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

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

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
WF7	Bionano	Deprecated - no longer used
WF8	Hi-C Scaffolding	YAHS chromosome scaffolding
WF9	Decontamination	Remove contaminants
PreCuration	Pretext Snapshot	Prepare files for manual curation

IWC Workflow Versions (as of March 2026)

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

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

Data Type	Minimum Coverage	Notes
HiFi	30x	Diploid genome
Hi-C	60x	Diploid genome

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

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

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