| Status | Module | Environment | Executable | Resources | Recommended Threads | Description | Database |
|---|---|---|---|---|---|---|---|
| Stable | preprocess | VEBA-preprocess_env | preprocess.py | 4GB-16GB | 4 | Fastq quality trimming, adapter removal, decontamination, and read statistics calculations (Short Reads) | Contamination |
| Stable | preproces-long | VEBA-preprocess_env | preproces-long.py | 4GB-16GB | 4 | Fastq quality trimming, adapter removal, decontamination, and read statistics calculations (Long Reads) | Contamination |
| Stable | assembly | VEBA-assembly_env | assembly.py | 32GB-128GB+ | 16 | Assemble short reads, align reads to assembly, and count mapped reads | |
| Stable | assembly-long | VEBA-assembly_env | assembly-long.py | 32GB-128GB+ | 16 | Assemble long reads, align reads to assembly, and count mapped reads | |
| Stable | coverage | VEBA-assembly_env | coverage.py | 24GB | 16 | Align short reads to (concatenated) reference and counts mapped reads | |
| Stable | coverage-long | VEBA-assembly_env | coverage-long.py | 24GB | 16 | Align long reads to (concatenated) reference and counts mapped reads | |
| Stable | binning-prokaryotic | VEBA-binning-prokaryotic_env | binning-prokaryotic.py | 16GB | 4 | Iterative consensus binning for recovering prokaryotic genomes with lineage-specific quality assessment | Classify |
| Stable | binning-eukaryotic | VEBA-binning-eukaryotic_env | binning-eukaryotic.py | 128GB | 4 | Binning for recovering eukaryotic genomes with exon-aware gene modeling and lineage-specific quality assessment | Classify |
| Stable | binning-viral | VEBA-binning-viral_env | binning-viral.py | 16GB | 4 | Detection of viral genomes and quality assessment | Classify |
| Stable | classify-prokaryotic | VEBA-classify-prokaryotic_env | classify-prokaryotic.py | 90GB | 32 | Taxonomic classification of prokaryotic genomes | Classify |
| Stable | classify-eukaryotic | VEBA-classify-eukaryotic_env | classify-eukaryotic.py | 32GB | 1 | Taxonomic classification of eukaryotic genomes | Classify |
| Stable | classify-viral | VEBA-classify-viral_env | classify-viral.py | 16GB | 4 | Taxonomic classification of viral genomes | Classify |
| Stable | cluster | VEBA-cluster_env | cluster.py | 32GB+ | 32 | Species-level clustering of genomes and lineage-specific orthogroup detection | |
| Stable | annotate | VEBA-annotate_env | annotate.py | 64GB | 32 | Annotates translated gene calls | Annotate |
| Stable | phylogeny | VEBA-phylogeny_env | phylogeny.py | 16GB+ | 32 | Constructs phylogenetic trees given a marker set | |
| Stable | index | VEBA-mapping_env | index.py | 16GB | 4 | Builds local or global index for alignment to genomes | |
| Stable | mapping | VEBA-mapping_env | mapping.py | 16GB | 4 | Aligns reads to local or global index of genomes | |
| Stable | biosynthetic | VEBA-biosynthetic_env | biosynthetic.py | 16GB | 16 | Identify biosynthetic gene clusters in prokaryotes and fungi | Annotate |
| Stable | profile-taxonomy | VEBA-profile_env | profile-taxonomy.py | 8-24GB | 4 | Taxonomic profiling of de novo genomes | |
| Stable | profile-pathway | VEBA-profile_env | profile-pathway.py | 16-32GB | 4 | Pathway profiling of de novo genomes | |
| Deprecated | assembly-sequential | VEBA-assembly_env | assembly-sequential.py | 32GB-128GB+ | 16 | Assemble metagenomes sequentially | |
| Developmental | amplicon | VEBA-amplicon_env | amplicon.py | 96GB | 16 | Automated read trim position detection, DADA2 ASV detection, taxonomic classification, and file conversion | |
| Developmental | crispr | VEBA-crispr_env | crispr.py | 4GB | 16 | CRISPR-Cas system detection |
Fastq quality trimming, adapter removal, decontamination, and read statistics calculations
The preprocess module is a wrapper around our fastq_preprocessor which is a modernized reimplementation of KneadData that relies on fastp for ultra-fast automated adapter removal and quality trimming. Pairing of the trimmed reads is assessed and corrected using BBTools’ repair.sh. If the user provides a contamination database (e.g., the human reference genome), then trimmed reads are aligned using Bowtie2 and reads that do not map to the contamination database are stored. If the --retain_contaminated_reads flag is used then the contaminated reads are stored as well. Similarly, if a k-mer reference database is provided (e.g., ribosomal k-mers) then the trimmed or decontaminated reads are aligned against the reference database using BBTools’ bbduk.sh with an option for storing. By default, the none of the contaminated or k-mer analyzed reads are stored but regardless of the choice for retaining reads, the read sets are quantified using seqkit for accounting purposes (e.g., % contamination or % ribosomal). All sequences included were downloaded using Kingfisher, included in the preprocess environment, which a fast and flexible program for the procurement of sequencing files and their annotations from public data sources including ENA, NCBI SRA, Amazon AWS, and Google Cloud.
Conda Environment: conda activate VEBA-preprocess_env
usage: preprocess.py -1 <reads_1.fq> -2 <reads_2.fq> -n <name> -o <output_directory> |Optional| -x <reference_index> -k <kmer_database>
Wrapper around github.com/jolespin/fastq_preprocessor
Running: preprocess.py v2023.5.8 via Python v3.9.15 | /expanse/projects/jcl110/anaconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-1 FORWARD_READS, --forward_reads FORWARD_READS
path/to/reads_1.fastq
-2 REVERSE_READS, --reverse_reads REVERSE_READS
path/to/reads_2.fastq
-n NAME, --name NAME Name of sample
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/preprocess]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv. Must have at least 2 columns [name, executable] [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint
-v, --version show program's version number and exit
Fastp arguments:
-m MINIMUM_READ_LENGTH, --minimum_read_length MINIMUM_READ_LENGTH
Fastp | Minimum read length [Default: 75]
-a ADAPTERS, --adapters ADAPTERS
Fastp | path/to/adapters.fasta [Default: detect]
--fastp_options FASTP_OPTIONS
Fastp | More options (e.g. --arg 1 ) [Default: '']
Bowtie2 arguments:
-x CONTAMINATION_INDEX, --contamination_index CONTAMINATION_INDEX
Bowtie2 | path/to/contamination_index
(e.g., Human T2T CHM13 v2 in $VEBA_DATABASE/Contamination/chm13v2.0/chm13v2.0)
--retain_trimmed_reads RETAIN_TRIMMED_READS
Retain fastp trimmed fastq after decontamination. 0=No, 1=yes [Default: 0]
--retain_contaminated_reads RETAIN_CONTAMINATED_READS
Retain contaminated fastq after decontamination. 0=No, 1=yes [Default: 0]
--bowtie2_options BOWTIE2_OPTIONS
Bowtie2 | More options (e.g. --arg 1 ) [Default: '']
http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml
BBDuk arguments:
-k KMER_DATABASE, --kmer_database KMER_DATABASE
BBDuk | path/to/kmer_database
(e.g., Ribokmers in $VEBA_DATABASE/Contamination/kmers/ribokmers.fa.gz)
--kmer_size KMER_SIZE
BBDuk | k-mer size [Default: 31]
--retain_kmer_hits RETAIN_KMER_HITS
Retain reads that map to k-mer database. 0=No, 1=yes [Default: 0]
--retain_non_kmer_hits RETAIN_NON_KMER_HITS
Retain reads that do not map to k-mer database. 0=No, 1=yes [Default: 0]
--bbduk_options BBDUK_OPTIONS
BBDuk | More options (e.g., --arg 1) [Default: '']
Output:
- cleaned/trimmed_1.fastq.gz - Cleaned or trimmed fastq file (forward)
- cleaned/trimmed_2.fastq.gz - Cleaned or trimmed fastq file (reverse)
- seqkit_stats.concatenated.tsv - Concatenated read statistics for all intermediate steps (e.g., fastp, bowtie2 removal of contaminated reads if provided, bbduk.sh removal of contaminated reads if provided)
Fastq quality trimming, adapter removal, decontamination, and read statistics calculations (Oxford Nanopore & PacBio)s
The preprocess-long module uses the approach from the original preprocess module and applies it to long-read technologies such as Oxford Nanopore and PacBio. More specifically, the following methodology is implemented: 1) reads are quality trimmed using NanoPack2 Chopper instead of fastp; 2) trimmed reads are aligned to a contamination database using minimap2 instead of Bowtie2; 3) BBTools’ BBDuk.sh for k-mer profiling (as in preprocess); and 4) SeqKit for read accounting purposes. As with the preprocess module, decontamination and k-mer quantification is optional.
Conda Environment: conda activate VEBA-preprocess_env
usage: preprocess-long.py -i <reads.fq[.gz]> -n <name> -o <output_directory> |Optional| -x <reference_index> -k <kmer_database>
Running: preprocess-long.py v2023.12.12 via Python v3.9.16 | /expanse/projects/jcl110/miniconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-i READS, --reads READS
path/to/reads.fastq[.gz]
-n NAME, --name NAME Name of sample
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/preprocess]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv. Must have at least 2 columns [name, executable] [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint
-v, --version show program's version number and exit
Chopper arguments:
-m MINIMUM_READ_LENGTH, --minimum_read_length MINIMUM_READ_LENGTH
Chopper | Minimum read length [Default: 500]
-q MINIMUM_QUALITY_SCORE, --minimum_quality_score MINIMUM_QUALITY_SCORE
Chopper | Minimum quality score [Default: 10]
--chopper_options CHOPPER_OPTIONS
Chopper | More options (e.g. --arg 1 ) https://github.com/wdecoster/chopper [Default: '']
MiniMap2 arguments:
-x CONTAMINATION_INDEX, --contamination_index CONTAMINATION_INDEX
MiniMap2 | path/to/contamination_index
(e.g., Human T2T assembly from https://ftp.ncbi.nlm.nih.gov/genomes/refseq/vertebrate_mammalian/Homo_sapiens/latest_assembly_versions/GCF_009914755.1_T2T-CHM13v2.0/GCF_009914755.1_T2T-CHM13v2.0_genomic.fna.gz)
--minimap2_preset MINIMAP2_PRESET
MiniMap2 | MiniMap2 preset {map-pb, map-ont, map-hifi} [Default: map-ont]
--retain_trimmed_reads RETAIN_TRIMMED_READS
Retain Chopper trimmed fastq after decontamination. 0=No, 1=yes [Default: 0]
--retain_contaminated_reads RETAIN_CONTAMINATED_READS
Retain contaminated fastq after decontamination. 0=No, 1=yes [Default: 0]
--minimap2_options MINIMAP2_OPTIONS
MiniMap2 | More options (e.g. --arg 1 ) [Default: '']
https://github.com/lh3/minimap2
BBDuk arguments:
-k KMER_DATABASE, --kmer_database KMER_DATABASE
BBDuk | path/to/kmer_database
(e.g., ribokmers.fa.gz from https://figshare.com/ndownloader/files/36220587)
--kmer_size KMER_SIZE
BBDuk | k-mer size [Default: 31]
--retain_kmer_hits RETAIN_KMER_HITS
Retain reads that map to k-mer database. 0=No, 1=yes [Default: 0]
--retain_non_kmer_hits RETAIN_NON_KMER_HITS
Retain reads that do not map to k-mer database. 0=No, 1=yes [Default: 0]
--bbduk_options BBDUK_OPTIONS
BBDuk | More options (e.g., --arg 1) [Default: '']
Output:
- cleaned/trimmed.fastq.gz - Cleaned or trimmed fastq file (forward)
- seqkit_stats.concatenated.tsv - Concatenated read statistics for all intermediate steps (e.g., fastp, bowtie2 removal of contaminated reads if provided, bbduk.sh removal of contaminated reads if provided)
Assemble reads, align reads to assembly, and count mapped reads
The assembly module optimizes the output for typical metagenomics workflows. In particular, the module does the following: 1) assembles reads using either metaSPAdes, SPAdes, rnaSPAdes, any of the other task-specific assemblers installed with the SPAdes package or MEGAHIT; 2) builds a Bowtie2 index for the scaffolds.fasta (or transcripts.fasta if rnaSPAdes is used); 3) aligns the reads using Bowtie2 to the assembly; 4) pipes the alignment file into Samtools to produce a sorted BAM file (necessary for many coverage applications); 5) counts the reads mapping to each scaffold via featureCounts; and 6) seqkit for useful assembly statistics such as N50, number of scaffolds, and total assembly size. This module automates many critical yet overlooked workflows dealing with assemblies.
Conda Environment: conda activate VEBA-assembly_env
usage: assembly.py -1 <forward_reads.fq> -2 <reverse_reads.fq> -n <name> -o <output_directory>
Running: assembly.py v2023.5.15 via Python v3.9.15 | /expanse/projects/jcl110/anaconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-1 FORWARD_READS, --forward_reads FORWARD_READS
path/to/forward_reads.fq
-2 REVERSE_READS, --reverse_reads REVERSE_READS
path/to/reverse_reads.fq
-n NAME, --name NAME Name of sample
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/assembly]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
--tmpdir TMPDIR Set temporary directory
Assembler arguments:
-P PROGRAM, --program PROGRAM
Assembler | {spades.py, metaspades.py, rnaspades.py, megahit, metaplasmidspades.py, plasmidspades.py, coronaspades.py}} [Default: 'metaspades.py']
-s SCAFFOLD_PREFIX, --scaffold_prefix SCAFFOLD_PREFIX
Assembler | Special options: Use NAME to use --name. Use NONE to not include a prefix. [Default: 'NAME__']
-m MINIMUM_CONTIG_LENGTH, --minimum_contig_length MINIMUM_CONTIG_LENGTH
Minimum contig length. Should be lenient here because longer thresholds can be used for binning downstream. Recommended for metagenomes to use 1000 here. [Default: 1]
--assembler_options ASSEMBLER_OPTIONS
Assembler options for SPAdes-based programs and MEGAHIT (e.g. --arg 1 ) [Default: '']
SPAdes arguments:
--spades_memory SPADES_MEMORY
SPAdes | RAM limit in Gb (terminates if exceeded). [Default: 250]
MEGAHIT arguments:
--megahit_memory MEGAHIT_MEMORY
MEGAHIT | RAM limit in Gb (terminates if exceeded). [Default: 0.9]
Bowtie2 arguments:
--bowtie2_index_options BOWTIE2_INDEX_OPTIONS
bowtie2-build | More options (e.g. --arg 1 ) [Default: '']
--bowtie2_options BOWTIE2_OPTIONS
bowtie2 | More options (e.g. --arg 1 ) [Default: '']
featureCounts arguments:
--featurecounts_options FEATURECOUNTS_OPTIONS
featureCounts | More options (e.g. --arg 1 ) [Default: ''] | http://bioinf.wehi.edu.au/featureCounts/
Output:
- featurecounts.tsv.gz - featureCounts output for contig-level counts
- mapped.sorted.bam - Sorted BAM
- mapped.sorted.bam.bai - Sorted BAM index
- scaffolds.fasta - Assembly scaffolds (preferred over contigs by SPAdes documentation)
- scaffolds.fasta.*.bt2 - Bowtie2 index of scaffolds
- scaffolds.fasta.saf - SAF formatted file for contig-level counts with featureCounts
- seqkit_stats.tsv.gz - Assembly statistics
Assemble reads, align reads to assembly, and count mapped reads (Oxford Nanopore & PacBio)
The assembly-long module uses a similar approach of the assembly module but using packages designed for long reads instead. For instance, instead of SPAdes-based assemblers the module uses Flye and MetaFlye where the sample name is prepended to the contigs. After (meta-)genomic assembly is finished and assembly statistics are computed with SeqKit, long reads are aligned back to the assembly using minimap2 and BAM files are sorted with Samtools. Sorted BAM files are counted using featureCounts in long reads mode.
Conda Environment: conda activate VEBA-assembly_env
usage: assembly-long.py -i <reads.fq[.gz]> -n <name> -o <output_directory>
Running: assembly-long.py v2024.4.29 via Python v3.9.16 | /expanse/projects/jcl110/miniconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-i READS, --reads READS
path/to/reads.fq[.gz]
-n NAME, --name NAME Name of sample
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/assembly]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
--tmpdir TMPDIR Set temporary directory
Assembler arguments:
-P {flye,metaflye}, --program {flye,metaflye}
Assembler | {flye, metaflye}} [Default: 'metaflye']
-s SCAFFOLD_PREFIX, --scaffold_prefix SCAFFOLD_PREFIX
Assembler | Special options: Use NAME to use --name. Use NONE to not include a prefix. [Default: 'NAME__']
-m MINIMUM_CONTIG_LENGTH, --minimum_contig_length MINIMUM_CONTIG_LENGTH
Minimum contig length. Should be lenient here because longer thresholds can be used for binning downstream. Recommended for metagenomes to use 1000 here. [Default: 1]
-t {pacbio-raw,nano-hq,nano-raw,pacbio-corr,nano-corr,pacbio-hifi}, --reads_type {pacbio-raw,nano-hq,nano-raw,pacbio-corr,nano-corr,pacbio-hifi}
Reads type for (meta)flye. {nano-hq, nano-corr, nano-raw, pacbio-hifi, pacbio-corr, pacbio-raw} [Default: nano-hq]
-g ESTIMATED_ASSEMBLY_SIZE, --estimated_assembly_size ESTIMATED_ASSEMBLY_SIZE
Estimated assembly size (e.g., 5m, 2.6g)
--deterministic Use deterministic mode. This will result in a slower assembly and will not be threaded but should produce the same assembly each run.
--assembler_options ASSEMBLER_OPTIONS
Assembler options for Flye-based programs (e.g. --arg 1 ) [Default: '']
MiniMap2 arguments:
--minimap2_preset MINIMAP2_PRESET
MiniMap2 | MiniMap2 preset {map-pb, map-ont, map-hifi} [Default: map-ont]
--minimap2_index_options MINIMAP2_INDEX_OPTIONS
MiniMap2 | More options (e.g. --arg 1 ) [Default: '']
https://github.com/lh3/minimap2
--minimap2_options MINIMAP2_OPTIONS
MiniMap2 | More options (e.g. --arg 1 ) [Default: '']
https://github.com/lh3/minimap2
featureCounts arguments:
--featurecounts_options FEATURECOUNTS_OPTIONS
featureCounts | More options (e.g. --arg 1 ) [Default: ''] | http://bioinf.wehi.edu.au/featureCounts/
Output:
- featurecounts.tsv.gz - featureCounts output for contig-level counts
- mapped.sorted.bam - Sorted BAM
- mapped.sorted.bam.bai - Sorted BAM index
- assembly.fasta - Assembly scaffolds
- assembly.fasta.*.bt2 - Bowtie2 index of scaffolds
- assembly.fasta.saf - SAF formatted file for contig-level counts with featureCounts
- seqkit_stats.tsv.gz - Assembly statistics
Align reads to (concatenated) reference and counts mapped reads
The coverage module further optimizes the output for typical metagenomics workflows. In particular, the module does the following: 1) filters contigs based on a size filter (default 1500 bp); 2) builds a Bowtie2 index for the reference.fasta; 3) aligns the reads from all provided samples using Bowtie2 to the assembly; 4) pipes the alignment file into Samtools to produce a sorted BAM file; 5) counts the reads mapping to each scaffold via featureCounts; and 6) seqkit for useful assembly statistics such as N50, number of scaffolds, and total assembly size. The preferred usage for this module is after prokaryotic, eukaryotic, and viral binning has been performed and the unbinned contigs are merged into a single coassembly used as input. The outputs of this module are expected to be used as a final pass through prokaryotic and eukaryotic binning modules.
Conda Environment: conda activate VEBA-assembly_env
usage: coverage.py -f <reference.fasta> -r <reads.tsv> -o <output_directory>
Running: coverage.py v2023.5.16 via Python v3.9.15 | /expanse/projects/jcl110/anaconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-f FASTA, --fasta FASTA
path/to/reference.fasta. Recommended usage is for merging unbinned contigs. [Required]
-r READS, --reads READS
path/to/reads_table.tsv with the following format: [id\_sample]<tab>[path/to/r1.fastq.gz]<tab>[path/to/r2.fastq.gz], No header
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/project_directory [Default: veba_output/assembly/multisample]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
--tmpdir TMPDIR Set temporary directory
SeqKit seq arguments:
-m MINIMUM_CONTIG_LENGTH, --minimum_contig_length MINIMUM_CONTIG_LENGTH
seqkit seq | Minimum contig length [Default: 1500]
--seqkit_seq_options SEQKIT_SEQ_OPTIONS
seqkit seq | More options (e.g. --arg 1 ) [Default: '']
Bowtie2 arguments:
--bowtie2_index_options BOWTIE2_INDEX_OPTIONS
bowtie2-build | More options (e.g. --arg 1 ) [Default: '']
--one_task_per_cpu Use GNU parallel to run GNU parallel with 1 task per CPU. Useful if all samples are roughly the same size but inefficient if depth varies.
--bowtie2_options BOWTIE2_OPTIONS
bowtie2 | More options (e.g. --arg 1 ) [Default: '']
featureCounts arguments:
--featurecounts_options FEATURECOUNTS_OPTIONS
featureCounts | More options (e.g. --arg 1 ) [Default: ''] | http://bioinf.wehi.edu.au/featureCounts/
Output:
- featurecounts.tsv.gz - featureCounts counts table of all samples
- [sample_id]/mapped.sorted.bam - Sorted BAM file under a subdirectory for each sample
- reference.fasta - Reference fasta (typically this would be the pseudo-coassembly of unbinned contigs)
- reference.fasta.saf - SAF formatted file for contig-level counts with featureCounts
- seqkit_stats.tsv - Assembly statistics
Align reads to a (multi-sample) reference and counts mapped reads (Oxford Nanopore & PacBio)
The coverage-long module follows the adaptation strategy of preprocess-long and assembly-long in which Bowtie2 is replaced with minimap2. The approach for coverage- long is the same as coverage but with tools designed for long reads.
Conda Environment: conda activate VEBA-assembly_env
usage: coverage-long.py -f <reference.fasta> -r <reads.tsv> -o <output_directory>
Running: coverage-long.py v2024.4.29 via Python v3.9.16 | /expanse/projects/jcl110/miniconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-f FASTA, --fasta FASTA
path/to/reference.fasta. Recommended usage is for merging unbinned contigs. [Required]
-r READS, --reads READS
path/to/reads_table.tsv with the following format: [id_sample]<tab>[path/to/reads.fastq.gz], No header
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/project_directory [Default: veba_output/assembly/multisample]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
--tmpdir TMPDIR Set temporary directory
SeqKit seq arguments:
-m MINIMUM_CONTIG_LENGTH, --minimum_contig_length MINIMUM_CONTIG_LENGTH
seqkit seq | Minimum contig length [Default: 1]
--seqkit_seq_options SEQKIT_SEQ_OPTIONS
seqkit seq | More options (e.g. --arg 1 ) [Default: '']
Minmap2 arguments:
--minimap2_preset MINIMAP2_PRESET
MiniMap2 | MiniMap2 preset {map-pb, map-ont, map-hifi} [Default: map-ont]
--minimap2_index_options MINIMAP2_INDEX_OPTIONS
Minimap2 | More options (e.g. --arg 1 ) [Default: '']
--minimap2_options MINIMAP2_OPTIONS
Minimap2 | More options (e.g. --arg 1 ) [Default: '']
featureCounts arguments:
--featurecounts_options FEATURECOUNTS_OPTIONS
featureCounts | More options (e.g. --arg 1 ) [Default: ''] | http://bioinf.wehi.edu.au/featureCounts/
Output:
- featurecounts.tsv.gz - featureCounts counts table of all samples
- [sample_id]/mapped.sorted.bam - Sorted BAM file under a subdirectory for each sample
- reference.fasta - Reference fasta (typically this would be the pseudo-coassembly of unbinned contigs)
- reference.fasta.saf - SAF formatted file for contig-level counts with featureCounts
- seqkit_stats.tsv - Assembly statistics
Iterative consensus binning for recovering prokaryotic genomes with lineage-specific quality assessment
The prokaryotic binning module implements a novel iterative consensus binning procedure that uses CoverM for fast coverage calculations, multiple binning algorithms (MaxBin2 (marker set = 107); MaxBin2 (marker set = 40); MetaBat2; and CONCOCT, consensus dereplication and aggregate binning with DAS Tool, the consensus domain wrapper for Tiara for removing eukaryotes at the MAG level, and CheckM2 for quality assessment where poor quality MAGs are removed (e.g., completeness < 50% and/or contamination ≥ 10) which has direct support for candidate phyla radiation (CPR). The novelty of this procedure is that the unbinned contigs are stored and fed back into the input of the binning procedure using a separate random seed state allowing for an exhaustive, yet effective, approach in extracting high quality and difficult to bin genomes; number of iterations specified by --n_iter option. Gene calls are performed using Pyrodigal and the gene models (GFF3 Format) are modified to include gene and contig identifiers for use with downstream feature counting software. BARRNAP and tRNAscan-SE are used for rRNA and tRNA detetion, respectively. Lastly, genome assembly a gene statistics such as GC, N50, number of scaffolds, and genome size are calculated using seqkit. MAG naming scheme for prokaryotes follows [SAMPLE]__[ALGORITHM]__P.[ITERATION]__[NAME] (e.g., SRR17458623__METABAT2__P.1__bin.1)
- If you have a lot of samples and a lot of contigs then use the
--skip_maxbin2flag because it takes MUCH longer to run. For the Plastisphere it was going to take 40 hours perMaxBin2run (there are 2MaxBin2runs) per iteration.Metabat2andCONCOCTcan do the heavy lifting much faster and often with better results so it's recommended to skipMaxBin2for larger datasets.
Conda Environment: conda activate VEBA-binning-prokaryotic_env
usage: binning-prokaryotic.py -f <scaffolds.fasta> -b <mapped.sorted.bam> -n <name> -o <output_directory>
Running: binning-prokaryotic.py v2023.7.7 via Python v3.9.15 | /expanse/projects/jcl110/anaconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-f FASTA, --fasta FASTA
path/to/scaffolds.fasta
-b BAM [BAM ...], --bam BAM [BAM ...]
path/to/mapped.sorted.bam files separated by spaces.
-n NAME, --name NAME Name of sample
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/binning/prokaryotic/]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
-I N_ITER, --n_iter N_ITER
Number of iterations to run binning. Use -1 for brute force (-1 isn't ready yet) [Default: 3]
--random_state RANDOM_STATE
Use -1 for completely random. Use 0 for consecutive random states. Use any other positive integer for the same random state for all iterations [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
--tmpdir TMPDIR Set temporary directory
Database arguments:
--veba_database VEBA_DATABASE
VEBA database location. [Default: $VEBA_DATABASE environment variable]
Binning arguments:
-m MINIMUM_CONTIG_LENGTH, --minimum_contig_length MINIMUM_CONTIG_LENGTH
Minimum contig length. Anything under 2500 will default to 2500 for MetaBat2 [Default: 1500]
-s MINIMUM_GENOME_LENGTH, --minimum_genome_length MINIMUM_GENOME_LENGTH
Minimum genome length. [Default: 150000]
--concoct_fragment_length CONCOCT_FRAGMENT_LENGTH
CONCOCT | Fragment length [Default: 10000]
--concoct_overlap_length CONCOCT_OVERLAP_LENGTH
CONCOCT | Fragment overlap length [Default: 0]
--skip_maxbin2 MaxBin2 | Skip MaxBin2. Useful when there's many contigs/scaffolds
--skip_concoct CONCOCT | Skip CONCOCT. Skip CONCOCT. Useful when there's many BAM files
--maxbin2_options MAXBIN2_OPTIONS
MaxBin2 | More options (e.g. --arg 1 ) [Default: ''] | https://sourceforge.net/projects/maxbin/
--metabat2_options METABAT2_OPTIONS
MetaBat2 | More options (e.g. --arg 1 ) [Default: ''] | https://bitbucket.org/berkeleylab/metabat/src/master/
--concoct_options CONCOCT_OPTIONS
CONCOCT | More options (e.g. --arg 1 ) [Default: '']
Gene model arguments:
--pyrodigal_minimum_gene_length PYRODIGAL_MINIMUM_GENE_LENGTH
Pyrodigal | Minimum gene length [Default: 90]
--pyrodigal_minimum_edge_gene_length PYRODIGAL_MINIMUM_EDGE_GENE_LENGTH
Pyrodigal | Minimum edge gene length [Default: 60]
--pyrodigal_maximum_gene_overlap_length PYRODIGAL_MAXIMUM_GENE_OVERLAP_LENGTH
Pyrodigal | Maximum gene overlap length [Default: 60]
--pyrodigal_genetic_code PYRODIGAL_GENETIC_CODE
Pyrodigal -g translation table [Default: 11]
Evaluation arguments:
--dastool_searchengine DASTOOL_SEARCHENGINE
DAS_Tool searchengine. [Default: diamond] | https://github.com/cmks/DAS_Tool
--dastool_minimum_score DASTOOL_MINIMUM_SCORE
DAS_Tool score_threshold. Score threshold until selection algorithm will keep selecting bins. This is set to a relaxed setting because CheckM2 is run post hoc. [Default: 0.1] | https://github.com/cmks/DAS_Tool
--dastool_options DASTOOL_OPTIONS
DAS_Tool | More options (e.g. --arg 1 ) [Default: ''] | https://github.com/cmks/DAS_Tool
--checkm2_completeness CHECKM2_COMPLETENESS
CheckM2 completeness threshold [Default: 50.0]
--checkm2_contamination CHECKM2_CONTAMINATION
CheckM2 contamination threshold [Default: 10.0]
--checkm2_options CHECKM2_OPTIONS
CheckM lineage_wf | More options (e.g. --arg 1 ) [Default: '']
barrnap arguments:
--barrnap_length_cutoff BARRNAP_LENGTH_CUTOFF
barrnap | Proportional length threshold to label as partial [Default: 0.8]
--barrnap_reject BARRNAP_REJECT
barrnap | Proportional length threshold to reject prediction [Default: 0.25]
--barrnap_evalue BARRNAP_EVALUE
barrnap | Similarity e-value cut-off [Default: 1e-6]
tRNAscan-SE arguments:
--trnascan_options TRNASCAN_OPTIONS
tRNAscan-SE | More options (e.g. --arg 1 ) [Default: ''] | https://github.com/UCSC-LoweLab/tRNAscan-SE
featureCounts arguments:
--long_reads featureCounts | Use this if long reads are being used
--featurecounts_options FEATURECOUNTS_OPTIONS
featureCounts | More options (e.g. --arg 1 ) [Default: ''] | http://bioinf.wehi.edu.au/featureCounts/
Domain classification arguments:
--logit_transform LOGIT_TRANSFORM
Transformation for consensus_domain_classification: {softmax, tss} [Default: softmax]
--tiara_minimum_length TIARA_MINIMUM_LENGTH
Tiara | Minimum contig length. Anything lower than 3000 is not recommended. [Default: 3000]
--tiara_options TIARA_OPTIONS
Tiara | More options (e.g. --arg 1 ) [Default: ''] | https://github.com/ibe-uw/tiara
Output:
- binned.list - List of binned contigs
- bins.list - List of MAG identifiers
- checkm2_results.filtered.tsv - Filtered CheckM2 output
- featurecounts.orfs.tsv.gz - ORF-level counts table
- genome_statistics.tsv - Genome assembly statistics
- gene_statistics.cds.tsv - Gene sequence statistics (CDS)
- gene_statistics.rRNA.tsv - Gene sequence statistics (rRNA)
- gene_statistics.tRNA.tsv - Gene sequence statistics (tRNA)
- genomes/ - MAG subdirectory
- genomes/[id_genome].fa - MAG assembly fasta
- genomes/[id_genome].faa - MAG protein fasta
- genomes/[id_genome].ffn - MAG CDS fasta
- genomes/[id_genome].gff - MAG gene models for assembly, CDS, rRNA, and tRNA
- genomes/[id_genome].rRNA - MAG rRNA fasta
- genomes/[id_genome].tRNA - MAG tRNA fasta
- genomes/identifier_mapping.tsv - Identifier mapping between [id_orf, id_contig, id_mag]
- scaffolds_to_bins.tsv - Identifier mapping between [id_contig, id_mag]
- unbinned.fasta - Fasta of unbinned contigs that have passed length thresholding
- unbinned.list - List of unbinned contigs
Binning for recovering eukaryotic genomes with exon-aware gene modeling and lineage-specific quality assessment
The eukaryotic binning module uses several checks and state-of-the-art software to ensure high quality genomes. In particular, non-prokaryotic-biased binning algorithms MetaBat2 [default] (coverage calculated with CoverM) or CONCOCT (coverage calculated using CONCOCT scripts) is used for binning out genomes followed by a genome size filter (2,000,000 bp is the default). The preliminary bins are run through the consensus domain wrapper for Tiara to predict eukaryotic MAGs. Contigs from the eukaryotic MAGs are input into MetaEuk easy-predict workflow using our custom consensus eukaryotic database. Although MetaEuk is a high-quality software suite, the identifiers from MetaEuk are very complex, long, and contain characters that are often problematic for downstream applications including parsing, file naming systems, and certain programs with simplified identifier requirements such as Anvi’o. In addition, the gene model GFF files are not intuitive, compatible with P(y)rodigal(-gv) GFF files or featureCounts without major modification. Therefore, we developed an essential wrapper for MetaEuk that simplifies identifiers (i.e., [ContigID]_[GeneStart]:[GeneEnd]([strand])), ensuring no duplicates are produced, creates a GFF file that can be concatenated with the Pyrodigal GFF file for use with featureCounts, and several identifier mapping tables to seamless convert between original and modified identifiers. BARRNAP and tRNAscan-SE are used for rRNA and tRNA detetion, respectively. Tiara is used to identfy mitochondrion and plastid contigs to run separate workflows for modeling genes (CDS, rRNA, and tRNA). Lineage-specific genome quality estimation is performed using BUSCO where poor quality MAGs are removed (e.g., completeness < 50%, contamination > 10). Gene counts are computed using featureCounts at the gene level. Lastly, genome and gene statistics such as N50, number of scaffolds, and genome size are calculated using seqkit. Iterative binning is not currently available as no consensus binning tool is available therefore iterative binning would result in diminishing returns. MAG naming scheme for eukaryotes follows [SAMPLE]__[ALGORITHM]__E.[ITERATION]__[NAME] (e.g., ERR2002407__METABAT2__E.1__bin.2).
Conda Environment: conda activate VEBA-binning-eukaryotic_env
usage: binning-eukaryotic.py -f <scaffolds.fasta> -b <mapped.sorted.bam> -n <name> -o <output_directory>
Running: binning-eukaryotic.py v2023.7.6 via Python v3.9.15 | /expanse/projects/jcl110/anaconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
I/O arguments:
-f FASTA, --fasta FASTA
path/to/scaffolds.fasta
-b BAM [BAM ...], --bam BAM [BAM ...]
path/to/mapped.sorted.bam files separated by spaces.
-n NAME, --name NAME Name of sample
-l CONTIG_IDENTIFIERS, --contig_identifiers CONTIG_IDENTIFIERS
path/to/contigs.list [Optional]
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/binning/eukaryotic]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
Database arguments:
--veba_database VEBA_DATABASE
VEBA database location. [Default: $VEBA_DATABASE environment variable]
Binning arguments:
-a ALGORITHM, --algorithm ALGORITHM
Binning algorithm: {concoct, metabat2} [Default: metabat2]
-m MINIMUM_CONTIG_LENGTH, --minimum_contig_length MINIMUM_CONTIG_LENGTH
Minimum contig length. [Default: 1500]
-s MINIMUM_GENOME_LENGTH, --minimum_genome_length MINIMUM_GENOME_LENGTH
Minimum genome length. [Default: 2000000]
--concoct_fragment_length CONCOCT_FRAGMENT_LENGTH
CONCOCT | Fragment length [Default: 10000]
--concoct_overlap_length CONCOCT_OVERLAP_LENGTH
CONCOCT | Fragment overlap length [Default: 0]
--concoct_options CONCOCT_OPTIONS
CONCOCT | More options (e.g. --arg 1 ) [Default: '']
--metabat2_options METABAT2_OPTIONS
MetaBat2 | More options (e.g. --arg 1 ) [Default: ''] | https://bitbucket.org/berkeleylab/metabat/src/master/
Domain classification arguments:
--logit_transform LOGIT_TRANSFORM
Transformation for consensus_domain_classification: {softmax, tss} [Default: softmax
--tiara_minimum_length TIARA_MINIMUM_LENGTH
Tiara | Minimum contig length. Anything lower than 3000 is not recommended. [Default: 3000]
--tiara_options TIARA_OPTIONS
Tiara | More options (e.g. --arg 1 ) [Default: ''] | https://github.com/ibe-uw/tiara
MetaEuk arguments:
--metaeuk_sensitivity METAEUK_SENSITIVITY
MetaEuk | Sensitivity: 1.0 faster; 4.0 fast; 7.5 sensitive [Default: 4.0]
--metaeuk_evalue METAEUK_EVALUE
MetaEuk | List matches below this E-value (range 0.0-inf) [Default: 0.01]
--metaeuk_options METAEUK_OPTIONS
MetaEuk | More options (e.g. --arg 1 ) [Default: ''] https://github.com/soedinglab/metaeuk
Pyrodigal arguments (Mitochondria):
--pyrodigal_minimum_gene_length PYRODIGAL_MINIMUM_GENE_LENGTH
Pyrodigal | Minimum gene length [Default: 90]
--pyrodigal_minimum_edge_gene_length PYRODIGAL_MINIMUM_EDGE_GENE_LENGTH
Pyrodigal | Minimum edge gene length [Default: 60]
--pyrodigal_maximum_gene_overlap_length PYRODIGAL_MAXIMUM_GENE_OVERLAP_LENGTH
Pyrodigal | Maximum gene overlap length [Default: 60]
--pyrodigal_mitochondrial_genetic_code PYRODIGAL_MITOCHONDRIAL_GENETIC_CODE
Pyrodigal -g translation table (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi/) [Default: 4] (The Mold, Protozoan, and Coelenterate Mitochondrial Code and the Mycoplasma/Spiroplasma Code))
--pyrodigal_plastid_genetic_code PYRODIGAL_PLASTID_GENETIC_CODE
Pyrodigal -g translation table (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi/) [Default: 11] (The Bacterial, Archaeal and Plant Plastid Code))
BUSCO arguments:
--busco_completeness BUSCO_COMPLETENESS
BUSCO completeness [Default: 50.0]
--busco_contamination BUSCO_CONTAMINATION
BUSCO contamination [Default: 10.0]
--busco_evalue BUSCO_EVALUE
BUSCO | E-value cutoff for BLAST searches. Allowed formats, 0.001 or 1e-03 [Default: 1e-03]
barrnap arguments:
--barrnap_length_cutoff BARRNAP_LENGTH_CUTOFF
barrnap | Proportional length threshold to label as partial [Default: 0.8]
--barrnap_reject BARRNAP_REJECT
barrnap | Proportional length threshold to reject prediction [Default: 0.25]
--barrnap_evalue BARRNAP_EVALUE
barrnap | Similarity e-value cut-off [Default: 1e-6]
tRNAscan-SE arguments:
--trnascan_nuclear_options TRNASCAN_NUCLEAR_OPTIONS
tRNAscan-SE | More options (e.g. --arg 1 ) [Default: ''] | https://github.com/UCSC-LoweLab/tRNAscan-SE
--trnascan_mitochondrial_searchmode TRNASCAN_MITOCHONDRIAL_SEARCHMODE
tRNAscan-SE | Search mode [Default: '-O'] | Current best option according to developer: https://github.com/UCSC-LoweLab/tRNAscan-SE/issues/24
--trnascan_mitochondrial_options TRNASCAN_MITOCHONDRIAL_OPTIONS
tRNAscan-SE | More options (e.g. --arg 1 ) [Default: ''] | https://github.com/UCSC-LoweLab/tRNAscan-SE
--trnascan_plastid_searchmode TRNASCAN_PLASTID_SEARCHMODE
tRNAscan-SE | Search mode [Default: '-O'] | https://github.com/UCSC-LoweLab/tRNAscan-SE
--trnascan_plastid_options TRNASCAN_PLASTID_OPTIONS
tRNAscan-SE | More options (e.g. --arg 1 ) [Default: ''] | https://github.com/UCSC-LoweLab/tRNAscan-SE
featureCounts arguments:
--long_reads featureCounts | Use this if long reads are being used
--featurecounts_options FEATURECOUNTS_OPTIONS
featureCounts | More options (e.g. --arg 1 ) [Default: ''] | http://bioinf.wehi.edu.au/featureCounts/
Output:
- binned.list - List of binned contigs
- bins.list - List of MAG identifiers
- busco_results.filtered.tsv - Filtered BUSCO output
- featurecounts.orfs.tsv.gz - ORF-level counts table
- genome_statistics.tsv - Genome assembly statistics
- gene_statistics.cds.tsv - Gene sequence statistics (CDS)
- gene_statistics.rRNA.tsv - Gene sequence statistics (rRNA)
- gene_statistics.tRNA.tsv - Gene sequence statistics (tRNA)
- genomes/ - MAG subdirectory
- genomes/[id_genome].fa - MAG assembly fasta
- genomes/[id_genome].faa - MAG protein fasta
- genomes/[id_genome].ffn - MAG CDS fasta
- genomes/[id_genome].gff - MAG gene models for assembly, CDS, rRNA, and tRNA
- genomes/[id_genome].rRNA - MAG rRNA fasta
- genomes/[id_genome].tRNA - MAG tRNA fasta
- genomes/[id_genome].seqtype.tsv - Identifier mapping between [id_contig, sequence_type] {nuclear, mitochondrion, plastid}
- genomes/identifier_mapping.tsv - Identifier mapping between [id_orf, id_contig, id_mag]
- identifier_mapping.metaeuk.tsv - Identifier mapping between original MetaEuk identifiers and modified identifiers. Includes fully parsed MetaEuk identifiers.
- scaffolds_to_bins.tsv - Identifier mapping between [id_contig, id_mag]
- unbinned.fasta - Fasta of unbinned contigs that have passed length thresholding
- unbinned.list - List of unbinned contigs
Detection of viral genomes and quality assessment
Viral binning is performed using either geNomad [default] or VirFinder to extract potential viral contigs (e.g., P < 0.05). The potential viral contigs are then input into CheckV where quality assessment removes poor quality or low confidence viral predictions. The filtering scheme is based on author recommendations in which a viral contig is considered if it meets the following criteria: 1) number of viral genes ≥ 5 x number of host genes; 2) completeness ≥ 50%; 3) CheckV quality is either medium-quality, high-quality, or complete; and 4) MIUViG quality is either medium-quality, high-quality, or complete. Proviruses can be included by using the --include_proviruses flag. After poor quality viral contigs are removed, Prodigal-GV is used for gene modeling and seqkit is used for useful genome statistics. Iterative binning is not applicable for viral detection as algorithms are executed on a per-contig basis and all viral genomes will be identified on first pass. MAG naming scheme for viruses follows [SAMPLE]__[ALGORITHM]__[NAME] (e.g., SRR9668957__GENOMAD__Virus.1).
Conda Environment: conda activate VEBA-binning-viral_env
usage: binning-viral.py -f <scaffolds.fasta> -l <contig_identifiers> -n <name> -o <output_directory> [Requires at least 20GB]
Running: binning-viral.py v2023.7.7 via Python v3.9.15 | /expanse/projects/jcl110/anaconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-f FASTA, --fasta FASTA
path/to/scaffolds.fasta
-n NAME, --name NAME Name of sample
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/binning/viral]
-b BAM [BAM ...], --bam BAM [BAM ...]
path/to/mapped.sorted.bam files separated by spaces.
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
Database arguments:
--veba_database VEBA_DATABASE
VEBA database location. [Default: $VEBA_DATABASE environment variable]
Binning arguments:
-a ALGORITHM, --algorithm ALGORITHM
Binning algorithm to use: {genomad, virfinder} [Default: genomad]
-m MINIMUM_CONTIG_LENGTH, --minimum_contig_length MINIMUM_CONTIG_LENGTH
Minimum contig length. [Default: 1500]
--include_provirus_detection
Include provirus viral detection
Gene model arguments:
--prodigal_genetic_code PRODIGAL_GENETIC_CODE
Prodigal-GV -g translation table (https://github.com/apcamargo/prodigal-gv) [Default: 11]
geNomad arguments
Using --relaxed mode by default. Adjust settings according to the following table: https://portal.nersc.gov/genomad/post_classification_filtering.html#default-parameters-and-presets:
--genomad_qvalue GENOMAD_QVALUE
Maximum accepted false discovery rate. [Default: 1.0; 0.0 < x ≤ 1.0]
--sensitivity SENSITIVITY
MMseqs2 marker search sensitivity. Higher values will annotate more proteins, but the search will be slower and consume more memory. [Default: 4.0; x ≥ 0.0]
--splits SPLITS Split the data for the MMseqs2 search. Higher values will reduce memory usage, but will make the search slower. If the MMseqs2 search is failing, try to increase the number of splits. Also used for VirFinder. [Default: 0; x ≥ 0]
--composition COMPOSITION
Method for estimating sample composition. (auto|metagenome|virome) [Default: auto]
--minimum_score MINIMUM_SCORE
Minimum score to flag a sequence as virus or plasmid. By default, the sequence is classified as virus/plasmid if its virus/plasmid score is higher than its chromosome score, regardless of the value. [Default: 0; 0.0 ≤ x ≤ 1.0]
--minimum_plasmid_marker_enrichment MINIMUM_PLASMID_MARKER_ENRICHMENT
Minimum allowed value for the plasmid marker enrichment score, which represents the total enrichment of plasmid markers in the sequence. Sequences with multiple plasmid markers will have higher values than the ones that encode few or no markers.[Default: -100]
--minimum_virus_marker_enrichment MINIMUM_VIRUS_MARKER_ENRICHMENT
Minimum allowed value for the virus marker enrichment score, which represents the total enrichment of plasmid markers in the sequence. Sequences with multiple plasmid markers will have higher values than the ones that encode few or no markers. [Default: -100]
--minimum_plasmid_hallmarks MINIMUM_PLASMID_HALLMARKS
Minimum number of plasmid hallmarks in the identified plasmids. [Default: 0; x ≥ 0]
--minimum_virus_hallmarks MINIMUM_VIRUS_HALLMARKS
Minimum number of virus hallmarks in the identified viruses. [Default: 0; x ≥ 0]
--maximum_universal_single_copy_genes MAXIMUM_UNIVERSAL_SINGLE_COPY_GENES
Maximum allowed number of universal single copy genes (USCGs) in a virus or a plasmid. Sequences with more than this number of USCGs will not be classified as viruses or plasmids, regardless of their score. [Default: 100]
--genomad_options GENOMAD_OPTIONS
geNomad | More options (e.g. --arg 1 ) [Default: '']
VirFinder arguments:
--virfinder_pvalue VIRFINDER_PVALUE
VirFinder statistical test threshold [Default: 0.05]
--mmseqs2_evalue MMSEQS2_EVALUE
Maximum accepted E-value in the MMseqs2 search. Used by genomad annotate when VirFinder is used as binning algorithm [Default: 1e-3]
--use_qvalue Use qvalue (FDR) instead of pvalue
--use_minimal_database_for_taxonomy
Use a smaller marker database to annotate proteins. This will make execution faster but sensitivity will be reduced.
--virfinder_options VIRFINDER_OPTIONS
VirFinder | More options (e.g. --arg 1 ) [Default: '']
CheckV arguments:
--checkv_options CHECKV_OPTIONS
CheckV | More options (e.g. --arg 1 ) [Default: '']
--multiplier_viral_to_host_genes MULTIPLIER_VIRAL_TO_HOST_GENES
Minimum number of viral genes [Default: 5]
--checkv_completeness CHECKV_COMPLETENESS
Minimum completeness [Default: 50.0]
--checkv_quality CHECKV_QUALITY
Comma-separated string of acceptable arguments between {High-quality,Medium-quality,Complete} [Default: High-quality,Medium-quality,Complete]
--miuvig_quality MIUVIG_QUALITY
Comma-separated string of acceptable arguments between {High-quality,Medium-quality,Complete} [Default: High-quality,Medium-quality,Complete]
featureCounts arguments:
--long_reads featureCounts | Use this if long reads are being used
--featurecounts_options FEATURECOUNTS_OPTIONS
featureCounts | More options (e.g. --arg 1 ) [Default: ''] | http://bioinf.wehi.edu.au/featureCounts/
Output:
- binned.list - List of binned contigs
- bins.list - List of MAG identifiers
- checkv_results.filtered.tsv - Filtered CheckV output
- featurecounts.orfs.tsv.gz - ORF-level counts table (If --bam file is provided)
- genome_statistics.tsv - Genome assembly statistics
- gene_statistics.cds.tsv - Gene sequence statistics (CDS)
- genomes/ - MAG subdirectory
- genomes/[id_genome].fa - MAG assembly fasta
- genomes/[id_genome].faa - MAG protein fasta
- genomes/[id_genome].ffn - MAG CDS fasta
- genomes/[id_genome].gff - MAG gene models
- genomes/identifier_mapping.tsv - Identifier mapping between [id_orf, id_contig, id_mag]
- scaffolds_to_bins.tsv - Identifier mapping between [id_contig, id_mag]
- unbinned.fasta - Fasta of unbinned contigs that have passed length thresholding
- unbinned.list - List of unbinned contigs
Taxonomic classification of prokaryotic genomes
The prokaryotic classification module is a useful wrapper around GTDB-Tk which either combines the resulting archaea and bacteria summary tables or runs GTDB-Tk lineage_wf from the beginning. If genome clusters are provided, then it performs consensus lineage classification. Krona plots are generated showing taxonomic levels.
Conda Environment: conda activate VEBA-classify-prokaryotic_env
usage: classify-prokaryotic.py -i <prokaryotic_binning_directory>|-g <genomes.list> -o <output_directory>
Running: classify-prokaryotic.py v2023.6.16 via Python v3.9.15 | /expanse/projects/jcl110/anaconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-i PROKARYOTIC_BINNING_DIRECTORY, --prokaryotic_binning_directory PROKARYOTIC_BINNING_DIRECTORY
path/to/prokaryotic_binning_directory [Cannot be used with --genomes]
-g GENOMES, --genomes GENOMES
path/to/genomes.list [Cannot be ued with --prokaryotic_binning_directory]
-c CLUSTERS, --clusters CLUSTERS
path/to/clusters.tsv, Format: [id_mag]<tab>[id_cluster], No header.
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/output_directory [Default: veba_output/classify/prokaryotic]
-x EXTENSION, --extension EXTENSION
Fasta file extension for genomes if a list is provided [Default: fa]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
--tmpdir TMPDIR path/to/TMPDIR
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
Database arguments:
--veba_database VEBA_DATABASE
VEBA database location. [Default: $VEBA_DATABASE environment variable]
GTDB-Tk arguments:
--skip_ani_screen Skip ANI screen [Default: Don't skip ANI screen]
--gtdbtk_options GTDBTK_OPTIONS
GTDB-Tk | classify_wf options (e.g. --arg 1 ) [Default: '']
Consensus genome classification arguments:
-l LENIENCY, --leniency LENIENCY
Leniency parameter. Lower value means more conservative weighting. A value of 1 indiciates no weight bias. A value greater than 1 puts higher weight on higher level taxonomic assignments. A value less than 1 puts lower weights on higher level taxonomic assignments. [Default: 1.382]
Output:
- taxonomy.tsv - Prokaryotic genome classification based on GTDBTk
- taxonomy.clusters.tsv - Prokaryotic cluster classification (If --clusters are provided)
- krona.html - Krona plot for various taxonomic levels
Taxonomic classification of eukaryotic genomes
The eukaryotic classification module can be performed on de novo genomes or utilize the target field of MetaEuk gene identifiers and the taxonomic lineage associated with each source genome. The default marker set is eukaryote_odb10 from BUSCO but custom marker sets are support along with the inclusion of all genes not just marker genes. An option to include marker-specific noise cutoff scores is also available using the --scores_cutoff parameter which is default behavior with BUSCO’s eukaryote_odb10 provided noise thresholds. For each MAG, bitscores are accumulated for each taxonomic level and taxonomy is assigned with leniency specified by the leniency parameter with high leniency resulting higher order taxonomic assignments. If genome clusters are provided, then it performs consensus lineage classification. Krona plots are generated showing taxonomic levels.
Conda Environment: conda activate VEBA-classify-eukaryotic_env
usage: classify-eukaryotic.py -i <eukaryotic_binning_directory>|-g <genomes.list> -o <output_directory>
Running: classify-eukaryotic.py v2024.6.9 via Python v3.10.14 | /Users/jolespin/miniconda3/envs/test_env/bin/python
options:
-h, --help show this help message and exit
I/O arguments:
-c CLUSTERS, --clusters CLUSTERS
path/to/clusters.tsv, Format: [id_mag]<tab>[id_cluster], No header.
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/output_directory [Default: veba_output/classify/eukaryotic]
-x EXTENSION, --extension EXTENSION
path/to/output_directory. Does not support gzipped. [Default: fa]
-m {directory,auto,genomes,manual}, --mode {directory,auto,genomes,manual}
{directory, genomes, manual, auto} [Default: auto]
[mode=binning_directory] arguments:
-i EUKARYOTIC_BINNING_DIRECTORY, --eukaryotic_binning_directory EUKARYOTIC_BINNING_DIRECTORY
path/to/eukaryotic_binning_directory [Cannot be used with --mode genomes or manual]
[mode=genomes] arguments:
-g GENOMES, --genomes GENOMES
path/to/genomes.list where each line is a path to a genome.fasta [Cannot be used with --mode binning_directory or manual]
[mode=manual] arguments:
-a PROTEINS, --proteins PROTEINS
path/to/concatenated_proteins.faa [Cannot be used with --model _binning_directory or genomes]
-t IDENTIFIER_MAPPING_METAEUK, --identifier_mapping_metaeuk IDENTIFIER_MAPPING_METAEUK
path/to/identifier_mapping.metaeuk.tsv [Cannot be used with --model _binning_directory or genomes]
-s SCAFFOLDS_TO_BINS, --scaffolds_to_bins SCAFFOLDS_TO_BINS
path/to/scaffolds_to_bins.tsv [Cannot be used with --model _binning_directory or genomes]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
Database arguments:
--veba_database VEBA_DATABASE
VEBA database location. [Default: $VEBA_DATABASE environment variable]
MetaEuk arguments:
--metaeuk_sensitivity METAEUK_SENSITIVITY
MetaEuk | Sensitivity: 1.0 faster; 4.0 fast; 7.5 sensitive [Default: 4.0]
--metaeuk_evalue METAEUK_EVALUE
MetaEuk | List matches below this E-value (range 0.0-inf) [Default: 0.01]
--metaeuk_split_memory_limit METAEUK_SPLIT_MEMORY_LIMIT
MetaEuk | Set max memory per split. E.g. 800B, 5K, 10M, 1G. Use 0 to use all available system memory. (Default value is experimental) [Default: 36G]
--metaeuk_options METAEUK_OPTIONS
MetaEuk | More options (e.g. --arg 1 ) [Default: ''] https://github.com/soedinglab/metaeuk
Consensus genome arguments:
-L LENIENCY_GENOME_CLASSIFICATION, --leniency_genome_classification LENIENCY_GENOME_CLASSIFICATION
Leniency parameter for genomes. Lower value means more conservative weighting. A value of 1 indiciates no weight bias. A value greater than 1 puts higher weight on higher level taxonomic assignments. A value less than 1 puts lower weights on higher level taxonomic assignments. [Default: 1.0]
-l LENIENCY, --leniency LENIENCY
Leniency parameter for genome cluster. Lower value means more conservative weighting. A value of 1 indiciates no weight bias. A value greater than 1 puts higher weight on higher level taxonomic assignments. A value less than 1 puts lower weights on higher level taxonomic assignments. [Default: 1.0]
--similarity_threshold SIMILARITY_THRESHOLD
Threshold for similarity analysis [Default: 0.8]
--retain_unannotated RETAIN_UNANNOTATED
Consider unannotations (i.e., blank functions) in the scording system [Default: 1]
--unannotated_weight UNANNOTATED_WEIGHT
Weight for unannotations (i.e., blank functions) in the scording system? [Default: 0.382]
--representative_threshold REPRESENTATIVE_THRESHOLD
Score to consider as representative [Default: 0.618]
-b BLACKLIST, --blacklist BLACKLIST
Comma-separated list of [taxon_level]:[blacklisted label]. Use 'NONE' for no black listed taxa. [Default: species:uncultured eukaryote]
Output:
- taxonomy.tsv - Eukaryotic genome classification based on microeukaryotic protein database and BUSCO's eukaryota_odb10 marker set
- taxonomy.clusters.tsv - Eukaryotic cluster classification (If --clusters are provided)
- gene-source_lineage.tsv - Gene source lineage and scores for classifying MAGs [id_gene, id_scaffold, id_mag, id_target, id_source, lineage, bitscore]
- krona.html - Krona plot for various taxonomic levels
Taxonomic classification for viral genomes
Viral classification uses geNomad's taxonomy module.
Conda Environment: conda activate VEBA-classify-viral_env
usage: classify-viral.py -i <viral_binning_directory>|-g <genomes.list> -o <output_directory>
Running: classify-viral.py v2023.5.8 via Python v3.9.15 | /expanse/projects/jcl110/anaconda3/envs/VEBA-preprocess_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-i VIRAL_BINNING_DIRECTORY, --viral_binning_directory VIRAL_BINNING_DIRECTORY
Either: path/to/checkv/quality_summary.tsv or directory of veba_output/binning/viral
-g GENOMES, --genomes GENOMES
path/to/genomes.list where each line is a path to a genome.fasta [Cannot be ued with --viral_binning_directory]
-c CLUSTERS, --clusters CLUSTERS
path/to/clusters.tsv, Format: [id_mag]<tab>[id_cluster], No header.
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/output_directory [Default: veba_output/classify/viral]
-x EXTENSION, --extension EXTENSION
path/to/output_directory. Does not support gzipped. [Default: fa]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
Database arguments:
--veba_database VEBA_DATABASE
VEBA database location. [Default: $VEBA_DATABASE environment variable]
Consensus genome classification arguments:
-t THRESHOLD, --threshold THRESHOLD
Fraction of classifications for consensus [Default: 0.5]
Output:
- taxonomy.tsv - Viral genome classification based on geNomad classifications
- taxonomy.clusters.tsv - Viral cluster classification (If --clusters are provided)
Species-level clustering of genomes and lineage-specific orthogroup detection
To leverage intra-sample genome analysis in an inter-sample analytical paradigm, genome clustering and lineage-specific orthogroup detection is necessary. The cluster module utilizes 2 separate wrappers for global (inter-sample) and local (intra-sample) clustering. FastANI is used to compute pairwise ANI and these are used to construct a NetworkX graph object where nodes are genomes and edges are ANI values. This graph is converted into subgraphs of connected components whose edges are connected by a particular threshold such as 95% ANI [default] as recommended by the authors for species-level clustering. These species-level clusters (SLC) are then partitioned and MMSEQS2 is then run on each SLC panproteome to SLC-specific Protein Clusters (SSPC) previously referred to as Sample-specific Orthogroups (SSO). Pangenome tables are created for each SLC which includes the number of proteins for a protein-cluster are detected in each genome. Each domain (i.e., prokaryotic, eukaryotic, viral) are clustered separately.
Conda Environment: conda activate VEBA-cluster_env
usage: cluster.py -i <genomes_table.tsv> -o <output_directory> -A 95 -a mmseqs-cluster
Running: cluster.py v2024.3.26 via Python v3.11.0 | /expanse/projects/jcl110/miniconda3/envs/VEBA-cluster_env/bin/python
options:
-h, --help show this help message and exit
Required I/O arguments:
-i GENOMES_TABLE, --genomes_table GENOMES_TABLE
path/to/genomes_table.tsv, Format: Must include the following columns (No header) [organism_type]<tab>[id_sample]<tab>[id_mag]<tab>[genome]<tab>[proteins]<tab>[cds] but can include additional columns to the right (e.g., [gene_models]). Suggested input is from `compile_genomes_table.py` script. [Default: stdin]
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/project_directory [Default: veba_output/cluster]
-l, --local_clustering
Perform local clustering after global clustering
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
Genome clustering arguments:
-G {fastani,skani}, --genome_clustering_algorithm {fastani,skani}
Program to use for ANI calculations. `skani` is faster and more memory efficient. For v1.0.0 - v1.3.x behavior, use `fastani`. [Default: skani]
-A ANI_THRESHOLD, --ani_threshold ANI_THRESHOLD
Species-level cluster (SLC) ANI threshold (Range (0.0, 100.0]) [Default: 95.0]
-F AF_THRESHOLD, --af_threshold AF_THRESHOLD
Species-level cluster (SLC) alignment fraction threshold. Only available if `skani` is used as --genome_clustering_algorithm. (Range (0.0, 100.0]) [Default: 30.0]
--genome_cluster_prefix GENOME_CLUSTER_PREFIX
Cluster prefix [Default: 'SLC-
--genome_cluster_suffix GENOME_CLUSTER_SUFFIX
Cluster suffix [Default: '
--genome_cluster_prefix_zfill GENOME_CLUSTER_PREFIX_ZFILL
Cluster prefix zfill. Use 7 to match identifiers from OrthoFinder. Use 0 to add no zfill. [Default: 0]
Skani triangle arguments:
--skani_target_ani SKANI_TARGET_ANI
skani | If you set --skani_target_ani to --ani_threshold, you may screen out genomes ANI ≥ --ani_threshold [Default: 80]
--skani_minimum_af SKANI_MINIMUM_AF
skani | Minimum aligned fraction greater than this value. If you set --skani_minimum_af to --af_threshold, you may screen out genomes AF ≥ --af_threshold [Default: 15]
--skani_no_confidence_interval
skani | Output [5,95] ANI confidence intervals using percentile bootstrap on the putative ANI distribution
[Prokaryotic & Eukaryotic] Skani triangle arguments:
--skani_nonviral_preset {slow,none,medium,fast}
skani [Prokaryotic & Eukaryotic]| Use `none` if you are setting skani -c (compression factor) {fast, medium, slow, none} [Default: medium]
--skani_nonviral_compression_factor SKANI_NONVIRAL_COMPRESSION_FACTOR
skani [Prokaryotic & Eukaryotic]| Compression factor (k-mer subsampling rate). [Default: 125]
--skani_nonviral_marker_kmer_compression_factor SKANI_NONVIRAL_MARKER_KMER_COMPRESSION_FACTOR
skani [Prokaryotic & Eukaryotic] | Marker k-mer compression factor. Markers are used for filtering. [Default: 1000]
--skani_nonviral_options SKANI_NONVIRAL_OPTIONS
skani [Prokaryotic & Eukaryotic] | More options for `skani triangle` (e.g. --arg 1 ) [Default: '']
[Viral] Skani triangle arguments:
--skani_viral_preset {slow,none,medium,fast}
skani | Use `none` if you are setting skani -c (compression factor) {fast, medium, slow, none} [Default: slow]
--skani_viral_compression_factor SKANI_VIRAL_COMPRESSION_FACTOR
skani [Viral] | Compression factor (k-mer subsampling rate). [Default: 30]
--skani_viral_marker_kmer_compression_factor SKANI_VIRAL_MARKER_KMER_COMPRESSION_FACTOR
skani [Viral] | Marker k-mer compression factor. Markers are used for filtering. Consider decreasing to ~200-300 if working with small genomes (e.g. plasmids or viruses). [Default: 200]
--skani_viral_options SKANI_VIRAL_OPTIONS
skani [Viral] | More options for `skani triangle` (e.g. --arg 1 ) [Default: '']
FastANI arguments:
--fastani_options FASTANI_OPTIONS
FastANI | More options (e.g. --arg 1 ) [Default: '']
Protein clustering arguments:
-P {mmseqs-linclust,mmseqs-cluster,diamond-linclust,diamond-cluster}, --protein_clustering_algorithm {mmseqs-linclust,mmseqs-cluster,diamond-linclust,diamond-cluster}
Clustering algorithm | Diamond can only be used for clustering proteins {mmseqs-cluster, mmseqs-linclust, diamond-cluster, mmseqs-linclust} [Default: mmseqs-cluster]
-t MINIMUM_IDENTITY_THRESHOLD, --minimum_identity_threshold MINIMUM_IDENTITY_THRESHOLD
Clustering | Percent identity threshold (Range (0.0, 100.0]) [Default: 50.0]
-c MINIMUM_COVERAGE_THRESHOLD, --minimum_coverage_threshold MINIMUM_COVERAGE_THRESHOLD
Clustering | Coverage threshold (Range (0.0, 1.0]) [Default: 0.8]
--protein_cluster_prefix PROTEIN_CLUSTER_PREFIX
Cluster prefix [Default: 'SSPC-
--protein_cluster_suffix PROTEIN_CLUSTER_SUFFIX
Cluster suffix [Default: '
--protein_cluster_prefix_zfill PROTEIN_CLUSTER_PREFIX_ZFILL
Cluster prefix zfill. Use 7 to match identifiers from OrthoFinder. Use 0 to add no zfill. [Default: 0]
--mmseqs2_options MMSEQS2_OPTIONS
MMSEQS2 | More options (e.g. --arg 1 ) [Default: '']
--diamond_options DIAMOND_OPTIONS
Diamond | More options (e.g. --arg 1 ) [Default: '']
Pangenome arguments:
--minimum_core_prevalence MINIMUM_CORE_PREVALENCE
Minimum ratio of genomes detected in a SLC for a SSPC to be considered core (Range (0.0, 1.0]) [Default: 1.0]
Output:
- global/feature_compression_ratios.tsv - Feature compression ratios for each domain. Also includes a summary of the number of genomes, genome clusters, proteins, and protein clusters.
- global/genome_clusters.tsv - Machine-readable table for genome clusters
[id_genome_cluster, number_of_components, number_of_samples_of_origin, components, samples_of_origin] - global/identifier_mapping.genomes.tsv - Identifier mapping for genomes
[id_genome, organism_type, sample_of_origin, id_genome_cluster, number_of_proteins, number_of_singleton_protein_clusters, ratio_of_protein_cluster_are_singletons] - global/identifier_mapping.proteins.tsv - Identifier mapping for proteins
[id_protein, organism_type, id_genome, sample_of_origin, id_genome_cluster, id_protein_cluster] - global/identifier_mapping.scaffolds.tsv - Identifier mapping for contigs
[id_scaffold, organism_type, id_genome, sample_of_origin, id_genome_cluster] - global/mags_to_slcs.tsv
- global/protein_clusters.tsv - Machine-readable table for protein clusters
[id_protein_cluster, number_of_components, number_of_samples_of_origin, components, samples_of_origin] - global/proteins_to_orthogroups.tsv - Identifier mapping between proteins and protein clusters
[id_protein, id_protein-cluster] - global/representative_sequences.faa - Protein sequences for cluster representatives. Header follows the following format:
id_protein-cluster id_original_protein - global/scaffolds_to_mags.tsv - Identifier mapping between contigs and genomes
[id\_contig, id_genome] - global/scaffolds_to_slcs.tsv - Identifier mapping between contigs and genome clusters
[id\_contig, id_genome-cluster] - global/pangenome_tables/*.tsv.gz - Pangenome tables for each SLC with prevalence values
- global/serialization/*.dict.pkl - Python dictionaries for clusters
- global/serialization/*.networkx_graph.pkl - NetworkX graphs for clusters
- local/* - If
--local_clusteringis selected then all of the files are also generated for local clustering
Annotates translated gene calls against UniRef, Pfam, KOFAM, VFDB, MiBIG, AMRFinder, and AntiFam
Annotation is performed using best hit annotations and profile HMMs. Proteins are aligned against UniRef50/90, MiBIG, and VFDB using Diamond. Protein domains are identified for Pfam, NCBIfam-AMRFinder, and AntiFam using HMMER3 and KEGG orthology using KOFAMSCAN. Functionality for annotating all proteins or only protein cluster representatives and propogating annotations across cluster membership.
Conda Environment: conda activate VEBA-annotate_env
usage: annotate.py -a <proteins> -o <output_directory> |Optional: -i <identifier_mapping]
Running: annotate.py v2024.6.7 via Python v3.10.14 | /Users/jolespin/miniconda3/envs/test_env/bin/python
options:
-h, --help show this help message and exit
Required I/O arguments:
-a PROTEINS, --proteins PROTEINS
path/to/proteins.faa fasta to annotate
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/project_directory [Default: veba_output/annotation]
-i IDENTIFIER_MAPPING, --identifier_mapping IDENTIFIER_MAPPING
Tab-seperated value table (identifier_mapping.proteins.tsv created by cluster.py) Requirements: 1) contain headers; 2) first column must be protein identifiers; and 3) contains these columns to the right in any order. Format: [id_protein]<tab>[organism_type]<tab>[id_genome]<tab>[sample_of_origin]<tab>[id_genome_cluster]<tab>[id_protein_cluster] with headers [Optional]
-x EXTENSION, --extension EXTENSION
Fasta file extension for proteins if a directory is provided for --proteins (Can be gzipped) [Default: faa]
-M MAXIMUM_PROTEIN_LENGTH, --maximum_protein_length MAXIMUM_PROTEIN_LENGTH
Proteins ≥ 100k will cause HMMSearch to crash and will use more resources [Default: 99999]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
--keep_temporary_directory
Keep temporary directory [Default is to remove]
--no_check_protein_lengths
Do not check protein sequence lengths. Not recommended. Sequences must be < 100k or else PyHMMSearch will fail.
-v, --version show program's version number and exit
Database arguments:
-u UNIREF, --uniref UNIREF
UniRef database to use {uniref90, uniref50}. uniref90 receommended for well-characterized systems and uniref50 for less characterized systems [Default: uniref50]
--veba_database VEBA_DATABASE
VEBA database location. [Default: $VEBA_DATABASE environment variable]
Diamond arguments:
--diamond_sensitivity DIAMOND_SENSITIVITY
Diamond | Sensitivity [Default: '']
--diamond_evalue DIAMOND_EVALUE
Diamond | E-Value [Default: 0.001]
--diamond_options DIAMOND_OPTIONS
Diamond | More options (e.g. --arg 1 ) [Default: '']
Composite arguments:
-j COMPOSITE_NAME_JOINER, --composite_name_joiner COMPOSITE_NAME_JOINER
Composite label separator [Default: ; ]
Output:
- annotations.proteins.tsv.gz - Concatenated annotations from Diamond (UniRef, MiBIG, VFDB, CAZy), PyHMMSearch (Pfam, NCBIfam-AMR, AntiFam), and PyKofamSearch (KEGG)
- annotations.protein_clusters.tsv.gz - Propogated annotations if clusters are provided
- module_completion_ratios.genomes.tsv - KEGG module completion ratios for each genomes [Only if --identifier_mapping is provided]
- module_completion_ratios.genome_clusters.tsv - KEGG module completion ratios for each genome clusters [Only if --identifier_mapping is provided]
Constructs phylogenetic trees given a marker set
The phylogeny module is a tool used for phylogenetic inference and constructing phylogenetic trees for genomes given a reference marker set. This is performed by the following method: 1) identifying marker proteins using PyHMMSearch; 2) creating protein alignments for each marker identified MUSCLE; 3) trimming the alignments using ClipKIT; 4) concatenating the alignments; 5) approximately-maximum-likelihood phylogenetic inference using FastTree2 ; and 6) optional maximum likelihood phylogenetic inference using IQ-TREE2. An option to include marker-specific noise cutoff scores is also available using the --scores_cutoff parameter. Poor-quality genomes that do not meet a threshold in the proportion of markers in the reference are removed using the --minimum_markers_aligned_ratio parameter. Similarly, non-informative markers that are not prevalent in the query genomes are removed using the --minimum_genomes_aligned_ratio parameter.
Conda Environment: conda activate VEBA-phylogeny_env
usage: phylogeny.py -d <database_hmms> -a <proteins> -o <output_directory>
Running: phylogeny.py v2024.6.7 via Python v3.10.14 | /Users/jolespin/miniconda3/envs/test_env/bin/python
options:
-h, --help show this help message and exit
Required I/O arguments:
-d DATABASE_HMM, --database_hmm DATABASE_HMM
path/to/HMM database of markers
-a PROTEINS, --proteins PROTEINS
Can be the following format: 1) Tab-seperated value table of [id_mag]<tab>[path/to/protein.fasta] (No header); 2) Files with list of filepaths [path/to/protein.fasta] (uses --extension); or 3) Directory of protein fasta (uses --extension)
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/project_directory [Default: veba_output/phylogeny]
-x EXTENSION, --extension EXTENSION
Fasta file extension for proteins if a list is provided [Default: faa]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
PyHMMSearch arguments:
-t {gathering,noise,e,trusted}, --threshold_method {gathering,noise,e,trusted}
Cutoff threshold method [Default: e]
-e EVALUE, --evalue EVALUE
E-value threshold [Default: 10.0]
-f {name,accession}, --hmm_marker_field {name,accession}
HMM reference type (accession, name) [Default: accession]
-s SCORES_CUTOFF, --scores_cutoff SCORES_CUTOFF
path/to/scores_cutoff.tsv. No header. [id_hmm]<tab>[score]
Alignment arguments:
-A {align,super5}, --alignment_algorithm {align,super5}
Muscle alignment algorithm. Align large input using Super5 algorithm if -align is too expensive. [Default: align]
-g MINIMUM_GENOMES_ALIGNED_RATIO, --minimum_genomes_aligned_ratio MINIMUM_GENOMES_ALIGNED_RATIO
Minimum ratio of genomes include in alignment. This removes markers that are under represented. [Default: 0.95]
-m MINIMUM_MARKERS_ALIGNED_RATIO, --minimum_markers_aligned_ratio MINIMUM_MARKERS_ALIGNED_RATIO
Minimum ratio of markers aligned. This removes genomes with few markers. Note, this is based on detected markers and NOT total markers in original HMM. [Default: 0.2]
--muscle_options MUSCLE_OPTIONS
MUSCLE | More options (e.g. --arg 1 ) [Default: '']
--clipkit_mode CLIPKIT_MODE
ClipKIT | Trimming mode [Default: smart-gap]
--clipkit_options CLIPKIT_OPTIONS
ClipKIT | More options (e.g. --arg 1 ) [Default: '']
Tree arguments:
-T {fasttree,veryfasttree}, --tree_algorithm {fasttree,veryfasttree}
Tree inference algorithm to use {fasttree, veryfasttree} [Default: fasttree]
--fasttree_options FASTTREE_OPTIONS
FastTree | More options (e.g. --arg 1 ) [Default: '']
--veryfasttree_options VERYFASTTREE_OPTIONS
VeryFastTree | More options (e.g. --arg 1 ) [Default: '']
--no_iqtree IQTree | Don't run IQTree
--iqtree_model IQTREE_MODEL
IQTree | Model finder [Default: MFP]
--iqtree_mset IQTREE_MSET
IQTree | Model set to choose from [Default: WAG,LG]
--iqtree_bootstraps IQTREE_BOOTSTRAPS
IQTree | Bootstraps [Default: 1000]
--iqtree_options IQTREE_OPTIONS
IQTree | More options (e.g. --arg 1 ) [Default: '']
--no_show_support ETE3 | Don't show branch bootstrap/support values
--no_show_branch_length
ETE3 | Don't show branch lengths
Output:
- alignment_table.boolean.tsv.gz - Alignment table of (n = genomes, m = markers, ij=fasta pass qc)
- concatenated_alignment.fasta - Concatenated protein alignment of all marker hits
- concatenated_alignment.fasttree.nw - FastTree newick format based on concatenated alignment
- prefiltered_alignment_table.tsv.gz - Prefiltered alignment table of (n = genomes, m = markers, ij=fasta alignment)
- output.treefile - IQTREE2 newick format based on concatenated alignment (if --no_iqtree is not selected)
Builds local or global index for alignment to genomes
The index module creates reference indices for alignments in both local or global paradigms. In the local paradigm, an index is created for all the assembled genomes concatenated together for each sample. This is useful in situations where perfectly paired metagenomics and metatranscriptomics are available where the metatranscriptomics can be mapped directly to the de novo reference generated from the metagenomics. However, this is not applicable in all cases such as when there is not a perfect overlap between metagenomics and metatranscriptomics. In this global paradigm, assembled genomes are concatenated across all samples and an alignment index is created for this concatenated reference. Currently, Bowtie2 is the only alignment software packages supported.
Conda Environment: conda activate VEBA-mapping_env
usage: index.py -i <mags> -o <output> --heatmap_output <pdf>
Running: index.py v2023.5.8 via Python v3.11.0 | /expanse/projects/jcl110/anaconda3/envs/VEBA-phylogeny_env/bin/python
options:
-h, --help show this help message and exit
Required I/O arguments:
-r REFERENCES, --references REFERENCES
local mode: [id\_sample]<tab>[path/to/reference.fa] and global mode: [path/to/reference.fa]
-g GENE_MODELS, --gene_models GENE_MODELS
local mode: [id\_sample]<tab>[path/to/reference.gff] and global mode: [path/to/reference.gff]
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/project_directory [Default: veba_output/index]
-m MINIMUM_CONTIG_LENGTH, --minimum_contig_length MINIMUM_CONTIG_LENGTH
Minimum contig length [Default: 1500]
-M MODE, --mode MODE Concatenate all references with global and build index or build index for each reference {global, local, infer}
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Use -1 for completely random. Use 0 for consecutive random states. Use any other positive integer for the same random state for all iterations [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
Bowtie2 Index arguments:
--bowtie2_build_options BOWTIE2_BUILD_OPTIONS
bowtie2-build | More options (e.g. --arg 1 ) [Default: '']
Output (global):
- reference.fa.gz - Concatenated reference fasta
- reference.fa.gz.*.bt2 - Bowtie2 index of reference fasta
- reference.gff - Concatenated gene models
- reference.saf - SAF format for reference
Output (local):
- [id_sample]/reference.fa.gz - Concatenated reference fasta
- [id_sample]/reference.fa.gz.*.bt2 - Bowtie2 index of reference fasta
- [id_sample]/reference.gff - Concatenated gene models
- [id_sample]/reference.saf - SAF format for reference
Aligns reads to local or global index of genomes
The mapping module uses local or global reference indices generated by the index module and aligns reads using Bowtie2. The alignment files are sorted to produce sorted BAM files using Samtools which are then indexed. Coverage is calculated for contigs via Samtools and genome spatial coverage (i.e., ratio of bases covered in genome) is provided. Reads from the sorted BAM files are then fed into featureCounts to produce gene-level counts, orthogroup-level counts, MAG-level counts, and SLC-level counts.
Conda Environment: conda activate VEBA-mapping_env
usage: mapping.py -1 <reads_1.fq> -2 <reads_2.fq> -n <name> -o <output_directory> -x <reference_directory>
Running: mapping.py v2023.5.15 via Python v3.11.0 | /expanse/projects/jcl110/anaconda3/envs/VEBA-phylogeny_env/bin/python
options:
-h, --help show this help message and exit
Required I/O arguments:
-1 FORWARD_READS, --forward_reads FORWARD_READS
path/to/reads_1.fastq
-2 REVERSE_READS, --reverse_reads REVERSE_READS
path/to/reads_2.fastq
-n NAME, --name NAME Name of sample
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/mapping]
Reference arguments:
-x REFERENCE_INDEX, --reference_index REFERENCE_INDEX
path/to/bowtie2_index. Either a file or directory. If directory, then it assumes the index is named `reference.fa.gz`
-r REFERENCE_FASTA, --reference_fasta REFERENCE_FASTA
path/to/reference.fasta. If not provided then it is set to the --reference_index
-a REFERENCE_GFF, --reference_gff REFERENCE_GFF
path/to/reference.gff. If not provided then --reference_index must be a directory that contains the file: 'reference.gff'
-s REFERENCE_SAF, --reference_saf REFERENCE_SAF
path/to/reference.saf. If not provided then --reference_index must be a directory that contains the file: 'reference.saf'
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv. Must have at least 2 columns [name, executable] [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint
-v, --version show program's version number and exit
Bowtie2 arguments:
--retain_unmapped_reads RETAIN_UNMAPPED_READS
Retain reads that do not map to reference. 0=No, 1=yes [Default: 1]
--bowtie2_options BOWTIE2_OPTIONS
Bowtie2 | More options (e.g. --arg 1 ) [Default: '']
http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml
featureCounts arguments:
-g ATTRIBUTE_TYPE, --attribute_type ATTRIBUTE_TYPE
Attribute type in GTF/GFF file. [Default: gene_id]
-t FEATURE_TYPE, --feature_type FEATURE_TYPE
Feature type in GTF/GFF file. [Default: CDS]
--retain_featurecounts RETAIN_FEATURECOUNTS
Retain feature counts output table (a slimmer version is output regardless). 0=No, 1=yes [Default: 0]
--featurecounts_options FEATURECOUNTS_OPTIONS
featureCounts | More options (e.g. --arg 1 ) [Default: ''] | http://bioinf.wehi.edu.au/featureCounts/
Identifier arguments:
--proteins_to_orthogroups PROTEINS_TO_ORTHOGROUPS
path/to/protein_to_orthogroup.tsv, [id\_orf]<tab>[id_orthogroup], No header
--scaffolds_to_bins SCAFFOLDS_TO_BINS
path/to/scaffold_to_bins.tsv, [id_scaffold]<tab>[id_bin], No header
--scaffolds_to_clusters SCAFFOLDS_TO_CLUSTERS
path/to/scaffold_to_cluster.tsv, [id_scaffold]<tab>[id_cluster], No header
Output:
- mapped.sorted.bam - Sorted BAM file
- mapped.sorted.bam.bai - Sorted BAM file index
- mapped.sorted.bam.coverage.tsv.gz - Samtools coverage table
- genome_spatial_coverage.tsv.gz - Spatial coverage for genome (i.e., ratio of bases covered) [Only if --scaffolds_to_bins is provided]
- counts.orfs.tsv.gz - ORF-level counts table
- counts.scaffolds.tsv.gz - Contig-level counts table
- counts.mags.tsv.gz - MAG-level counts table [Only if --scaffolds_to_bins is provided]
- counts.clusters.tsv.gz - SLC-level counts table [Only if --scaffolds_to_clusters is provided]
- counts.orthogroups.tsv.gz - Orthogroup-level counts table [Only if --orf_to_orthogroups is provided]
- unmapped_1.fastq.gz - Unmapped reads (forward)
- unmapped_2.fastq.gz - Unmapped reads (reverse)
Identify biosynthetic gene clusters in prokaryotes and fungi
The biosynthetic module is a wrapper around antiSMASH. It produces a tabular output that is machine-readale and easier to parse than the GBK and JSON files produced by antiSMASH. Novelty scores are calculated using the ratio of proteins that align to MiBIG.
usage: biosynthetic.py -i <genomes_gene-models.tsv> -o <output_directory> -t bacteria | Suggested input is from `compile_genomes_table.py` script. Use cut -f3,4,7
Running: biosynthetic.py v2024.3.5 via Python v3.8.15 | /expanse/projects/jcl110/miniconda3/envs/VEBA-biosynthetic_env/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-i FROM_GENOMES, --from_genomes FROM_GENOMES
path/to/input.tsv. Cannot be used with -A/--from_antismash. Format: Must include the follow columns (No header) [id_genome]<tab>[genome]<tab>[gene_models]. Suggested input is from `compile_genomes_table.py` script. Use cut -f3,4,7
-A FROM_ANTISMASH, --from_antismash FROM_ANTISMASH
path/to/input.tsv. Cannot be used with -i/--from_genomes. Format: Must include the follow columns (No header) [id_genome]<tab>[path/to/biosynthetic_results_directory/]
-o OUTPUT_DIRECTORY, --output_directory OUTPUT_DIRECTORY
path/to/project_directory [Default: veba_output/biosynthetic]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
Database arguments:
--veba_database VEBA_DATABASE
VEBA database location. [Default: $VEBA_DATABASE environment variable]
antiSMASH arguments:
-t TAXON, --taxon TAXON
Taxonomic classification of input sequence {bacteria,fungi} [Default: bacteria]
--minimum_contig_length MINIMUM_CONTIG_LENGTH
Minimum contig length. [Default: 1]
-d ANTISMASH_DATABASE, --antismash_database ANTISMASH_DATABASE
antiSMASH | Database directory path [Default: /expanse/projects/jcl110/miniconda3/envs/VEBA-biosynthetic_env/lib/python3.8/site-packages/antismash/databases]
-s HMMDETECTION_STRICTNESS, --hmmdetection_strictness HMMDETECTION_STRICTNESS
antiSMASH | Defines which level of strictness to use for HMM-based cluster detection {strict,relaxed,loose} [Default: relaxed]
--tta_threshold TTA_THRESHOLD
antiSMASH | Lowest GC content to annotate TTA codons at [Default: 0.65]
--antismash_options ANTISMASH_OPTIONS
antiSMASH | More options (e.g. --arg 1 ) [Default: '']
Diamond arguments:
--diamond_sensitivity DIAMOND_SENSITIVITY
Diamond | Sensitivity [Default: '']
--diamond_evalue DIAMOND_EVALUE
Diamond | E-Value [Default: 0.001]
--diamond_options DIAMOND_OPTIONS
Diamond | More options (e.g. --arg 1 ) [Default: '']
Novelty score threshold arguments:
--pident PIDENT pident lower bound [float:0 ≤ x < 100] [Default: 0]
--qcovhsp QCOVHSP qcovhsp lower bound [float:0 ≤ x < 100] [Default: 0]
--scovhsp SCOVHSP scovhsp lower bound [float:0 ≤ x < 100] [Default: 0]
--evalue EVALUE e-value lower bound [float:0 < x < 1] [Default: 1e-3]
MMSEQS2 arguments:
-a {mmseqs-linclust,mmseqs-cluster}, --algorithm {mmseqs-linclust,mmseqs-cluster}
MMSEQS2 | {mmseqs-cluster, mmseqs-linclust} [Default: mmseqs-cluster]
-f REPRESENTATIVE_OUTPUT_FORMAT, --representative_output_format REPRESENTATIVE_OUTPUT_FORMAT
Format of output for representative sequences: {table, fasta} [Default: fasta]
--protein_minimum_identity_threshold PROTEIN_MINIMUM_IDENTITY_THRESHOLD
MMSEQS2 | Protein cluster percent identity threshold (Range (0.0, 100.0]) [Default: 50.0]
--protein_minimum_coverage_threshold PROTEIN_MINIMUM_COVERAGE_THRESHOLD
MMSEQS2 | Protein coverage threshold (Range (0.0, 1.0]) [Default: 0.8]
--protein_cluster_prefix PROTEIN_CLUSTER_PREFIX
Protein cluster prefix [Default: 'BGCPC-
--protein_cluster_suffix PROTEIN_CLUSTER_SUFFIX
Protein cluster suffix [Default: '
--protein_cluster_prefix_zfill PROTEIN_CLUSTER_PREFIX_ZFILL
Protein cluster prefix zfill. Use 7 to match identifiers from OrthoFinder. Use 0 to add no zfill. [Default: 0]
--no_protein_clustering
No protein clustering
--nucleotide_minimum_identity_threshold NUCLEOTIDE_MINIMUM_IDENTITY_THRESHOLD
MMSEQS2 | Nucleotide cluster percent identity threshold (Range (0.0, 100.0]) [Default: 90.0]
--nucleotide_minimum_coverage_threshold NUCLEOTIDE_MINIMUM_COVERAGE_THRESHOLD
MMSEQS2 | Nucleotide coverage threshold (Range (0.0, 1.0]) [Default: 0.8]
--nucleotide_cluster_prefix NUCLEOTIDE_CLUSTER_PREFIX
Nucleotide cluster prefix [Default: 'BGCNC-
--nucleotide_cluster_suffix NUCLEOTIDE_CLUSTER_SUFFIX
Nucleotide cluster suffix [Default: '
--nucleotide_cluster_prefix_zfill NUCLEOTIDE_CLUSTER_PREFIX_ZFILL
Nucleotide cluster prefix zfill. Use 0 to add no zfill. [Default: 0]
--no_nucleotide_clustering
No nucleotide clustering
--no_singletons Exclude singletons (not recommended)
--mmseqs2_options MMSEQS2_OPTIONS
MMSEQS2 | More options (e.g. --arg 1 ) [Default: '']
Output:
- bgc_clusters.tsv - BGC to BGC nucleotide cluster
- bgc_protocluster-types.tsv.gz - Summary of BGCs detected organized by type. Also includes summary of BGCs that are NOT on contig edge.
- bgcs.representative_sequences.fasta.gz - Full length BGC nucleotide cluster representatives
- component_clusters.tsv - BGC protein to BGC protein cluster
- components.representative_sequences.faa.gz - BGC protein cluster representatives
- fasta/[id/_genome].faa/fasta.gz - BGC sequences in protein and nucleotide space
- genbanks/[id_genome]/*.gbk - Genbank formatted antiSMASH results
- homology.tsv.gz - Diamond results for MIBiG and VFDB
- identifier_mapping.bgcs.tsv.gz - All of the BGCs in tabular format organized by genome, contig, region, and gene.
- identifier_mapping.components.tsv.gz - All of the BGC components (i.e., genes in BGC) in tabular format organized by genome, contig, region, and gene.
- krona.html - HTML showing Krona plot for number of BGCs per protocluster-type.
- krona.tsv - Data to produce Krona plot
- prevalence_tables/bgcs.tsv.gz - Genome vs. BGC nucleotide cluster prevalence table
- prevalence_tables/components.tsv.gz - Genome vs. BGC protein cluster prevalence table
Taxonomic profiling of de novo genomes
The profile-taxonomy module does the following: 0) builds a Sylph sketch database (63) for non-viral and viral genomes using the compile_custom_sylph_sketch_database_from_genomes.py script prior to running the module; 1) converts paired reads to a query sketch database using Sylph; 2) profiles the genome sketch databases using the query sketch database generated from the reads; 3) reformats the Sylph output tables; and 4) aggregates abundances with respect to SLC if clusters are provided.
usage: profile-taxonomy.py -1 <forward_reads.fq> -2 <reverse_reads.fq>|-s <sketch> -n <name> -o <output_directory> -d <db_1.syldb db_2.syldb ... db_n.syldb>
Running: profile-taxonomy.py v2023.12.19 via Python v3.10.12 | /expanse/projects/jcl110/miniconda3/envs/VEBA-profile_env/bin/python
options:
-h, --help show this help message and exit
Required I/O arguments:
-1 FORWARD_READS, --forward_reads FORWARD_READS
path/to/forward_reads.fq[.gz]
-2 REVERSE_READS, --reverse_reads REVERSE_READS
path/to/reverse_reads.fq[.gz]]
-s READS_SKETCH, --reads_sketch READS_SKETCH
path/to/reads_sketch.sylsp (e.g., sylph sketch output) (Cannot be used with --forward_reads and --reverse_reads)
-n NAME, --name NAME Name of sample
-d SYLPH_DATABASES [SYLPH_DATABASES ...], --sylph_databases SYLPH_DATABASES [SYLPH_DATABASES ...]
Sylph database(s) with all genomes. Can be multiple databases delimited by spaces. Use compile_custom_sylph_sketch_database_from_genomes.py to build database.
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/profiling/taxonomy]
-c GENOME_CLUSTERS, --genome_clusters GENOME_CLUSTERS
path/to/mags_to_slcs.tsv. [id_genome]<tab>[id_genome-cluster], No header. Aggregates counts for genome clusters.
-F {sketch,paired}, --input_reads_format {sketch,paired}
Input reads format {paired, sketch} [Default: auto]
-x EXTENSION, --extension EXTENSION
Fasta file extension for bins. Assumes all genomes have the same file extension. [Default: fa]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
--tmpdir TMPDIR Set temporary directory
Sylph sketch arguments (Fastq):
--sylph_sketch_k {21,31}
Sylph sketch [Fastq] | Value of k. Only k = 21, 31 are currently supported. [Default: 31]
--sylph_sketch_minimum_spacing SYLPH_SKETCH_MINIMUM_SPACING
Sylph sketch [Fastq] | Minimum spacing between selected k-mers on the genomes [Default: 30]
--sylph_sketch_subsampling_rate SYLPH_SKETCH_SUBSAMPLING_RATE
Sylph sketch [Fastq] | Subsampling rate. sylph runs without issues if the -c for all genomes is ≥ the -c for reads. [Default: 100]
--sylph_sketch_options SYLPH_SKETCH_OPTIONS
Sylph sketch [Fastq] | More options for `sylph sketch` (e.g. --arg 1 ) [Default: '']
Sylph profile arguments:
--sylph_profile_minimum_ani SYLPH_PROFILE_MINIMUM_ANI
Sylph profile | Minimum adjusted ANI to consider (0-100). [Default: 95]
--sylph_profile_minimum_number_kmers SYLPH_PROFILE_MINIMUM_NUMBER_KMERS
Sylph profile | Exclude genomes with less than this number of sampled k-mers. Default is 50 in Sylph but lowering to 20 accounts for viruses and small CPR genomes. [Default: 20]
--sylph_profile_minimum_count_correct SYLPH_PROFILE_MINIMUM_COUNT_CORRECT
Sylph profile | Minimum k-mer multiplicity needed for coverage correction. Higher values gives more precision but lower sensitivity [Default: 3]
--sylph_profile_options SYLPH_PROFILE_OPTIONS
Sylph profile | More options for `sylph profile` (e.g. --arg 1 ) [Default: '']
--header Include header in taxonomic abundance tables
Output:
- reads.sylsp - Reads sketch if paired-end reads were provided
- sylph_profile.tsv.gz - Output of
sylph profile - taxonomic_abundance.tsv.gz - Genome-level taxonomic abundance (No header)
- taxonomic_abundance.clusters.tsv.gz - SLC-level taxonomic abundance (No header, if --genome_clusters wer provided)
Pathway profiling of de novo genomes The profile-pathway module does the following: 0) builds a custom HUMAnN database based on protein annotations, identifier mapping tables, and taxonomy assignments using the compile_custom_humann_database_from_annotations.py script prior to running the module; 1) either accepts pre-joined reads, joins paired end reads using bbmerge.sh from BBSuite, or a BAM file of paired-end reads and joins them; 2) builds a Diamond database of proteins from the custom HUMAnN annotation table; 3) uses HUMAnN for pathway profiling of the joined reads using the custom HUMAnN database (16); and 4) reformats the output files.
usage: profile-pathway.py -1 <forward_reads.fq> -2 <reverse_reads.fq> -n <name> -o <output_directory>
Running: profile-pathway.py v2023.11.30 via Python v3.10.12 | /expanse/projects/jcl110/miniconda3/envs/VEBA-profile_env/bin/python
options:
-h, --help show this help message and exit
Required reads arguments:
-1 FORWARD_READS, --forward_reads FORWARD_READS
path/to/forward_reads.fq (Requires --reverse_reads, cannot be used with --joined_reads or --bam)
-2 REVERSE_READS, --reverse_reads REVERSE_READS
path/to/reverse_reads.fq (Requires --forward_reads, cannot be used with --joined_reads or --bam)
-j JOINED_READS, --joined_reads JOINED_READS
path/to/joined_reads.fq (e.g., bbmerge.sh output) (Cannot be used with --forward_reads, --reverse_reads, or --bam)
-b BAM, --bam BAM path/to/mapped.sorted.bam file aligned to genomes (Cannot be used with --forward_reads, --reverse_reads, or --joined_reads)
-F INPUT_READS_FORMAT, --input_reads_format INPUT_READS_FORMAT
Input reads format {paired, joined, bam} [Default: auto]
Required database arguments:
-i IDENTIFIER_MAPPING, --identifier_mapping IDENTIFIER_MAPPING
Identifier mapping which includes [id_protein]<tab>[id_uniref]<tab>[length]<tab>[lineage]. In VEBA, you can use `compile_custom_humann_database_from_annotations.py`.
https://github.com/biobakery/humann#custom-reference-database-annotations
-f FASTA, --fasta FASTA
Protein fasta to build database
-d DIAMOND_DATABASE, --diamond_database DIAMOND_DATABASE
Diamond database with all proteins from --identifier_mapping
Required I/O arguments:
-n NAME, --name NAME Name of sample
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/profiling/pathways]
Utility arguments:
--path_config PATH_CONFIG
path/to/config.tsv [Default: CONDA_PREFIX]
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
--tmpdir TMPDIR Set temporary directory
bbmerge.sh arguments:
--minimum_merge_overlap MINIMUM_MERGE_OVERLAP
bbmerge.sh | Minimum number of overlapping bases to allow merging. [Default: 12]
--bbmerge_options BBMERGE_OPTIONS
bbmerge.sh options (e.g. --arg 1 ) [Default: '']
HUMAnN arguments:
--search_mode SEARCH_MODE
HUMAnN | Search for uniref50 or uniref90 gene families {uniref50, uniref90, auto} [Default: 'auto']
--pathways PATHWAYS HUMAnN | The database to use for pathway computations {metacyc, unipathway} [Default: 'metacyc']
-e EVALUE, --evalue EVALUE
HUMAnN | The evalue threshold to use with the translated search [Default: 1.0]
-m TRANSLATED_IDENTITY_THRESHOLD, --translated_identity_threshold TRANSLATED_IDENTITY_THRESHOLD
HUMAnN | Identity threshold for translated alignments [Default: Tuned automatically (based on uniref mode) unless a custom value is specified]
-q TRANSLATED_QUERY_COVERAGE_THRESHOLD, --translated_query_coverage_threshold TRANSLATED_QUERY_COVERAGE_THRESHOLD
HUMAnN | Query coverage threshold for translated alignments [Default: 90.0]
-s TRANSLATED_SUBJECT_COVERAGE_THRESHOLD, --translated_subject_coverage_threshold TRANSLATED_SUBJECT_COVERAGE_THRESHOLD
HUMAnN | Subject coverage threshold for translated alignments [Default: 50.0]
--humann_memory HUMANN_MEMORY
HUMAnN | Memory use mode {minimum, maximum} [Default: 'minimum']
--humann_options HUMANN_OPTIONS
HUMAnN options (e.g. --arg 1 ) [Default: '']
Output:
- reads.seqkit_stats.tsv - Sequence stats for input reads
- humann_pathabundance.tsv - Stratified abundance of taxa-specific metabolic pathways
- humann_pathcoverage.tsv - Stratified pathway completion ratio of taxa-specific metabolic pathways
- humann_genefamilies.tsv - Stratified abundance of taxa-specific gene families
- humann_diamond_unaligned.fa.gz - Joined reads that did not align to database
- humann_diamond_aligned.tsv.gz - Aligned reads from translated blast search to database (blast6 format)
Automated read trim position detection, DADA2 ASV detection, taxonomic classification, and file conversion
The amplicon module is a wrapper around QIIME2's implementation of the DADA2 ASV pipeline which has been fairly standardized. This works exclusively on paired-end short reads and is not designed for single-end reads nor long reads (the latter may be adapted later). The experimental portion of this module is the automatic detection of forward and reverse trim. This module first imports reads into a QIIME2 Artifact object, summarizes reads, and gets position-specific fastq statistics. The amplicon module uses the position-specific fastq statistics to suggest forward and reverse trim positions (this part is experimental, please use --inspect_trim_regions to manually check the quality plots to ensure it is where you would cut). Next ASVs are detected via DADA2 and denoising statistics are calculated. After ASVs are detected, taxonomy is classified using classification modules provided by user (e.g., silva-138-99-nb-classifier.qza followed by phylogenetic inference. Finally, QIIME2 and BIOM formatted files are converted into tab-separated value tables and fasta files.
usage: amplicon.py -i <reads_table.tsv> -c <classifier.qza> -o <output_directory>
Running: amplicon.py v2022.10.24 via Python v3.9.7 | /Users/jespinoz/anaconda3/bin/python
optional arguments:
-h, --help show this help message and exit
Required I/O arguments:
-i READS_TABLE, --reads_table READS_TABLE
path/to/reads_table.tsv. 3 columns separated by tabs with the following header: [sample-id <tab> forward-absolute-filepath <tab> reverse-absolute-filepath]
A utility script is provided: compile_reads_table.py
-c CLASSIFIER, --classifier CLASSIFIER
path/to/feature_classifier. Data Resources: https://docs.qiime2.org/2022.8/data-resources/
-o PROJECT_DIRECTORY, --project_directory PROJECT_DIRECTORY
path/to/project_directory [Default: veba_output/amplicon]
Utility arguments:
-p N_JOBS, --n_jobs N_JOBS
Number of threads [Default: 1]
--random_state RANDOM_STATE
Random state [Default: 0]
--restart_from_checkpoint RESTART_FROM_CHECKPOINT
Restart from a particular checkpoint [Default: None]
-v, --version show program's version number and exit
--tmpdir TMPDIR Set temporary directory
Trim detection arguments:
--inspect_trim_regions
Manually inspect trim regions then rerun [PLEASE USE THIS TO CHECK TRIMMING SUGGESTIONS AS THEY ARE CURRENTLY EXPERIMENTAL]
-f FORWARD_TRIM, --forward_trim FORWARD_TRIM
Specify forward trim position
-r REVERSE_TRIM, --reverse_trim REVERSE_TRIM
Specify reverse trim position
-q MINIMUM_QUALITY, --minimum_quality MINIMUM_QUALITY
Minimum quality value
-m MINIMUM_LENGTH, --minimum_length MINIMUM_LENGTH
Minimum length. If minimum quality value makes length shorter than this then an error will yield with which samples are responsible [Default: 100]
-w WINDOW_SIZE, --window_size WINDOW_SIZE
Window size [Default: 4]
-l MAXIMUM_AVERAGE_LOSS, --maximum_average_loss MAXIMUM_AVERAGE_LOSS
Maximum average loss for window size [Default: --window_size]
DADA2 arguments:
--minimum_overlap MINIMUM_OVERLAP
DADA2 | The minimum length of the overlap required for merging the forward and reverse reads. [Default: 12]
--dada2_options DADA2_OPTIONS
Additional DADA2 options. '--arg value'
Phylogeny arguments:
--phylogeny_mode PHYLOGENY_MODE
QIIME2 phylogeny submodule [Default: align-to-tree-mafft-fasttree]
--phylogeny_options PHYLOGENY_OPTIONS
Additional options. '--arg value'
Copyright 2021 Josh L. Espinoza ([email protected])
Output:
- aligned-dna-sequences.fasta - Aligned ASV reference sequences
- dna-sequences.fasta - ASV reference sequences
- dna-sequences.with_taxonomy.fasta - ASV reference sequences with taxonomy/confidence info in description
- feature-table.biom - BIOM format ASV feature table
- feature-table.tsv - Tab-seperated values ASV table (rows=ASV, columns=samples, skiprows=1)
- stats.tsv - Read statistics
- taxonomy.tsv - Taxonomy table [ASV][Lineage][Confidence]
- tree.nwk - Newick formatted tree