UMIC-seq-PacBio

Performance Optimizations (2025)

UMIC-seq-pacbio: Complete Pipeline

For PacBio data analysis, use the complete pipeline entry-point that handles the entire workflow from UMI extraction to final variant analysis:

python UMIC-seq-pacbio.py all \
  --input reads.fastq.gz \
  --probe probe.fasta \
  --reference reference.fasta \
  --output_dir /path/to/output

Required Arguments:

• --input: Input FASTQ file (can be .gz compressed)
• --probe: Probe FASTA file containing an approximately 50 bp sequence adjacent to the UMI
• --reference: Reference FASTA file containing the reference gene sequence
• --output_dir: Output directory where all results will be written

Optional Arguments (with defaults):

UMI Extraction:

• --umi_len (default: 52): Length of the UMI in base pairs
• --umi_loc (default: 'up'): Location of UMI relative to probe. Options: 'up' (upstream) or 'down' (downstream)
• --min_probe_score (default: 15): Minimum alignment score required for probe matching. For a 50bp probe, perfect match = 50. Lower values accept more mismatches.

Clustering:

• --fast (default: True): Use fast CD-HIT clustering (recommended)
• --slow: Use slow alignment-based clustering (alternative to --fast, legacy)
• --identity (default: 0.90): Sequence identity threshold for fast clustering (0-1). 0.90 = 90% identity = allows up to 10% mismatch. For a 52bp UMI, this allows ~5 mismatches.
• --aln_thresh (default: 0.47): Alignment score threshold for slow clustering (only used with --slow). Converts to integer score: 0.47 → 47. For a 52bp UMI with perfect match ≈ 104, threshold 47 ≈ 45% of perfect. Note: This is legacy and will be removed in future versions.

Cluster Filtering:

• --size_thresh (default: 10): Minimum number of PacBio reads required per cluster. Clusters with fewer reads are discarded. Lower values = more sensitive (detects rare variants), higher values = more conservative (only high-confidence variants).

Consensus Generation:

• --max_reads (default: 20): Maximum number of reads per cluster used for consensus generation. Uses first N reads from each cluster. More reads = better consensus quality but slower. Fewer reads = faster.

Performance:

• --max_workers (default: 4): Number of parallel workers for consensus generation and variant calling. Increase for faster processing if you have more CPU cores available.

Example with custom parameters:

python UMIC-seq-pacbio.py all \
  --input reads.fastq.gz \
  --probe probe.fasta \
  --reference reference.fasta \
  --output_dir /path/to/output \
  --umi_len 52 \
  --umi_loc up \
  --min_probe_score 30 \
  --identity 0.95 \
  --size_thresh 15 \
  --max_reads 30 \
  --max_workers 8

Pipeline Steps

The all command runs the complete pipeline:

1. UMI Extraction: Extract UMIs from PacBio reads
2. Clustering: Cluster similar UMIs using ultra-fast hash-based algorithm
3. Consensus Generation: Generate consensus sequences using abpoa
4. Variant Calling: Call variants using minimap2 and bcftools
5. Analysis: Generate detailed CSV with mutation analysis

Individual Commands

You can also run individual steps:

# Extract UMIs
python UMIC-seq-pacbio.py extract \
  --input reads.fastq.gz \
  --probe probe.fasta \
  --umi_len 52 \
  --output ExtractedUMIs.fasta \
  --umi_loc up \
  --min_probe_score 15

# Cluster UMIs
python UMIC-seq-pacbio.py cluster \
  --input_umi ExtractedUMIs.fasta \
  --input_reads reads.fastq.gz \
  --output_dir UMIclusterfull_fast \
  --aln_thresh 0.47 \
  --size_thresh 10

# Generate consensus sequences
python UMIC-seq-pacbio.py consensus \
  --input_dir UMIclusterfull_fast \
  --output_dir consensus_results \
  --max_reads 20 \
  --max_workers 4

# Call variants
python UMIC-seq-pacbio.py variants \
  --input_dir consensus_results \
  --reference reference.fasta \
  --output_dir variant_results \
  --combined_vcf combined_variants.vcf \
  --max_workers 4

# Analyze variants
python UMIC-seq-pacbio.py analyze \
  --input_vcf combined_variants.vcf \
  --reference reference.fasta \
  --output final_results.csv

### NGS Pool Counting (Illumina) with UMI matching and haplotypes

This repository includes an NGS pooling/counting module to match Illumina paired-end reads back to consensus haplotypes and count per-variant and per-haplotype occurrences per pool.

Key features:
- PEAR-based merging of R1/R2 per pool (fallback to on-the-fly merge)
- UMI extraction from assembled reads by taking the internal window (ignores first 22 and last 24 bases by default, these numbers should be configured for your reads)
- Circular, strand-aware UMI-to-consensus matching
- Per-variant counts (rows = VCF entries; columns = pools)
- Per-haplotype counts that preserve multi-mutations with amino acid mutations (non-synonymous only)
- Deduplicated by non-synonamous amino acid mutational identity

Requirements:
- PEAR installed and available in PATH (e.g., `conda install -c bioconda pear`)

Usage:
```bash
python UMIC-seq-pacbio.py ngs_count \
  --pools_dir /path/to/NGS_data \
  --consensus_dir /path/to/consensus \
  --variants_dir /path/to/variants \
  --probe /path/to/probe.fasta \
  --reference /path/to/reference.fasta \
  --umi_len 52 \
  --umi_loc up \
  --left_ignore 22 \
  --right_ignore 24 \
  --output /path/to/pool_variant_counts.csv

Inputs:

• pools_dir: directory containing one subfolder per pool; each subfolder has paired fastqs (*_R1*.fastq.gz and *_R2*.fastq.gz)
• consensus_dir: the consensus sequences directory (one FASTA per cluster)
• variants_dir: per-consensus VCFs generated by the variant calling step
• probe: probe FASTA (used only for logging; UMI extraction for Illumina uses your defined trimming rules)
• reference: reference FASTA for amino acid mapping

Outputs:

• pool_variant_counts.csv: wide table, rows = VCF entries (CHROM, POS, REF, ALT), columns = pools
• pool_haplotype_counts.csv: rows = consensus haplotypes (cluster), columns = pools
  • Columns: CONSENSUS, MUTATIONS (nucleotide, position-sorted), AA_MUTATIONS (non-synonymous only, grouped by codon)
  • Example AA format: S45F+Y76P; wild type is WT
• merged_on_nonsyn_counts.csv: haplotype counts merged by identical non-synonymous amino acid patterns; includes the contributing consensus IDs, distinct nucleotide mutation strings, and per-pool totals

Notes:

• For Illumina reads, UMIs are taken from the internal region of merged reads (default first 22 and last 24 bases ignored); the probe is not searched in Illumina.
• Amino acid numbering is 1-indexed; multiple nucleotide changes within a codon are combined into a single AA mutation.

Fitness Analysis

The fitness analysis module processes merged_on_nonsyn_counts.csv to calculate fitness from input/output pool comparisons and generate comprehensive visualizations.

Key Features:

• Filters variants by minimum input count threshold
• Calculates relative frequencies (column normalization)
• Computes log fitness ratios: log(rel_output / rel_input) for each input/output pair
• Calculates average fitness across all pairs
• Bootstrap confidence intervals for fitness estimates (when multiple replicates available)
• Generates mutability, epistasis, fitness distribution, reproducibility, and substitution matrix plots

Requirements:

• pandas, numpy, matplotlib, seaborn (typically available via conda/pip)

Usage:

python UMIC-seq-pacbio.py fitness \
  --input merged_on_nonsyn_counts.csv \
  --output_dir fitness_results/ \
  --input_pools pool1 pool2 \
  --output_pools pool3 pool4 \
  --min_input 10 \
  --aa_filter S

Arguments:

• --input: Path to merged_on_nonsyn_counts.csv (from ngs_count step)
• --output_dir: Directory to save plots and processed data
• --input_pools: Space-separated list of input pool names (e.g., pool1 pool2)
• --output_pools: Space-separated list of output pool names, paired with inputs (e.g., pool3 pool4)
• --min_input (default: 10): Minimum count threshold in input pools; variants below this in any input are filtered out
• --aa_filter (optional): Filter mutability plot to specific mutant amino acid (e.g., S for serine, P for proline, * for stop codons)

Outputs:

• fitness_analysis_results.csv: Processed data with fitness calculations, relative frequencies, mutation annotations, and bootstrap confidence intervals (if multiple replicates)
• mutability_plot.png: Average fitness at each position for Hamming 1 (single) mutants, relative to WT
• mutability_plot_{AA}.png: Mutability plot filtered to specific amino acid (if --aa_filter used)
• epistasis_plot.png: Scatter plot of sum of single mutant fitnesses (x-axis) vs double mutant fitness (y-axis)
  • Only includes double mutants where both constituent singles are present in the dataset
  • Includes additivity line and correlation coefficient
• fitness_distributions.png: Overlaid KDE plots for single mutants:
  • Stop codons
  • Proline mutations
  • All other mutations
• hamming_distributions.png: Overlaid KDE plots for fitness distributions at Hamming distances 1-5
• reproducibility_plot.png: Pairwise comparison heatmaps of fitness across replicate pairs (only generated if ≥2 replicates)
  • Shows correlation between replicates with density heatmaps (blue→red color scheme)
• substitution_matrix.png: Heatmap showing average fitness for each amino acid substitution type (21×21 matrix including stop codons)
  • Amino acids arranged by similarity (hydrophobic, polar, charged, etc.)
  • Red = beneficial, Blue = deleterious
• substitution_matrix.csv: Full substitution matrix data for further analysis

Example with multiple input/output pairs:

python UMIC-seq-pacbio.py fitness \
  --input merged_on_nonsyn_counts.csv \
  --output_dir fitness_results/ \
  --input_pools input_pool1 input_pool2 \
  --output_pools output_pool1 output_pool2 \
  --min_input 15

Notes:

• Fitness is calculated as log(rel_output / rel_input) where relative frequencies are column-normalized
• Average fitness is the mean across all input/output pairs
• Bootstrap confidence intervals (95% CI) are calculated using replicate-level resampling (1000 iterations) when multiple replicates are available
• Epistasis analysis requires both single and double mutants to be present in the dataset
• Mutability plots show average fitness aggregated across all single mutants at each position, relative to WT
• Reproducibility plot requires at least 2 replicate pairs

Threshold Selection Guide

Quick reference:

• High-quality data: Use --min_probe_score 30-40, --identity 0.90-0.95, --size_thresh 10-20
• Lower-quality data: Use --min_probe_score 15-20, --identity 0.85-0.90, --size_thresh 5-10
• Rare variant detection: Lower --size_thresh (e.g., 3)
• High-confidence only: Higher --size_thresh (e.g., 20)

Output Files

The pipeline generates:

• ExtractedUMIs.fasta: Extracted UMI sequences
• UMIclusterfull_fast/: Cluster files (cluster_1.fasta, cluster_2.fasta, ...)
• consensus_results/: Consensus sequences per cluster
• variant_results/: Individual VCF files per cluster
• combined_variants.vcf: Combined variant calls
• final_results.csv: Detailed analysis with amino acid mutations, Hamming distance, stop codons, and indels
• pool_variant_counts.csv: wide table, rows = VCF entries (CHROM, POS, REF, ALT), columns = pools
• pool_haplotype_counts.csv: rows = consensus haplotypes (cluster), columns = pools
  • Columns: CONSENSUS, MUTATIONS (nucleotide, position-sorted), AA_MUTATIONS (non-synonymous only, grouped by codon)
  • Example AA format: S45F+Y76P; wild type is WT
• merged_on_nonsyn_counts.csv: haplotype counts merged by identical non-synonymous amino acid patterns; includes the contributing consensus IDs, distinct nucleotide mutation strings, and per-pool totals
• fitness_analysis_results.csv: Processed fitness data with log ratios, annotations, and bootstrap CIs (from fitness analysis step)
• mutability_plot.png: Average fitness by position for single mutants (from fitness analysis step)
• epistasis_plot.png: Epistasis analysis plot (from fitness analysis step)
• fitness_distributions.png: Fitness distributions by mutation type (from fitness analysis step)
• hamming_distributions.png: Fitness distributions by Hamming distance (from fitness analysis step)
• reproducibility_plot.png: Replicate comparison heatmaps (from fitness analysis step, if ≥2 replicates)
• substitution_matrix.png: Amino acid substitution fitness heatmap (from fitness analysis step)
• substitution_matrix.csv: Substitution matrix data (from fitness analysis step)

Note that this pipeline has been used for both PacBio and ONT data.

OS requirements: Unix (MacOS or Linux)

Estimated wallclock runtime benchmarks:

• Generates a dictionary and UMI-gene counts in ~2h on an Apple M2 for library size 200k unique variants