Vgp Pipeline

VGP Assembly Pipeline Skill

Overview

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):

• RESOURCE_ANALYSIS.md - Workflow canonical names, official/non-official filtering, metric availability, tool-level resource optimization
• DATA_INTEGRATION.md - ToLID patterns, GenomeArk S3 integration, NCBI accession recovery, Meryl k-mer management, species-metrics merging
• QUALITY_VALIDATION.md - Curation impact analysis, GenomeScope data validation, assembly size interpretation, communication patterns

Pre-flight Checks Before Testing

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.

Trajectories (by frequency of use)

Trajectory A: HiFi + Hi-C (Most Common)

• Inputs: HiFi Reads, Hi-C Reads
• Path: WF1 -> WF4 -> [WF6] -> WF8 -> WF9 -> PreCuration
• Output: HiC Phased assembly (hap1/hap2)
• WF6: Optional (can skip directly to WF8)

Trajectory B: HiFi + Trio

• Inputs: HiFi Reads, Hi-C Reads, Parental Reads
• Path: WF2 -> WF5 -> [WF6] -> WF8 -> WF9 -> PreCuration
• Output: Trio Phased assembly (maternal/paternal)
• WF6: Optional (can skip directly to WF8)

Trajectory C: HiFi Only (Least Common)

• Inputs: HiFi Reads only
• Path: WF1 -> WF3 -> WF6 -> WF9 -> PreCuration
• Output: Pseudohaplotype assembly (primary/alternate)
• WF6: Required (no Hi-C scaffolding step)
• Note: Skips WF8 entirely

Workflow Selection by Data Availability

Non-trio workflows (HiFi reads only)

• VGP1 (WF1): K-mer profiling with HiFi reads alone
• VGP3 (WF3): HiFi-only assembly with HiFiasm

Trio workflows (HiFi + Parental Illumina)

• VGP2 (WF2): Trio k-mer profiling (HiFi child + Illumina parents)
• VGP5 (WF5): Trio-phased assembly with HiFiasm

Universal scaffolding workflows

• RagTag scaffolding: Used for both trio and non-trio assemblies
• Requires reference genome specification

Methods language pattern

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 Descriptions

Post-Curation Processing

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:

• Curated AGP file (PretextView AGP v2.1 — 11 columns: 9 standard + "Painted" + haplotype label)
• Combined haplotype FASTA (both haplotypes in one file)
• Hi-C reads (list:paired), PacBio reads (list)
• Parameters: species name, assembly name, telomere pattern, compleasm DB

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).

IWC Workflow Versions (as of March 2026)

Recent Breaking Changes

BUSCO -> Compleasm (WF4 v0.5, WF8 v3.3):

• Compleasm (0.2.5+galaxy0) replaced BUSCO for gene completeness assessment
• Uses miniprot for protein-to-genome alignment (faster than BUSCO's BLAST approach)
• Same output categories: Complete (Single-copy + Duplicated), Fragmented, Missing
• Input parameters still named "Database for Busco Lineage" and "Lineage" (backward compat)

Hi-C reads format change (WF4 v0.5, WF8 v3.3):

• Changed from separate forward/reverse datasets to list:paired collection
• Users must build a list:paired collection before running these workflows

New required inputs across all workflows:

• Species Name (text) -- used for workflow reports
• Assembly Name (text) -- used for workflow reports

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)

Verifying IWC Versions

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

Haplotype Execution Patterns

Run Once (Both Haplotypes Together)

• WF1, WF2 (K-mer profiling)
• WF3, WF4, WF5 (Assembly)
• WF6 (Purge Duplicates) - depends on trajectory
• PreCuration

Run Twice (x2 per Haplotype)

• WF8 (Hi-C Scaffolding)
• WF9 (Decontamination)

WF6 (Purge Duplicates) Decision Logic

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):

• Merqury k-mer spectra shows clean haplotype separation
• Assembly QV is already high
• No significant duplication detected

When to run WF6 (Trajectories A/B):

• K-mer spectra shows residual duplications
• Higher heterozygosity samples
• Conservative approach preferred

Coverage Requirements

QC Checkpoints

After WF1/WF2 (K-mer Profiling)

• Verify GenomeScope2 model fit
• Check estimated genome size
• Review heterozygosity estimate

After WF4/WF5 (Assembly)

• Inspect Merqury k-mer spectra
• Decide whether to run WF6 based on duplication levels

After WF8 (Hi-C Scaffolding)

• Check Pretext Hi-C contact maps
• Verify chromosome-level scaffolding
• Validate against expected karyotype (see Karyotype Validation below)

After WF9 (Decontamination)

• Review contamination reports
• Check for unexpected removals

Karyotype-Based Scaffold Validation

Sex Chromosome Adjustment

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:

• Diploid organisms have two sex chromosomes (XX, XY, ZZ, ZW)
• X and Y (or Z and W) are distinct chromosomes
• If both in main haplotype -> two separate scaffolds expected
• Example: Asian elephant 2n=56, n=28, has X+Y -> expect 29 scaffolds

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

Common Pitfalls

Wrong: Compare diploid expected (2n) to haploid assembly

• Results in ~50% achievement rates
• Biologically incorrect

Wrong: Use haploid (n) when both sex chromosomes present

• Underestimates by 1
• Shows artificial "extra scaffold" problem

Correct: Use adjusted haploid (n or n+1 depending on sex chromosome configuration)

WF0 (Mitochondrial) Handling

WF0 runs in parallel with the main pipeline and may fail if:

• No mitochondrial reads present in HiFi data
• This is a biological failure, not technical

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

Visual Diagram Elements

When creating workflow diagrams:

Color Coding (Suggested)

• K-mer Profiling section: Orange (#fff3e0)
• Assembly section: Green (#e8f5e9)
• Purging section: Purple (#f3e5f5)
• Scaffolding section: Blue (#e3f2fd)
• Finishing section: Green (#e8f5e9)
• WF0 (Mitochondrial): Pink (#fce4ec)

Visual Indicators

• Solid lines: Required workflow connections
• Dashed lines: Optional skip paths
• Dashed box border: Optional workflow (WF6 in trajectories A/B)
• Solid box border: Required workflow
• Dimmed elements: Workflows not used in current trajectory

Haplotype Badges

• Blue badge (#e3f2fd): "x2 per haplotype" - runs separately
• Green badge (#e8f5e9): "both haplotypes" - runs together

Input Data Labels

• HiFi Reads: Blue (#4285f4)
• Hi-C Reads: Green (#34a853)
• Parental Reads: Red (#ea4335)

Summary Table

[WF6] = optional, WF6 = required, - = skipped

Reference Genomes for Scaffolding

Common Reference Genome

GCA_011100685.1 - Frequently used reference genome for RagTag scaffolding in canid genome assemblies.

When documenting scaffolding in methods sections:

• Always specify the reference genome accession
• Include version number if applicable
• Example: "scaffolded using RagTag v2.1.0 with the reference genome GCA_011100685.1"

Best Practices

For reproducibility:

• Document exact accession used
• Specify if custom modifications were made to reference
• Note if different references used for different species/assemblies

References

• VGP Galaxy Workflows - VGP workflows
• Vertebrate Genome Project