/bio-genome-assembly-hifi-assembly
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SKILL.md
bio-genome-assembly-hifi-assembly.SKILL.md<!--
COPYRIGHT NOTICE
This file is part of the "Universal Biomedical Skills" project.
Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
All Rights Reserved.
#
This code is proprietary and confidential.
Unauthorized copying of this file, via any medium is strictly prohibited.
#
Provenance: Authenticated by MD BABU MIA
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--- name: bio-genome-assembly-hifi-assembly description: High-quality genome assembly from PacBio HiFi reads using hifiasm with phasing support. Use when building reference-quality diploid assemblies from HiFi data, especially with trio or Hi-C phasing for fully resolved haplotypes. tool_type: cli primary_tool: hifiasm measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
---
HiFi Assembly
Basic Assembly
# Primary assembly (single haplotype consensus)
hifiasm -o output_prefix -t 32 reads.hifi.fastq.gz
# Output files:
# output_prefix.bp.p_ctg.gfa - Primary contigs
# output_prefix.bp.a_ctg.gfa - Alternate contigs
# output_prefix.bp.hap1.p_ctg.gfa - Haplotype 1 (if phased)
# output_prefix.bp.hap2.p_ctg.gfa - Haplotype 2 (if phased)
# Convert GFA to FASTA
awk '/^S/{print ">"$2;print $3}' output_prefix.bp.p_ctg.gfa > assembly.fastaTrio-Binned Phasing
# With parental short reads for trio binning
hifiasm -o trio_asm -t 32 \
-1 paternal.yak \
-2 maternal.yak \
child.hifi.fastq.gz
# Create yak databases from parental Illumina reads first
yak count -b37 -t16 -o paternal.yak paternal_R1.fq.gz paternal_R2.fq.gz
yak count -b37 -t16 -o maternal.yak maternal_R1.fq.gz maternal_R2.fq.gzHi-C Phasing
# Use Hi-C reads for phasing (no parents needed)
hifiasm -o hic_asm -t 32 \
--h1 hic_R1.fastq.gz \
--h2 hic_R2.fastq.gz \
reads.hifi.fastq.gz
# Produces fully phased hap1 and hap2 assembliesKey Parameters
| Parameter | Default | Description | |-----------|---------|-------------| | -t | 1 | Threads | | -l | 0 | Purge level (0=none, 1=light, 2=aggressive) | | -s | 0.55 | Similarity threshold for duplicate detection | | --primary | - | Output primary contigs only (no alternates) | | --n-hap | 2 | Expected number of haplotypes | | -D | 5.0 | Drop reads with depth > D*average | | -N | 100 | Consider up to N overlaps for each read |
Purge Duplicates
# Aggressive purging for high heterozygosity
hifiasm -o asm -t 32 -l 2 reads.hifi.fastq.gz
# Minimal purging for inbred samples
hifiasm -o asm -t 32 -l 0 reads.hifi.fastq.gz
Ultra-Long ONT Integration
# Combine HiFi accuracy with ONT length
hifiasm -o hybrid_asm -t 32 \
--ul ont_ultralong.fastq.gz \
hifi_reads.fastq.gz
# UL reads help span complex repeatsAssembly Stats
# Quick stats with seqkit
seqkit stats assembly.fasta
# Detailed with assembly-stats
assembly-stats assembly.fasta
# QUAST assessment
quast.py -o quast_output assembly.fasta
# BUSCO completeness
busco -i assembly.fasta -l mammalia_odb10 -o busco_out -m genome
Memory and Runtime
| Genome Size | HiFi Coverage | RAM | Time (32 cores) | |-------------|---------------|-----|-----------------| | 3 Gb | 30x | ~200 GB | 12-24 hours | | 3 Gb | 60x | ~400 GB | 24-48 hours | | 500 Mb | 40x | ~64 GB | 2-4 hours |
Python Wrapper
import subprocess
from pathlib import Path
def run_hifiasm(hifi_reads, output_prefix, threads=32, purge_level=0,
hic_r1=None, hic_r2=None, ul_reads=None):
cmd = ['hifiasm', '-o', output_prefix, '-t', str(threads), '-l', str(purge_level)]
if hic_r1 and hic_r2:
cmd.extend(['--h1', hic_r1, '--h2', hic_r2])
if ul_reads:
cmd.extend(['--ul', ul_reads])
cmd.append(hifi_reads)
subprocess.run(cmd, check=True)
gfa = Path(f'{output_prefix}.bp.p_ctg.gfa')
fasta = Path(f'{output_prefix}.fasta')
with open(fasta, 'w') as out:
with open(gfa) as f:
for line in f:
if line.startswith('S'):
parts = line.strip().split('\t')
out.write(f'>{parts[1]}\n{parts[2]}\n')
return fasta
# Example
assembly = run_hifiasm('sample.hifi.fq.gz', 'sample_asm', threads=48, hic_r1='hic_R1.fq.gz', hic_r2='hic_R2.fq.gz')Troubleshooting
| Issue | Solution | |-------|----------| | High duplication | Increase purge level (-l 2) | | Missing haplotypes | Add Hi-C or trio data for phasing | | Memory errors | Reduce -D parameter or downsample reads | | Fragmented assembly | Check read quality; consider UL ONT addition |
Related Skills
- genome-assembly/assembly-qc - QUAST and BUSCO
- genome-assembly/scaffolding - YaHS Hi-C scaffolding
- genome-assembly/contamination-detection - CheckM2 decontamination
- long-read-sequencing/read-qc - HiFi quality control
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
Read more
<!--
COPYRIGHT NOTICE
This file is part of the "Universal Biomedical Skills" project.
Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
All Rights Reserved.
#
This code is proprietary and confidential.
Unauthorized copying of this file, via any medium is strictly prohibited.
#
Provenance: Authenticated by MD BABU MIA
-->
--- name: bio-genome-assembly-hifi-assembly description: High-quality genome assembly from PacBio HiFi reads using hifiasm with phasing support. Use when building reference-quality diploid assemblies from HiFi data, especially with trio or Hi-C phasing for fully resolved haplotypes. tool_type: cli primary_tool: hifiasm measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
---
HiFi Assembly
Basic Assembly
# Primary assembly (single haplotype consensus)
hifiasm -o output_prefix -t 32 reads.hifi.fastq.gz
# Output files:
# output_prefix.bp.p_ctg.gfa - Primary contigs
# output_prefix.bp.a_ctg.gfa - Alternate contigs
# output_prefix.bp.hap1.p_ctg.gfa - Haplotype 1 (if phased)
# output_prefix.bp.hap2.p_ctg.gfa - Haplotype 2 (if phased)
# Convert GFA to FASTA
awk '/^S/{print ">"$2;print $3}' output_prefix.bp.p_ctg.gfa > assembly.fastaTrio-Binned Phasing
# With parental short reads for trio binning
hifiasm -o trio_asm -t 32 \
-1 paternal.yak \
-2 maternal.yak \
child.hifi.fastq.gz
# Create yak databases from parental Illumina reads first
yak count -b37 -t16 -o paternal.yak paternal_R1.fq.gz paternal_R2.fq.gz
yak count -b37 -t16 -o maternal.yak maternal_R1.fq.gz maternal_R2.fq.gzHi-C Phasing
# Use Hi-C reads for phasing (no parents needed)
hifiasm -o hic_asm -t 32 \
--h1 hic_R1.fastq.gz \
--h2 hic_R2.fastq.gz \
reads.hifi.fastq.gz
# Produces fully phased hap1 and hap2 assembliesKey Parameters
| Parameter | Default | Description | |-----------|---------|-------------| | -t | 1 | Threads | | -l | 0 | Purge level (0=none, 1=light, 2=aggressive) | | -s | 0.55 | Similarity threshold for duplicate detection | | --primary | - | Output primary contigs only (no alternates) | | --n-hap | 2 | Expected number of haplotypes | | -D | 5.0 | Drop reads with depth > D*average | | -N | 100 | Consider up to N overlaps for each read |
Purge Duplicates
# Aggressive purging for high heterozygosity hifiasm -o asm -t 32 -l 2 reads.hifi.fastq.gz # Minimal purging for inbred samples hifiasm -o asm -t 32 -l 0 reads.hifi.fastq.gz
Ultra-Long ONT Integration
# Combine HiFi accuracy with ONT length
hifiasm -o hybrid_asm -t 32 \
--ul ont_ultralong.fastq.gz \
hifi_reads.fastq.gz
# UL reads help span complex repeatsAssembly Stats
# Quick stats with seqkit seqkit stats assembly.fasta # Detailed with assembly-stats assembly-stats assembly.fasta # QUAST assessment quast.py -o quast_output assembly.fasta # BUSCO completeness busco -i assembly.fasta -l mammalia_odb10 -o busco_out -m genome
Memory and Runtime
| Genome Size | HiFi Coverage | RAM | Time (32 cores) | |-------------|---------------|-----|-----------------| | 3 Gb | 30x | ~200 GB | 12-24 hours | | 3 Gb | 60x | ~400 GB | 24-48 hours | | 500 Mb | 40x | ~64 GB | 2-4 hours |
Python Wrapper
import subprocess
from pathlib import Path
def run_hifiasm(hifi_reads, output_prefix, threads=32, purge_level=0,
hic_r1=None, hic_r2=None, ul_reads=None):
cmd = ['hifiasm', '-o', output_prefix, '-t', str(threads), '-l', str(purge_level)]
if hic_r1 and hic_r2:
cmd.extend(['--h1', hic_r1, '--h2', hic_r2])
if ul_reads:
cmd.extend(['--ul', ul_reads])
cmd.append(hifi_reads)
subprocess.run(cmd, check=True)
gfa = Path(f'{output_prefix}.bp.p_ctg.gfa')
fasta = Path(f'{output_prefix}.fasta')
with open(fasta, 'w') as out:
with open(gfa) as f:
for line in f:
if line.startswith('S'):
parts = line.strip().split('\t')
out.write(f'>{parts[1]}\n{parts[2]}\n')
return fasta
# Example
assembly = run_hifiasm('sample.hifi.fq.gz', 'sample_asm', threads=48, hic_r1='hic_R1.fq.gz', hic_r2='hic_R2.fq.gz')Troubleshooting
| Issue | Solution | |-------|----------| | High duplication | Increase purge level (-l 2) | | Missing haplotypes | Add Hi-C or trio data for phasing | | Memory errors | Reduce -D parameter or downsample reads | | Fragmented assembly | Check read quality; consider UL ONT addition |
Related Skills
- genome-assembly/assembly-qc - QUAST and BUSCO
- genome-assembly/scaffolding - YaHS Hi-C scaffolding
- genome-assembly/contamination-detection - CheckM2 decontamination
- long-read-sequencing/read-qc - HiFi quality control
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
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Questions about bio-genome-assembly-hifi-assembly.
- Which coding agents can use bio-genome-assembly-hifi-assembly?
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