TheiaMeta

Quick Facts

Workflow Type	Applicable Kingdom	Last Known Changes	Command-line Compatibility	Workflow Level
Genomic Characterization	Any Taxa	PHB v2.3.0	Yes	Sample-level

TheiaMeta Workflows

Genomic characterization of pathogens is an increasing priority for public health laboratories globally. The workflows in the TheiaMeta Genomic Characterization Series make the analysis of pathogens from metagenomic samples easy by taking raw next-generation sequencing (NGS) data and generating metagenome-assembled genomes (MAGs), either using a reference-genome or not.

TheiaMeta can use one of two distinct methods for generating and processing the final assembly:

• If a reference genome is not provided, the de novo assembly will be the final assembly. Additionally, go through a binning process where the contigs are separated into distinct files ("bins") according to composition and coverage such that each bin hopefully contains a single taxon.
• If a reference genome is provided by the user, the de novo metagenomic assembly is filtered by mapping the contigs to the reference and those constitute the final assembly. No binning is necessary as the mapping will filter contigs that are likely the same taxon as the reference.

TheiaMeta Workflow Diagram

Inputs

The TheiaMeta_Illumina_PE workflow processes Illumina paired-end (PE) reads generated for metagenomic characterization (typically by shotgun). By default, this workflow will assume that input reads were generated using a 300-cycle sequencing kit (i.e. 2 x 150 bp reads). Modifications to the optional parameter for trim_minlen may be required to accommodate shorter read data, such as 2 x 75bp reads generated using a 150-cycle sequencing kit.

Terra Task Name	Variable	Type	Description	Default Value	Terra Status
theiameta_illumina_pe	read1	File	Forward Illumina read in FASTQ file format		Required
theiameta_illumina_pe	read2	File	Reverse Illumina read in FASTQ file format		Required
theiameta_illumina_pe	samplename	String	Name of the sample being analyzed		Required
assembled_reads_percent	cpu	Int	Number of CPUs to allocate to the task	2	Optional
assembled_reads_percent	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
assembled_reads_percent	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/samtools:1.17	Optional
assembled_reads_percent	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
bwa	cpu	Int	Number of CPUs to allocate to the task	6	Optional
bwa	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
bwa	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/ivar:1.3.1-titan	Optional
bwa	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	16	Optional
calculate_coverage	cpu	Int	Number of CPUs to allocate to the task	2	Optional
calculate_coverage	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
calculate_coverage	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/bedtools:2.31.0	Optional
calculate_coverage	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
calculate_coverage_paf	cpu	Int	Number of CPUs to allocate to the task	2	Optional
calculate_coverage_paf	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
calculate_coverage_paf	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/quay/ubuntu:latest	Optional
calculate_coverage_paf	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
compare_assemblies	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
kraken2_clean	cpu	Int	Number of CPUs to allocate to the task	4	Optional
kraken2_clean	disk_size	Int	GB of storage to request for VM used to run the kraken2 task. Increase this when using large (>30GB kraken2 databases such as the "k2_standard" database)	100	Optional
kraken2_clean	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/kraken2:2.1.2-no-db	Optional
kraken2_clean	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	16	Optional
kraken2_raw	cpu	Int	Number of CPUs to allocate to the task	4	Optional
kraken2_raw	disk_dize	Int	GB of storage to request for VM used to run the kraken2 task. Increase this when using large (>30GB kraken2 databases such as the "k2_standard" database)	100	Optional
kraken2_raw	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/kraken2:2.1.2-no-db	Optional
kraken2_raw	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	16	Optional
krona_clean	cpu	Int	Number of CPUs to allocate to the task	4	Optional
krona_clean	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
krona_clean	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/biocontainers/krona:2.7.1--pl526_5	Optional
krona_clean	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
krona_raw	cpu	Int	Number of CPUs to allocate to the task	4	Optional
krona_raw	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
krona_raw	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/biocontainers/krona:2.7.1--pl526_5	Optional
krona_raw	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
metaspades	kmers	String	Kmer list to use with metaspades. If not provided metaspades automatically sets this value		Optional
metaspades	metaspades_opts	String	Additional arguments to pass to metaspades task		Optional
minimap2_assembly	cpu	Int	Number of CPUs to allocate to the task	2	Optional
minimap2_assembly	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
minimap2_assembly	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/minimap2:2.22	Optional
minimap2_assembly	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
minimap2_assembly	query2	File	Internal component. Do not modify.		Optional
minimap2_reads	cpu	Int	Number of CPUs to allocate to the task	2	Optional
minimap2_reads	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
minimap2_reads	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/minimap2:2.22	Optional
minimap2_reads	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
quast	cpu	Int	Number of CPUs to allocate to the task	2	Optional
quast	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
quast	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/quast:5.0.2	Optional
quast	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	2	Optional
read_QC_trim	adapters	File	Adapter file to be trimmed by trimmomatic		Optional
read_QC_trim	bbduck_mem	Int	Memory to use with bbduck	8	Optional
read_QC_trim	call_midas	Boolean	Optional to run Midas on input data	FALSE	Optional
read_QC_trim	fastp_args	String	Fastp-specific options that you might choose, see https://github.com/OpenGene/fastp		Optional
read_QC_trim	midas_db	File	A Midas database in .tar.gz format	gs://theiagen-public-files-rp/terra/theiaprok-files/midas/midas_db_v1.2.tar.gz	Optional
read_QC_trim	phix	File			Optional
read_QC_trim	read_processing	String			Optional
read_QC_trim	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	target_organism	String	Internal component. Do not modify.		Optional
read_QC_trim	trim_min_length	Int			Optional
read_QC_trim	trim_window_size	Int			Optional
read_QC_trim	trimmomatic_args	String			Optional
retrieve_aligned_contig_paf	cpu	Int	Number of CPUs to allocate to the task	2	Optional
retrieve_aligned_contig_paf	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
retrieve_aligned_contig_paf	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/seqkit:2.3.1	Optional
retrieve_aligned_contig_paf	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
retrieve_aligned_pe_reads_sam	cpu	Int	Number of CPUs to allocate to the task	2	Optional
retrieve_aligned_pe_reads_sam	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
retrieve_aligned_pe_reads_sam	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/samtools:1.17	Optional
retrieve_aligned_pe_reads_sam	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
retrieve_unaligned_pe_reads_sam	cpu	Int	Number of CPUs to allocate to the task	2	Optional
retrieve_unaligned_pe_reads_sam	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
retrieve_unaligned_pe_reads_sam	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/samtools:1.17	Optional
retrieve_unaligned_pe_reads_sam	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
sam_to_sorted_bam	cpu	Int	Number of CPUs to allocate to the task	2	Optional
sam_to_sorted_bam	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
sam_to_sorted_bam	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/samtools:1.17	Optional
sam_to_sorted_bam	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
semibin	cpu	Int	Number of CPUs to allocate to the task	6	Optional
semibin	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
semibin	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/biocontainers/semibin:2.0.2--pyhdfd78af_0	Optional
semibin	environment	String	Environment model to use. Options: • human_gut • dog_gut • ocean • soil • cat_gut • human_oral • mouse_gut • pig_gut • built_environment • wastewater • chicken_caecum - global	global	Optional
semibin	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
semibin	min_length	Int	Minimum contig length for binning	1000	Optional
semibin	ratio	Float	If the ratio of the number of base pairs of contigs between 1000-2500 bp smaller than this value, the minimal length will be set as 1000bp, otherwise 2500bp.	0.05	Optional
sort_bam_assembly_correction	cpu	Int	Number of CPUs to allocate to the task	2	Optional
sort_bam_assembly_correction	disk_size	Int	Amount of storage (in GB) to allocate to the task	100	Optional
sort_bam_assembly_correction	docker	String	The Docker container to use for the task	us-docker.pkg.dev/general-theiagen/staphb/samtools:1.17	Optional
sort_bam_assembly_correction	memory	Int	Amount of memory/RAM (in GB) to allocate to the task	8	Optional
theiameta_illumina_pe	kraken2_db	File	A Kraken2 database in .tar.gz format	gs://theiagen-public-files-rp/terra/theiaprok-files/k2_standard_08gb_20230605.tar.gz	Optional
theiameta_illumina_pe	output_additional_files	Boolean	Output additional files such as aligned and unaligned reads to reference	FALSE	Optional
theiameta_illumina_pe	reference	File	Reference file for consensus calling, in FASTA format		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

Workflow Tasks

versioning: Version Capture for TheiaMeta

The versioning task captures the workflow version from the GitHub (code repository) version.

Version Capture Technical details

	Links
Task	task_versioning.wdl

Read Cleaning and QC

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	NCBI Scrub on GitHub
Software Documentation	https://github.com/ncbi/sra-human-scrubber/blob/master/README.md

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

read_QC_trim is a sub-workflow within TheiaMeta 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.

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. In TheiaProk_Illumina_PE, 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 Identification (optional)

The MIDAS task is for the identification of reads to detect contamination with non-target taxa. This task is optional and turned off by default. It can be used by setting the call_midas input variable to true.

The MIDAS reference database, located at gs://theiagen-large-public-files-rp/terra/theiaprok-files/midas/midas_db_v1.2.tar.gz, is provided as the default. It is possible to provide a custom database. More information is available here.

How are the MIDAS output columns determined?

Example MIDAS report in the ****midas_report column:

species_id	count_reads	coverage	relative_abundance
Salmonella_enterica_58156	3309	89.88006645	0.855888033
Salmonella_enterica_58266	501	11.60606061	0.110519371
Salmonella_enterica_53987	99	2.232896237	0.021262881
Citrobacter_youngae_61659	46	0.995216227	0.009477003
Escherichia_coli_58110	5	0.123668877	0.001177644

MIDAS report column descriptions:

• species_id: species identifier
• count_reads: number of reads mapped to marker genes
• coverage: estimated genome-coverage (i.e. read-depth) of species in metagenome
• relative_abundance: estimated relative abundance of species in metagenome

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

kraken: Taxonomic Classification

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 the set of raw reads, provided as input, as well as the set of clean reads that are resulted from the read_QC_trim workflow

Database-dependent

The Kraken2 software is database-dependent and taxonomic assignments are highly sensitive to the database used. An appropriate database should contain the expected organism(s) (e.g. Escherichia coli) and other taxa that may be present in the reads (e.g. Citrobacter freundii, a common contaminant).

Kraken2 Technical Details

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

Assembly

metaspades: De Novo Metagenomic Assembly

While metagenomics has emerged as a technology of choice for analyzing bacterial populations, the assembly of metagenomic data remains challenging. A dedicated metagenomic assembly algorithm is necessary to circumvent the challenge of interpreting variation. metaSPAdes addresses various challenges of metagenomic assembly by capitalizing on computational ideas that proved to be useful in assemblies of single cells and highly polymorphic diploid genomes.

metaspades is a de novo assembler that first constructs a de Bruijn graph of all the reads using the SPAdes algorithm. Through various graph simplification procedures, paths in the assembly graph are reconstructed that correspond to long genomic fragments within the metagenome. For more details, please see the original publication.

MetaSPAdes Technical Details

	Links
Task	task_metaspades.wdl
Software Source Code	SPAdes on GitHub
Software Documentation	SPAdes Manual
Original Publication(s)	metaSPAdes: a new versatile metagenomic assembler

minimap2: Assembly Alignment and Contig Filtering

If a reference genome is provided through the reference optional input, the assembly produced with metaspades will be mapped to the reference genome with minimap2. The contigs which align to the reference are retrieved and returned in the assembly_fasta output.

minimap2 is a popular aligner that is used for correcting the assembly produced by metaSPAdes. This is done by aligning the reads back to the generated assembly or a reference genome.

In minimap2, "modes" are a group of preset options. Two different modes are used in this task depending on whether a reference genome is provided.

If a reference genome is not provided, the only mode used in this task is sr which is intended for "short single-end reads without splicing". The sr mode indicates the following parameters should be used: -k21 -w11 --sr --frag=yes -A2 -B8 -O12,32 -E2,1 -b0 -r100 -p.5 -N20 -f1000,5000 -n2 -m20 -s40 -g100 -2K50m --heap-sort=yes --secondary=no. The output file is in SAM format.

If a reference genome is provided, then after the draft assembly polishing with pilon, this task runs again with the mode set to asm20 which is intended for "long assembly to reference mapping". The asm20 mode indicates the following parameters should be used: -k19 -w10 -U50,500 --rmq -r100k -g10k -A1 -B4 -O6,26 -E2,1 -s200 -z200 -N50. The output file is in PAF format.

For more information, 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

samtools: SAM File Conversion

This task converts the output SAM file from minimap2 and converts it to a BAM file. It then sorts the BAM based on the read names, and then generates an index file.

samtools Technical Details

	Links
Task	task_samtools.wdl
Software Source Code	samtools on GitHub
Software Documentation	samtools
Original Publication(s)	The Sequence Alignment/Map format and SAMtools Twelve Years of SAMtools and BCFtools

pilon: Assembly Polishing

pilon is a tool that uses read alignment to correct errors in an assembly. It is used to polish the assembly produced by metaSPAdes. The input to Pilon is the sorted BAM file produced by samtools, and the original draft assembly produced by metaspades.

pilon Technical Details

	Links
Task	task_pilon.wdl
Software Source Code	Pilon on GitHub
Software Documentation	Pilon Wiki
Original Publication(s)	Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement

Assembly QC

quast: Assembly Quality Assessment

QUAST stands for QUality ASsessment Tool. It evaluates genome/metagenome assemblies by computing various metrics without a reference being necessary. It includes useful metrics such as number of contigs, length of the largest contig and N50.

QUAST Technical Details

	Links
Task	task_quast.wdl
Software Source Code	QUAST on GitHub
Software Documentation	https://quast.sourceforge.net/
Original Publication(s)	QUAST: quality assessment tool for genome assemblies

Binning

semibin2: Metagenomic binning (if a reference is NOT provided)

If no reference genome is provided through the reference optional input, the assembly produced with metaspades will be binned with semibin2, a a command tool for metagenomic binning with deep learning.

Outputs

Variable	Type	Description
assembly_fasta	File	Final assembly (MAG)
assembly_length	Int	Length of final assembly in basepairs
assembly_mean_coverage	Float	Mean depth of coverage of the final assembly
average_read_length	Float	Average read length of the clean reads
bbduk_docker	String	Docker image for bbduk
bedtools_docker	String	Docker image for bedtools
bedtools_version	String	Version of bedtools
contig number	Int	Number of contigs in final assembly
fastp_html_report	File	Report file for fastp in HTML format
fastp_version	String	Version of fastp used
fastq_scan_docker	String	Docker image of fastq_scan
fastq_scan_clean1_json	File	JSON file output from fastq-scan containing summary stats about clean forward read quality and length
fastq_scan_clean2_json	File	JSON file output from fastq-scan containing summary stats about clean reverse read quality and length
fastq_scan_num_reads_clean_pairs	String	Number of read pairs after cleaning as calculated by fastq_scan
fastq_scan_num_reads_clean1	Int	Number of forward reads after cleaning as calculated by fastq_scan
fastq_scan_num_reads_clean2	Int	Number of reverse reads after cleaning as calculated by fastq_scan
fastq_scan_num_reads_raw_pairs	String	Number of input read pairs as calculated by fastq_scan
fastq_scan_num_reads_raw1	Int	Number of input forward reads as calculated by fastq_scan
fastq_scan_num_reads_raw2	Int	Number of input reserve reads as calculated by fastq_scan
fastq_scan_raw1_json	File	JSON file output from fastq-scan containing summary stats about raw forward read quality and length
fastq_scan_raw2_json	File	JSON file output from fastq-scan containing summary stats about raw reverse read quality and length
fastq_scan_version	String	fastq_scan version
fastqc_clean1_html	File	Graphical visualization of clean forward read quality from fastqc to open in an internet browser
fastqc_clean2_html	File	Graphical visualization of clean reverse read quality from fastqc to open in an internet browser
fastqc_docker	String	Docker container used for fastqc
fastqc_num_reads_clean_pairs	String	Number of read pairs after cleaning by fastqc
fastqc_num_reads_clean1	Int	Number of forward reads after cleaning by fastqc
fastqc_num_reads_clean2	Int	Number of reverse reads after cleaning by fastqc
fastqc_num_reads_raw_pairs	String	Number of input read pairs by fastqc
fastqc_num_reads_raw1	Int	Number of input forward reads by fastqc
fastqc_num_reads_raw2	Int	Number of input reverse reads by fastqc
fastqc_raw1_html	File	Graphical visualization of raw forward read quality from fastqc to open in an internet browser
fastqc_raw2_html	File	Graphical visualization of raw reverse read qualityfrom fastqc to open in an internet browser
fastqc_version	String	Version of fastqc software used
kraken2_docker	String	Docker image of kraken2
kraken2_percent_human_clean	Float	Percentage of human-classified reads in the sample's clean reads
kraken2_percent_human_raw	Float	Percentage of human-classified reads in the sample's raw reads
kraken2_report_clean	File	Full Kraken report for the sample's clean reads
kraken2_report_raw	File	Full Kraken report for the sample's raw reads
kraken2_version	String	Version of kraken
krona_docker	String	Docker image of Krona
krona_html_clean	File	The KronaPlot after reads are cleaned
krona_html_raw	File	The KronaPlot before reads are cleaned
krona_version	String	Version of Krona
largest_contig	Int	Largest contig size
metaspades_docker	String	Docker image of metaspades
metaspades_version	String	Version of metaspades
midas_primary_genus	String	Primary genus detected by MIDAS
midas_report	File	MIDAS report file tsv file
minimap2_docker	String	Docker image of minimap2
minimap2_version	String	Version of minimap2
ncbi_scrub_docker	String	Docker image for NCBI's HRRT
percent_coverage	Float	Percentage coverage of the reference genome provided
percentage_mapped_reads	Float	Percentage of mapped reads to the assembly
pilon_docker	String	Docker image for pilon
pilon_version	String	Version of pilon
quast_docker	String	Docker image of QUAST
quast_version	String	Version of QUAST
read1_clean	File	Clean forward reads file
read1_dehosted	File	Dehosted forward reads file
read1_mapped	File	Mapped forward reads to the assembly
read1_unmapped	File	Unmapped forwards reads to the assembly
read2_clean	File	Clean reverse reads file
read2_dehosted	File	Dehosted reverse reads file
read2_mapped	File	Mapped reverse reads to the assembly
read2_unmapped	File	Unmapped reverse reads to the assembly
samtools_docker	String	Docker image of samtools
samtools_version	String	Version of samtools
semibin_bins	Array[File]	Array of binned metagenomic assembled genome files
semibin_docker	String	Docker image of semibin
semibin_version	String	Semibin version used
theiameta_illumina_pe_analysis_date	String	Date of analysis
theiameta_illumina_pe_version	String	Version of workflow
trimmomatic_docker	String	Docker image of trimmomatic
trimmomatic_version	String	Version of trimmomatic used

References

Human read removal tool (HRRT): https://github.com/ncbi/sra-human-scrubber

Trimmomatic: Anthony M. Bolger and others, Trimmomatic: a flexible trimmer for Illumina sequence data, Bioinformatics, Volume 30, Issue 15, August 2014, Pages 2114–2120, https://doi.org/10.1093/bioinformatics/btu170

Fastq-Scan: https://github.com/rpetit3/fastq-scan

metaSPAdes: Sergey Nurk and others, metaSPAdes: a new versatile metagenomic assembler, Genome Res. 2017 May; 27(5): 824–834., https://doi.org/10.1101%2Fgr.213959.116

Pilon: Bruce J. Walker and others. Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement. Plos One. November 19, 2014. https://doi.org/10.1371/journal.pone.0112963

Minimap2: Heng Li, Minimap2: pairwise alignment for nucleotide sequences, Bioinformatics, Volume 34, Issue 18, September 2018, Pages 3094–3100, https://doi.org/10.1093/bioinformatics/bty191

QUAST: Alexey Gurevich and others, QUAST: quality assessment tool for genome assemblies, Bioinformatics, Volume 29, Issue 8, April 2013, Pages 1072–1075, https://doi.org/10.1093/bioinformatics/btt086

Samtools: Li, Heng, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, Richard Durbin, and 1000 Genome Project Data Processing Subgroup. 2009. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25(16): 2078-2079.

Bcftools: Petr Danecek, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, Thomas Keane, Shane A McCarthy, Robert M Davies, Heng Li. Twelve years of SAMtools and BCFtools. GigaScience, Volume 10, Issue 2, February 2021, giab008, https://doi.org/10.1093/gigascience/giab008

Semibin2: Shaojun Pan, Xing-Ming Zhao, Luis Pedro Coelho, SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing, Bioinformatics, Volume 39, Issue Supplement_1, June 2023, Pages i21–i29, https://doi.org/10.1093/bioinformatics/btad209