VGP assembly pipeline - Galaxy workflow selection, execution patterns, QC checkpoints, and batch orchestration
VGP assembly pipeline - Galaxy workflow selection, execution patterns, QC checkpoints, and batch orchestration
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BioBlend and Planemo expertise for Galaxy workflow automation. Galaxy API usage, workflow invocation, status checking, error handling, batch processing, and dataset management. Essential for any Galaxy automation project.
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Expert in Galaxy workflow development, testing, and IWC best practices. Create, validate, and optimize .ga workflows following Intergalactic Workflow Commission standards.
| name | vgp-pipeline |
| description | VGP assembly pipeline - Galaxy workflow selection, execution patterns, QC checkpoints, and batch orchestration |
The Vertebrate Genome Project (VGP) assembly pipeline consists of Galaxy workflows for producing high-quality, phased, chromosome-level genome assemblies. This skill covers workflow selection, execution patterns, and quality control checkpoints.
Supporting files (detailed reference material):
VGP user accounts have a 250 GB quota. If quota_percent is at 100%, all new upload jobs land in paused state immediately and planemo dies with a cryptic message:
Exception: Upload job [...] failed with state [paused]
The error gives no hint that quota is the cause. Check before launching any planemo test:
mcp__Galaxy__connect(url=$GXYVGP, api_key=$TESTKEY)
mcp__Galaxy__get_user() # Inspect quota_percent in response
If quota is full, free up space (delete old test histories) and re-check before retrying.
When documenting workflow selection in publications:
"For species with available parental data (trio datasets), we employed
VGP2 -> VGP5 workflows. For species without parental data (non-trio datasets),
we performed VGP1 -> VGP3 workflows."
| Workflow | Name | Description |
|---|---|---|
| WF0 | Mitochondrial Assembly | MitoHiFi assembly (runs in parallel, may fail if no mito reads) |
| WF1 | K-mer Profiling | Genome size, heterozygosity estimation (HiFi) |
| WF2 | Trio K-mer Profiling | K-mer profiling with parental data |
| WF3 | Hifiasm | HiFi-only assembly |
| WF4 | Hifiasm + HiC | HiC-phased assembly |
| WF5 | Hifiasm Trio | Trio-phased assembly |
| WF6 | Purge Duplicates | Remove haplotypic duplications |
| Deprecated - no longer used | ||
| WF8 | Hi-C Scaffolding | YAHS chromosome scaffolding |
| WF9 | Decontamination | Remove contaminants |
| PreCuration | Pretext Snapshot | Prepare files for manual curation |
| PostCuration | Post Curation | Apply curation, split haplotypes, rename/reorient, QC |
Takes curated AGP + combined haplotype FASTA from pre-curation, applies curation changes, splits by haplotype, assigns chromosome names, renames/reorients hap2 to match hap1, generates QC.
Key inputs:
Haplotype labels in AGP: Hap_1, Hap_2, Z, W, Unloc, Haplotig
Rename/Reorient logic: Chromosomes assigned by size independently per haplotype, then mashmap (seqLength=50000) aligns hap2 to hap1 to detect mismatches → renames + inversions.
Key parameter: mashmap seqLength: 50000 — sequences must be ≥50kb for rename/reorient to be exercised.
Compleasm parameter gotcha: The workflow input "Database for Compleasm Genes" maps to compleasm's lineage_dataset parameter — pass a lineage like vertebrata_odb10, NOT the busco database version (v5). The busco_database: v5 is hardcoded in the tool_state.
Chromosome naming: vgp_chromosome_assignment produces SUPER_N names (SUPER_1, SUPER_2, SUPER_Z, SUPER_W), not chr1/chr2.
Known issue: vgp_chromosome_assignment v1.1 crashes when AGP contains multi-component scaffolds with Unloc pieces (e.g., Scaffold_1 = main + gap + unloc). Workaround: make Unloc scaffolds standalone single-component entries in the AGP.
Tag inheritance pitfall: The curation subworkflow sets #hap1 (via name:hap1 in .ga) on the Hap1 AGP at the "convert to agp" step. This propagates through chromosome assignment → mashmap → sak generation → reoriented hap2, causing Curated Hap2 to inherit #hap1. Fix: add RemoveTagDatasetAction for name:hap1 at key junction points (sak_file output, Curated Hap2 output).
| Workflow | IWC Repo | Latest Version | Dockstore ID |
|---|---|---|---|
| WF1 | kmer-profiling-hifi-VGP1 | v0.6 | github.com/iwc-workflows/kmer-profiling-hifi-VGP1/main |
| WF4 | Assembly-Hifi-HiC-phasing-VGP4 | v0.5 | github.com/iwc-workflows/Assembly-Hifi-HiC-phasing-VGP4/main |
| WF8 | Scaffolding-HiC-VGP8 | v3.3 | github.com/iwc-workflows/Scaffolding-HiC-VGP8/main |
BUSCO -> Compleasm (WF4 v0.5, WF8 v3.3):
0.2.5+galaxy0) replaced BUSCO for gene completeness assessmentHi-C reads format change (WF4 v0.5, WF8 v3.3):
New required inputs across all workflows:
WF4 additional new inputs: Trim Hi-C reads? (boolean), Name for Haplotype 1/2 (defaults: Hap1/Hap2), Bits for bloom filter (default: 37) WF8 additional new inputs: Haplotype (restricted: Haplotype 1/2, Maternal/Paternal, Primary/Alternate), Trim Hi-C Data? (boolean), Minimum Mapping Quality (default: 10)
Check latest versions via Dockstore API:
https://dockstore.org/api/ga4gh/trs/v2/tools/%23workflow%2Fgithub.com%2Fiwc-workflows%2F{REPO}%2Fmain/versions
Check workflow inputs by fetching the .ga file from GitHub:
https://raw.githubusercontent.com/iwc-workflows/{REPO}/main/{WORKFLOW_NAME}.ga
if trajectory == "C" (HiFi only):
WF6 is REQUIRED
WF6 border: solid
else: # Trajectory A or B
WF6 is OPTIONAL
WF6 border: dashed
Can skip directly to WF8
When to skip WF6 (Trajectories A/B):
When to run WF6 (Trajectories A/B):
| Data Type | Minimum Coverage | Notes |
|---|---|---|
| HiFi | 30x | Diploid genome |
| Hi-C | 60x | Diploid genome |
Problem: VGP assemblies often place both sex chromosomes (X+Y or Z+W) in the main haplotype, requiring adjustment to expected chromosome counts.
Solution: When both sex chromosomes present, expected = n + 1 (not n)
Implementation:
# Adjust haploid expected when BOTH sex chromosomes in main haplotype
df['num_chromosomes_haploid_adjusted'] = df['num_chromosomes_haploid'].copy()
both_sex_chr_patterns = [
'Has X and Y',
'Has Z and W',
'has Z and W',
'Has X1, X2, and Y',
'Has Z1, Z2, and W',
'Has 5X and 5Y'
]
if 'Sex chromosomes main haploptype' in df.columns:
has_both_sex = df['Sex chromosomes main haploptype'].isin(both_sex_chr_patterns)
df.loc[has_both_sex & df['num_chromosomes_haploid'].notna(),
'num_chromosomes_haploid_adjusted'] = \
df.loc[has_both_sex & df['num_chromosomes_haploid'].notna(),
'num_chromosomes_haploid'] + 1
Biological Reasoning:
Impact: Improved perfect match rate from 0% to ~90% in validation analyses
Validation Metrics:
# Use adjusted counts for validation
achieved = df['total_number_of_chromosomes']
expected = df['num_chromosomes_haploid_adjusted']
perfect_matches = (achieved == expected).sum()
within_1 = ((achieved - expected).abs() <= 1).sum()
ratio = achieved / expected
Wrong: Compare diploid expected (2n) to haploid assembly
Wrong: Use haploid (n) when both sex chromosomes present
Correct: Use adjusted haploid (n or n+1 depending on sex chromosome configuration)
WF0 runs in parallel with the main pipeline and may fail if:
def check_mitohifi_failure(wf0_result):
"""Distinguish biological vs technical failure"""
if "no_mito_reads" in wf0_result.log:
return "biological" # Expected for some samples
else:
return "technical" # Investigate further
When creating workflow diagrams:
#fff3e0)#e8f5e9)#f3e5f5)#e3f2fd)#e8f5e9)#fce4ec)#e3f2fd): "x2 per haplotype" - runs separately#e8f5e9): "both haplotypes" - runs together#4285f4)#34a853)#ea4335)| Trajectory | Inputs | K-mer | Assembly | Purge | Scaffold | Finish | Output |
|---|---|---|---|---|---|---|---|
| A | HiFi+HiC | WF1 | WF4 | [WF6] | WF8 | WF9->Pre | hap1/hap2 |
| B | HiFi+Trio | WF2 | WF5 | [WF6] | WF8 | WF9->Pre | mat/pat |
| C | HiFi only | WF1 | WF3 | WF6 | - | WF9->Pre | pri/alt |
[WF6] = optional, WF6 = required, - = skipped
GCA_011100685.1 - Frequently used reference genome for RagTag scaffolding in canid genome assemblies.
When documenting scaffolding in methods sections:
For reproducibility: