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Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 248% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 114% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 30% | 0% |
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bash# 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.fasta
bash# 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.gz
bash# 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 assemblies
| 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 > Daverage | | -N | 100 | Consider up to N overlaps for each read |
bash# 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
bash# 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 repeats
bash# 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
| 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 |
pythonimport 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')
| 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 |
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