_QC_trim is a sub-workflow that removes low-quality reads, low-quality regions of reads, and sequencing adapters to improve data quality. It uses a number of tasks, described below. The differences between the PE and SE versions of the read_QC_trim sub-workflow lie in the default parameters, the use of two or one input read file(s), and the different output files.

HRRT: Human Host Sequence Removal

All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).

HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.

NCBI-Scrub Technical Details

	Links
Task	task_ncbi_scrub.wdl
Software Source Code	HRRT on GitHub
Software Documentation	HRRT on NCBI

Read quality trimming

Either trimmomatic or fastp can be used for read-quality trimming. Trimmomatic is used by default. Both tools trim low-quality regions of reads with a sliding window (with a window size of trim_window_size), cutting once the average quality within the window falls below trim_quality_trim_score. They will both discard the read if it is trimmed below trim_minlen.

If fastp is selected for analysis, fastp also implements the additional read-trimming steps indicated below:

Parameter	Explanation
-g	enables polyG tail trimming
-5 20	enables read end-trimming
-3 20	enables read end-trimming
--detect_adapter_for_pe	enables adapter-trimming only for paired-end reads

Adapter removal

The BBDuk task removes adapters from sequence reads. To do this:

• Repair from the BBTools package reorders reads in paired fastq files to ensure the forward and reverse reads of a pair are in the same position in the two fastq files.
• BBDuk ("Bestus Bioinformaticus" Decontamination Using Kmers) is then used to trim the adapters and filter out all reads that have a 31-mer match to PhiX, which is commonly added to Illumina sequencing runs to monitor and/or improve overall run quality.

What are adapters and why do they need to be removed?

Adapters are manufactured oligonucleotide sequences attached to DNA fragments during the library preparation process. In Illumina sequencing, these adapter sequences are required for attaching reads to flow cells. You can read more about Illumina adapters here. For genome analysis, it's important to remove these sequences since they're not actually from your sample. If you don't remove them, the downstream analysis may be affected.

Read Quantification

There are two methods for read quantification to choose from: fastq-scan (default) or fastqc. Both quantify the forward and reverse reads in FASTQ files. For paired-end data, they also provide the total number of read pairs. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads. fastqc also provides a graphical visualization of the read quality.

read_QC_trim Technical Details

	Links
Sub-workflow	wf_read_QC_trim_pe.wdl wf_read_QC_trim_se.wdl
Tasks	task_fastp.wdl task_trimmomatic.wdl task_bbduk.wdl task_fastq_scan.wdl task_midas.wdl task_kraken2.wdl
Software Source Code	fastp; Trimmomatic; fastq-scan; MIDAS; Kraken2
Software Documentation	fastp; Trimmomatic; BBDuk; fastq-scan; MIDAS; Kraken2
Original Publication(s)	Trimmomatic: a flexible trimmer for Illumina sequence data fastp: an ultra-fast all-in-one FASTQ preprocessor An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography Improved metagenomic analysis with Kraken 2

bwa Details

This task aligns the cleaned short reads (Illumina) to the reference genome provided by the user.

BWA Technical Details

	Links
Task	task_bwa.wdl
Software Source Code	https://github.com/lh3/bwa
Software Documentation	https://bio-bwa.sourceforge.net/
Original Publication(s)	Fast and accurate short read alignment with Burrows-Wheeler transform

primer_trim Details

This task trims the primer sequences from the aligned bam file with iVar. The optional input, keep_noprimer_reads, does not have to be modified.

Primer Trim Technical Details

	Links
Task	task_ivar_primer_trim.wdl
Software Source Code	https://github.com/andersen-lab/ivar
Software Documentation	https://andersen-lab.github.io/ivar/html/manualpage.html
Original Publication(s)	An amplicon-based sequencing framework for accurately measuring intrahost virus diversity using PrimalSeq and iVar

read_QC_trim_ont: Read Quality Trimming, Quantification, and Identification

read_QC_trim_ont is a sub-workflow that filters low-quality reads and trims low-quality regions of reads. It uses several tasks, described below.

HRRT: Human Host Sequence Removal

All reads of human origin are removed, including their mates, by using NCBI's human read removal tool (HRRT).

HRRT is based on the SRA Taxonomy Analysis Tool and employs a k-mer database constructed of k-mers from Eukaryota derived from all human RefSeq records with any k-mers found in non-Eukaryota RefSeq records subtracted from the database.

NCBI-Scrub Technical Details

	Links
Task	task_ncbi_scrub.wdl
Software Source Code	HRRT on GitHub
Software Documentation	HRRT on NCBI

Read quality filtering

Read filtering is performed using artic guppyplex which performs a quality check by filtering the reads by length to remove chimeric reads.

Read Identification with Kraken2

Kraken2 is a bioinformatics tool originally designed for metagenomic applications. It has additionally proven valuable for validating taxonomic assignments and checking contamination of single-species (e.g. bacterial isolate, eukaryotic isolate, viral isolate, etc.) whole genome sequence data.

Kraken2 is run on both the raw and clean reads.

Database-dependent

This workflow automatically uses a viral-specific Kraken2 database. This database was generated in-house from RefSeq's viral sequence collection and human genome GRCh38. It's available at gs://theiagen-large-public-files-rp/terra/databases/kraken2/kraken2_humanGRCh38_viralRefSeq_20240828.tar.gz.

Kraken2 Technical Details

	Links
Task	task_kraken2.wdl
Software Source Code	Kraken2 on GitHub
Software Documentation	https://github.com/DerrickWood/kraken2/blob/master/docs/MANUAL.markdown
Original Publication(s)	Improved metagenomic analysis with Kraken 2

nanoplot: Plotting and quantifying long-read sequencing data

Nanoplot is used for the determination of mean quality scores, read lengths, and number of reads. This task is run once with raw reads as input and once with clean reads as input. If QC has been performed correctly, you should expect fewer clean reads than raw reads.

read_QC_trim_ont Technical Details

	Links
Sub-workflow	wf_read_QC_trim_ont.wdl
Tasks	task_fastq_scan.wdl task_nanoplot.wdl task_rasusa.wdl task_nanoq.wdl
Software Source Code	fastq-scan NanoPlot RASUSA nanoq
Software Documentation	NCBI Scrub on GitHub NanoPlot documentation on GitHub Rasusa on GitHub Nanoq on GitHub Artic Pipeline ReadTheDocs Kraken2 on GitHub
Original Publication(s)	NanoPack2: population-scale evaluation of long-read sequencing data Rasusa: Randomly subsample sequencing reads to a specified coverage Nanoq: ultra-fast quality control for nanopore reads Improved metagenomic analysis with Kraken 2

minimap2: Read Alignment Details

minimap2 is a popular aligner that is used to align reads (or assemblies) to an assembly file. In minimap2, "modes" are a group of preset options.

The mode used in this task is map-ont which is the default mode for long reads and indicates that long reads of ~10% error rates should be aligned to the reference genome. The output file is in SAM format.

For more information regarding modes and the available options for minimap2, please see the minimap2 manpage

minimap2 Technical Details

	Links
Task	task_minimap2.wdl
Software Source Code	minimap2 on GitHub
Software Documentation	minimap2
Original Publication(s)	Minimap2: pairwise alignment for nucleotide sequences

freyja Details

The Freyja task will call variants and capture sequencing depth information to identify the relative abundance of lineages present. Optionally, if bootstrap is set to true, bootstrapping will be performed. After the optional bootstrapping step, the variants are demixed.

Freyja Technical Details

	Links
Task	task_freyja_one_sample.wdl
Software Source Code	https://github.com/andersen-lab/Freyja
Software Documentation	https://andersen-lab.github.io/Freyja/index.html#

Outputs

The main output file used in subsequent Freyja workflows is found under the freyja_demixed column. This TSV file takes on the following format:

	sample name
summarized	[('Delta', 0.65), ('Other', 0.25), ('Alpha', 0.1')]
lineages	['B.1.617.2' 'B.1.2' 'AY.6' 'Q.3']
abundances	"[0.5 0.25 0.15 0.1]"
resid	3.14159
coverage	95.8

• The summarized array denotes a sum of all lineage abundances in a particular WHO designation (i.e. B.1.617.2 and AY.6 abundances are summed in the above example), otherwise they are grouped into "Other".
• The lineage array lists the identified lineages in descending order
• The abundances array contains the corresponding abundances estimates.
• The value of resid corresponds to the residual of the weighted least absolute deviation problem used to estimate lineage abundances.
• The coverage value provides the 10x coverage estimate (percent of sites with 10 or greater reads)

Click "Ignore empty outputs"

When running the Freyja_FASTQ_PHB workflow, it is recommended to select the "Ignore empty outputs" option in the Terra UI. This will hide the output columns that will not be generated for your input data type.

Illumina paired-end input dataIllumina single-end input dataONT input data

Variable	Type	Description
aligned_bai	File	Index companion file to the bam file generated during the consensus assembly process
aligned_bam	File	Sorted BAM file containing the alignments of reads to the reference genome
alignment_method	String	The method used to generate the alignment
bbduk_docker	String	The Docker image for bbduk, which was used to remove the adapters from the sequences
bwa_version	String	Version of BWA software used
fastp_html_report	File	The HTML report made with fastp
fastp_version	String	The version of fastp used
fastq_scan_clean1_json	File	The JSON file output from fastq-scan containing summary stats about clean forward read quality and length
fastq_scan_clean2_json	File	The JSON file output from fastq-scan containing summary stats about clean reverse read quality and length
fastq_scan_num_reads_clean_pairs	String	The number of read pairs after cleaning as calculated by fastq_scan
fastq_scan_num_reads_clean1	Int	The number of forward reads after cleaning as calculated by fastq_scan
fastq_scan_num_reads_clean2	Int	The number of reverse reads after cleaning as calculated by fastq_scan
fastq_scan_num_reads_raw_pairs	String	The number of input read pairs as calculated by fastq_scan
fastq_scan_num_reads_raw1	Int	The number of input forward reads as calculated by fastq_scan
fastq_scan_num_reads_raw2	Int	The number of input reserve reads as calculated by fastq_scan
fastq_scan_raw1_json	File	The JSON file output from fastq-scan containing summary stats about raw forward read quality and length
fastq_scan_raw2_json	File	The JSON file output from fastq-scan containing summary stats about raw reverse read quality and length
fastq_scan_version	String	The version of fastq_scan
fastqc_clean1_html	File	An HTML file that provides a graphical visualization of clean forward read quality from fastqc to open in an internet browser
fastqc_clean2_html	File	An HTML file that provides a graphical visualization of clean reverse read quality from fastqc to open in an internet browser
fastqc_docker	String	The Docker container used for fastqc
fastqc_num_reads_clean_pairs	String	The number of read pairs after cleaning by fastqc
fastqc_num_reads_clean1	Int	The number of forward reads after cleaning by fastqc
fastqc_num_reads_clean2	Int	The number of reverse reads after cleaning by fastqc
fastqc_num_reads_raw_pairs	String	The number of input read pairs by fastqc before cleaning
fastqc_num_reads_raw1	Int	The number of input forward reads by fastqc before cleaning
fastqc_num_reads_raw2	Int	The number of input reverse reads by fastqc before cleaning
fastqc_raw1_html	File	An HTML file that provides a graphical visualization of raw forward read quality from fastqc to open in an internet browser
fastqc_raw2_html	File	An HTML file that provides a graphical visualization of raw reverse read quality from fastqc to open in an internet browser
fastqc_version	String	Version of fastqc software used
freyja_abundances	String	Abundances estimates identified by Freyja and parsed from freyja_demixed file
freyja_barcode_version	String	Name of barcode file used, or the date if update_db is true
freyja_bootstrap_lineages	File	A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each lineage
freyja_bootstrap_lineages_pdf	File	A boxplot of the bootstrap lineages CSV file
freyja_bootstrap_summary	File	A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each WHO designated VOI/VOC
freyja_bootstrap_summary_pdf	File	A boxplot of the bootstrap summary CSV file
freyja_coverage	Float	Coverage identified by Freyja and parsed from freyja_demixed file
freyja_demixed	File	The main output TSV; see the section directly above this table for an explanation
freyja_demixed_parsed	File	Parsed freyja_demixed file, containing the same information, for easy result concatenation
freyja_depths	File	A TSV listing the depth of every position
freyja_fastq_wf_analysis_date	String	Date of analysis
freyja_fastq_wf_version	String	The version of the Public Health Bioinformatics (PHB) repository used
freyja_lineages	String	Lineages in descending order identified by Freyja and parsed from freyja_demixed file
freyja_metadata_version	String	Name of lineage metadata file used, or the date if update_db is true
freyja_resid	String	Residual of the weighted least absolute deviation problem used to estimate lineage abundances identified by Freyja and parsed from freyja_demixed file
freyja_sc2_barcode_file	File	Barcode feather file used for SARS-CoV-2. Can be the one provided as input or downloaded by Freyja if update_db is true
freyja_sc2_lineage_metadata_file	File	Lineage metadata JSON file used for SARS-CoV-2. Can be the one provided as input or downloaded by Freyja if update_db is true
freyja_summarized	String	Sum of all lineage abundances in a particular WHO designation identified by Freyja and parsed from freyja_demixed file
freyja_variants	File	The TSV file containing the variants identified by Freyja
freyja_version	String	version of Freyja used
ivar_version_primtrim	String	Version of iVar for running the iVar trim command
kraken_human	Float	Percent of human read data detected using the Kraken2 software
kraken_human_dehosted	Float	Percent of human read data detected using the Kraken2 software after host removal
kraken_report	File	Full Kraken report
kraken_report_dehosted	File	Full Kraken report after host removal
kraken_sc2	String	Percent of SARS-CoV-2 read data detected using the Kraken2 software
kraken_sc2_dehosted	Float	Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal
kraken_version	String	Version of Kraken software used
primer_bed_name	String	Name of the primer bed files used for primer trimming
primer_trimmed_read_percent	Float	Percentage of read data with primers trimmed as determined by iVar trim
read1_clean	File	Forward read file after quality trimming and adapter removal
read1_dehosted	File	The dehosted forward reads file; suggested read file for SRA submission
read2_clean	File	Reverse read file after quality trimming and adapter removal
read2_dehosted	File	The dehosted reverse reads file; suggested read file for SRA submission
samtools_version	String	The version of SAMtools used to sort and index the alignment file
samtools_version_primtrim	String	The version of SAMtools used to create the pileup before running iVar trim
trimmomatic_docker	String	The docker image used for the trimmomatic module in this workflow
trimmomatic_version	String	The version of Trimmomatic used

Variable	Type	Description
aligned_bai	File	Index companion file to the bam file generated during the consensus assembly process
aligned_bam	File	Sorted BAM file containing the alignments of reads to the reference genome
alignment_method	String	The method used to generate the alignment
bbduk_docker	String	The Docker image for bbduk, which was used to remove the adapters from the sequences
bwa_version	String	Version of BWA software used
fastp_html_report	File	The HTML report made with fastp
fastp_version	String	The version of fastp used
fastq_scan_clean1_json	File	The JSON file output from fastq-scan containing summary stats about clean forward read quality and length
fastq_scan_num_reads_clean1	Int	The number of forward reads after cleaning as calculated by fastq_scan
fastq_scan_num_reads_raw1	Int	The number of input forward reads as calculated by fastq_scan
fastq_scan_raw1_json	File	The JSON file output from fastq-scan containing summary stats about raw forward read quality and length
fastq_scan_version	String	The version of fastq_scan
fastqc_clean1_html	File	An HTML file that provides a graphical visualization of clean forward read quality from fastqc to open in an internet browser
fastqc_docker	String	The Docker container used for fastqc
fastqc_num_reads_clean1	Int	The number of forward reads after cleaning by fastqc
fastqc_num_reads_raw1	Int	The number of input forward reads by fastqc before cleaning
fastqc_raw1_html	File	An HTML file that provides a graphical visualization of raw forward read quality from fastqc to open in an internet browser
fastqc_version	String	Version of fastqc software used
freyja_abundances	String	Abundances estimates identified by Freyja and parsed from freyja_demixed file
freyja_barcode_version	String	Name of barcode file used, or the date if update_db is true
freyja_bootstrap_lineages	File	A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each lineage
freyja_bootstrap_lineages_pdf	File	A boxplot of the bootstrap lineages CSV file
freyja_bootstrap_summary	File	A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each WHO designated VOI/VOC
freyja_bootstrap_summary_pdf	File	A boxplot of the bootstrap summary CSV file
freyja_coverage	Float	Coverage identified by Freyja and parsed from freyja_demixed file
freyja_demixed	File	The main output TSV; see the section directly above this table for an explanation
freyja_demixed_parsed	File	Parsed freyja_demixed file, containing the same information, for easy result concatenation
freyja_depths	File	A TSV listing the depth of every position
freyja_fastq_wf_analysis_date	String	Date of analysis
freyja_fastq_wf_version	String	The version of the Public Health Bioinformatics (PHB) repository used
freyja_lineages	String	Lineages in descending order identified by Freyja and parsed from freyja_demixed file
freyja_metadata_version	String	Name of lineage metadata file used, or the date if update_db is true
freyja_resid	String	Residual of the weighted least absolute deviation problem used to estimate lineage abundances identified by Freyja and parsed from freyja_demixed file
freyja_sc2_barcode_file	File	Barcode feather file used for SARS-CoV-2. Can be the one provided as input or downloaded by Freyja if update_db is true
freyja_sc2_lineage_metadata_file	File	Lineage metadata JSON file used for SARS-CoV-2. Can be the one provided as input or downloaded by Freyja if update_db is true
freyja_summarized	String	Sum of all lineage abundances in a particular WHO designation identified by Freyja and parsed from freyja_demixed file
freyja_variants	File	The TSV file containing the variants identified by Freyja
freyja_version	String	version of Freyja used
ivar_version_primtrim	String	Version of iVar for running the iVar trim command
kraken_human	Float	Percent of human read data detected using the Kraken2 software
kraken_human_dehosted	Float	Percent of human read data detected using the Kraken2 software after host removal
kraken_report	File	Full Kraken report
kraken_report_dehosted	File	Full Kraken report after host removal
kraken_sc2	String	Percent of SARS-CoV-2 read data detected using the Kraken2 software
kraken_sc2_dehosted	Float	Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal
kraken_version	String	Version of Kraken software used
primer_bed_name	String	Name of the primer bed files used for primer trimming
primer_trimmed_read_percent	Float	Percentage of read data with primers trimmed as determined by iVar trim
samtools_version	String	The version of SAMtools used to sort and index the alignment file
samtools_version_primtrim	String	The version of SAMtools used to create the pileup before running iVar trim
trimmomatic_docker	String	The docker image used for the trimmomatic module in this workflow
trimmomatic_version	String	The version of Trimmomatic used

Variable	Type	Description
aligned_bai	File	Index companion file to the bam file generated during the consensus assembly process
aligned_bam	File	Sorted BAM file containing the alignments of reads to the reference genome
alignment_method	String	The method used to generate the alignment
freyja_abundances	String	Abundances estimates identified by Freyja and parsed from freyja_demixed file
freyja_barcode_version	String	Name of barcode file used, or the date if update_db is true
freyja_bootstrap_lineages	File	A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each lineage
freyja_bootstrap_lineages_pdf	File	A boxplot of the bootstrap lineages CSV file
freyja_bootstrap_summary	File	A CSV that contains the 0.025, 0.05, 0.25, 0.5 (median), 0.75, 0.95, and 0.975 percentiles for each WHO designated VOI/VOC
freyja_bootstrap_summary_pdf	File	A boxplot of the bootstrap summary CSV file
freyja_coverage	Float	Coverage identified by Freyja and parsed from freyja_demixed file
freyja_demixed	File	The main output TSV; see the section directly above this table for an explanation
freyja_demixed_parsed	File	Parsed freyja_demixed file, containing the same information, for easy result concatenation
freyja_depths	File	A TSV listing the depth of every position
freyja_fastq_wf_analysis_date	String	Date of analysis
freyja_fastq_wf_version	String	The version of the Public Health Bioinformatics (PHB) repository used
freyja_lineages	String	Lineages in descending order identified by Freyja and parsed from freyja_demixed file
freyja_metadata_version	String	Name of lineage metadata file used, or the date if update_db is true
freyja_resid	String	Residual of the weighted least absolute deviation problem used to estimate lineage abundances identified by Freyja and parsed from freyja_demixed file
freyja_sc2_barcode_file	File	Barcode feather file used for SARS-CoV-2. Can be the one provided as input or downloaded by Freyja if update_db is true
freyja_sc2_lineage_metadata_file	File	Lineage metadata JSON file used for SARS-CoV-2. Can be the one provided as input or downloaded by Freyja if update_db is true
freyja_summarized	String	Sum of all lineage abundances in a particular WHO designation identified by Freyja and parsed from freyja_demixed file
freyja_variants	File	The TSV file containing the variants identified by Freyja
freyja_version	String	version of Freyja used
ivar_version_primtrim	String	Version of iVar for running the iVar trim command
kraken_human	Float	Percent of human read data detected using the Kraken2 software
kraken_human_dehosted	Float	Percent of human read data detected using the Kraken2 software after host removal
kraken_report	File	Full Kraken report
kraken_report_dehosted	File	Full Kraken report after host removal
kraken_sc2	String	Percent of SARS-CoV-2 read data detected using the Kraken2 software
kraken_sc2_dehosted	Float	Percent of SARS-CoV-2 read data detected using the Kraken2 software after host removal
kraken_version	String	Version of Kraken software used
minimap2_docker	String	The Docker image of minimap2
minimap2_version	String	The version of minimap2
nanoplot_html_clean	File	An HTML report describing the clean reads
nanoplot_html_raw	File	An HTML report describing the raw reads
nanoplot_num_reads_clean1	Int	Number of clean reads
nanoplot_num_reads_raw1	Int	Number of raw reads
nanoplot_r1_est_coverage_clean	Float	Estimated coverage on the clean reads by nanoplot
nanoplot_r1_est_coverage_raw	Float	Estimated coverage on the raw reads by nanoplot
nanoplot_r1_mean_q_clean	Float	Mean quality score of clean forward reads
nanoplot_r1_mean_q_raw	Float	Mean quality score of raw forward reads
nanoplot_r1_mean_readlength_clean	Float	Mean read length of clean forward reads
nanoplot_r1_mean_readlength_raw	Float	Mean read length of raw forward reads
nanoplot_r1_median_q_clean	Float	Median quality score of clean forward reads
nanoplot_r1_median_q_raw	Float	Median quality score of raw forward reads
nanoplot_r1_median_readlength_clean	Float	Median read length of clean forward reads
nanoplot_r1_median_readlength_raw	Float	Median read length of raw forward reads
nanoplot_r1_n50_clean	Float	N50 of clean forward reads
nanoplot_r1_n50_raw	Float	N50 of raw forward reads
nanoplot_r1_stdev_readlength_clean	Float	Standard deviation read length of clean forward reads
nanoplot_r1_stdev_readlength_raw	Float	Standard deviation read length of raw forward reads
nanoplot_tsv_clean	File	A TSV report describing the clean reads
nanoplot_tsv_raw	File	A TSV report describing the raw reads
nanoq_version	String	Version of nanoq used in analysis
primer_bed_name	String	Name of the primer bed files used for primer trimming
primer_trimmed_read_percent	Float	Percentage of read data with primers trimmed as determined by iVar trim
samtools_version	String	The version of SAMtools used to sort and index the alignment file
samtools_version_primtrim	String	The version of SAMtools used to create the pileup before running iVar trim

Freyja_Plot_PHB

This workflow visualizes aggregated freyja_demixed output files produced by Freyja_FASTQ_PHB in a single plot (pdf format) which provides fractional abundance estimates for all aggregated samples.

Options exist to provide lineage-specific breakdowns and/or sample collection time information.

Inputs

This workflow runs on the set level.

Terra Task Name	Variable	Type	Description	Default Value	Terra Status
freyja_plot	freyja_demixed	Array[File]	An array containing the output files (freyja_demixed) made by Freyja_FASTQ		Required
freyja_plot	freyja_plot_name	String	The name of the plot to be produced. Example: "my-freyja-plot"		Required
freyja_plot	samplename	Array[String]	The names of the samples being analyzed		Required
freyja_plot	collection_date	Array[String]	An array containing the collection dates for the sample (YYYY-MM-DD format)		Optional
freyja_plot_task	cpu	Int	Number of CPUs to allocate to the task	1	Optional
freyja_plot_task	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
freyja_plot_task	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.3	Optional
freyja_plot_task	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	2	Optional
freyja_plot_task	mincov	Int	The minimum genome coverage used as a cut-off of data to include in the plot	60	Optional
freyja_plot_task	plot_day_window	Int	The width of the rolling average window; only used if plot_time_interval is "D"	14	Optional
freyja_plot_task	plot_lineages	Boolean	If true, will plot a lineage-specific breakdown	FALSE	Optional
freyja_plot_task	plot_time	Boolean	If true, will plot sample collection time information (requires the collection_date input variable)	FALSE	Optional
freyja_plot_task	plot_time_interval	String	Options: "MS" for month, "D" for day	MS	Optional
version_capture	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0	Optional
version_capture	timezone	String	Set the time zone to get an accurate date of analysis (uses UTC by default)		Optional

Analysis Tasks

freyja_plot_task Details

This task will aggregate multiple samples together, and then creates a plot. Several optional inputs dictate the plot appearance (see each variable's description for more information).

Freyja Plot Technical Details

	Links
Task	wf_freyja_plot.wdl
Software Source Code	https://github.com/andersen-lab/Freyja
Software Documentation	https://github.com/andersen-lab/Freyja

Outputs

Variable	Type	Description
freyja_demixed_aggregate	File	A TSV file that summarizes the freyja_demixed outputs for all samples
freyja_plot	File	A PDF of the plot produced by the workflow
freyja_plot_metadata	File	The metadata used to create the plot
freyja_plot_version	String	The version of Freyja used
freyja_plot_wf_analysis_date	String	The date of analysis
freyja_plot_wf_version	String	The version of the Public Health Bioinformatics (PHB) repository used

Freyja_Dashboard_PHB

This workflow creates a group of interactive visualizations based off of the aggregated freyja_demixed output files produced by Freyja_FASTQ_PHB called a "dashboard". Creating this dashboard requires knowing the viral load of your samples (viral copies/litre).

Warning

This dashboard is not "live" — that is, you must rerun the workflow every time you want new data to be included in the visualizations.

Inputs

This workflow runs on the set level.

Terra Task Name	Variable	Type	Description	Default Value	Terra Status
freyja_dashboard	collection_date	Array[String]	An array containing the collection dates for the sample (YYYY-MM-DD format)		Required
freyja_dashboard	freyja_dashboard_title	String	The name of the dashboard to be produced. Example: "my-freyja-dashboard"		Required
freyja_dashboard	freyja_demixed	Array[File]	An array containing the output files (freyja_demixed) made by Freyja_FASTQ workflow		Required
freyja_dashboard	samplename	Array[String]	The names of the samples being analyzed		Required
freyja_dashboard	viral_load	Array[String]	An array containing the number of viral copies per liter		Required
freyja_dashboard_task	config	File	(found in the optional section, but is required) A yaml file that applies various configurations to the dashboard, such as grouping lineages together, applying colorings, etc. See also https://github.com/andersen-lab/Freyja/blob/main/freyja/data/plot_config.yml.		Optional, Required
freyja_dashboard	dashboard_intro_text	File	A file containing the text to be contained at the top of the dashboard.	SARS-CoV-2 lineage de-convolution performed by the Freyja workflow (https://github.com/andersen-lab/Freyja).	Optional
freyja_dashboard_task	cpu	Int	Number of CPUs to allocate to the task	1	Optional
freyja_dashboard_task	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
freyja_dashboard_task	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/freyja:1.5.3	Optional
freyja_dashboard_task	headerColor	String	A hex color code to change the color of the header		Optional
freyja_dashboard_task	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	2	Optional
freyja_dashboard_task	mincov	Float	The minimum genome coverage used as a cut-off of data to include in the dashboard. Default is set to 60 by the freyja command-line tool (not a WDL task default, per se)		Optional
freyja_dashboard_task	scale_by_viral_load	Boolean	If set to true, averages samples taken the same day while taking viral load into account	FALSE	Optional
freyja_dashboard_task	thresh	Float	The minimum lineage abundance cut-off value		Optional
version_capture	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/theiagen/alpine-plus-bash:3.20.0	Optional
version_capture	timezone	String	Set the time zone to get an accurate date of analysis (uses UTC by default)		Optional

Analysis Tasks

freyja_dashboard_task Details

This task will aggregate multiple samples together, and then create an interactive HTML visualization. Several optional inputs dictate the dashboard appearance (see each variable's description for more information).

Freyja Dashboard Technical Details

	Links
Task	wf_freyja_dashboard.wdl
Software Source Code	https://github.com/andersen-lab/Freyja
Software Documentation	https://github.com/andersen-lab/Freyja

Outputs

Variable	Type	Description
freyja_dashboard	File	The HTML file of the dashboard created
freyja_dashboard_metadata	File	The metadata used to create the dashboard
freyja_dashboard_version	String	The version of Freyja used
freyja_dashboard_wf_analysis_date	String	The date of analysis
freyja_dashboard_wf_version	String	The version of the Public Health Bioinformatics (PHB) repository used
freyja_demixed_aggregate	File	A TSV file that summarizes the freyja_demixed outputs for all samples

Running Freyja on other pathogens

Experimental Feature

Please be aware this is an experimental feature and we cannot guarantee complete functionality at this time.

The main requirement to run Freyja on other pathogens is the existence of a barcode file for your pathogen of interest. Currently, barcodes exist for the following organisms:

• SARS-CoV-2 (default)
• FLU-B-VIC
• H1N1
• H3N2
• H5Nx-cattle
• H5NX
• MEASLESN450
• MEASLESgenome
• MPX
• RSVa
• RSVb

Freyja barcodes for other pathogens

Data for various pathogens can be found in the following repository: Freyja Barcodes

Folders are organized by pathogen, with each subfolder named after the date the barcode was generated, using the format YYYY-MM-DD, as well as a "latest" folder. Barcode files are named barcode.csv, and reference genome files are named reference.fasta.

There are two ways to run Freyja_FASTQ_PHB for non-SARS-CoV-2 organisms:

• Using the freyja_pathogen optional input (limited set of allowable organisms)
• Providing the appropriate barcode file through the freyja_barcodes optional input (any organism for which barcodes are supplied)

Using the freyja_pathogen flag

When using the freyja_pathogen flag, the user must set the optional update_db flag to true, so that the latest version of the barcode file is automatically downloaded by Freyja.

Figure 2: Optional input for Freyja_FASTQ_PHB to provide the pathogen to be used by Freyja

Figure 2

Allowed options:

• SARS-CoV-2 (default)
• MPXV
• H1N1pdm
• H5NX
• FLU-B-VIC
• MEASLESN450
• MEASLES
• RSVa
• RSVb

Warning

The freyja_pathogen flag is not used if a barcodes file is provided. This means that this option is ignored if a barcode file is provided through freyja_barcodes.

Providing the appropriate barcode file

The appropriate barcode file for your organism of interest and reference sequence need to be downloaded and uploaded to your Terra.bio workspace. When running Freyja_FASTQ_PHB, the appropriate reference and barcodes file need to be passed as inputs. The first is a required input and will show up at the top of the workflows inputs page on Terra.bio (Figure 3).

Figure 3: Required input for Freyja_FASTQ_PHB to provide the reference genome to be used by Freyja

Figure 3

The barcodes file can be passed directly to Freyja by the freyja_barcodes optional input (Figure 4).

Figure 4: Optional input for Freyja_FASTQ_PHB to provide the barcodes file to be used by Freyja

Figure 4

References

If you use any of the Freyja workflows, please cite:

Karthikeyan, S., Levy, J.I., De Hoff, P. et al. Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission. Nature 609, 101–108 (2022). https://doi.org/10.1038/s41586-022-05049-6

Freyja source code can be found at https://github.com/andersen-lab/Freyja

Freyja barcodes (non-SARS-CoV-2): https://github.com/gp201/Freyja-barcodes