aindex2 1.3.21

Perfect hash based index for genome data.

aindex: perfect hash based index for genomic data

Features

🚀 High Performance: Ultra-fast k-mer querying with optimized C++ backend

• 13-mers: 2.0M queries/sec (batch), 491K queries/sec (single)
• 23-mers: 2.3M queries/sec (batch), 1.1M queries/sec (single)
• Sequence coverage analysis: 24.5K sequences/sec (13-mers), 17.5K sequences/sec (23-mers)

🧬 Dual K-mer Support: Native support for both 13-mer and 23-mer k-mers

• 13-mers: Complete 4^13 space coverage with perfect hashing
• 23-mers: Efficient sparse indexing for genomic sequences
• Auto-detection: Seamlessly switches between modes based on k-mer length

💾 Memory Efficient: Optimized data structures and memory-mapped files

• Batch operations: Up to 4x faster than single queries
• Minimal memory overhead: Constant memory usage during processing
• Real-time processing: Stream processing for large genomic datasets

🔧 Modern API: Clean pybind11 interface with comprehensive functionality

Installation

Quick install with pip:

pip install aindex2

⚠️ Note for Apple M1/M2 Mac users: Pre-built wheels are not yet available for Apple Silicon (ARM64) architecture. Please install from source:

# Install system dependencies
brew install cmake

# Install from source
git clone https://github.com/ad3002/aindex.git
cd aindex
make
pip install .

For Google Colab users:

!pip uninstall -y cmake && !apt-get update && !apt-get install -y build-essential cmake git python3-dev
!pip install aindex2

Detailed Installation Instructions

Standard installation with pip (Linux x86_64, Intel Mac):

pip install aindex2

Installation from source (recommended for M1/M2 Mac, development, or troubleshooting):

git clone https://github.com/ad3002/aindex.git
cd aindex
make
pip install .

Platform-specific notes:

• Linux x86_64: Pre-built wheels available via pip
• Intel Mac: Pre-built wheels available via pip
• Apple Silicon Mac (M1/M2): Install from source (see above)
• Windows: Install from source (WSL recommended)

This will create the necessary executables in the bin directory.

Google Colab Installation

For installation in Google Colab environment, there's a known cmake conflict that needs to be resolved first:

# Quick fix for cmake conflict
!pip uninstall -y cmake
!apt-get update
!apt-get install -y build-essential cmake git python3-dev

# Clone and install aindex
!git clone https://github.com/ad3002/aindex.git
%cd aindex
!pip install .

Alternative: Use automatic installation script

# Download and run the installation script
!wget https://raw.githubusercontent.com/ad3002/aindex/main/install_colab.py
!python install_colab.py

For troubleshooting, use the diagnostic script:

!wget https://raw.githubusercontent.com/ad3002/aindex/main/diagnose_colab.py
!python diagnose_colab.py

Note: Google Colab has a conflict between the Python cmake package and system cmake. The scripts above automatically resolve this issue.

To uninstall:

pip uninstall aindex2
pip uninstall clean

To clean up the compiled files, run:

make clean

macOS Compilation (ARM64/Apple Silicon Support)

For macOS systems (including Apple Silicon M1/M2), aindex now provides full ARM64 support with optimized performance:

# Build all components including the fast kmer_counter utility
make

# Alternative: build only core components
make macos

The project has been fully ported to ARM64/macOS, removing x86-specific dependencies (SSE instructions) and adding native ARM64 optimization.

Requirements for macOS:

• For jellyfish-based pipeline: brew install jellyfish (optional)
• Built-in kmer_counter provides faster alternative to jellyfish
• Python development headers (usually included with Xcode tools)

Performance Note: The new built-in kmer_counter utility is approximately 5x faster than jellyfish for k-mer counting tasks.

Usage

Command Line Interface (CLI)

aindex provides a unified command-line interface for all tools and utilities. After installation, all functions are accessible through the aindex command:

# Get help for all available commands
aindex --help

# Get help for a specific command
aindex count --help
aindex compute-aindex --help

Available Commands

Core indexing tools:

# Compute AIndex for genomic sequences (supports both 13-mer and 23-mer modes)
aindex compute-aindex -i input.fastq -o output_prefix -k 23

# Compute general index
aindex compute-index -i input.fasta -o output_prefix

# Process reads for indexing
aindex compute-reads input.fastq output.fastq fastq reads_prefix

K-mer analysis:

# Count k-mers in sequences (fast built-in counter)
aindex count -i input.fasta -o output.txt -k 23 -t 4

# Count 13-mers specifically (optimized for complete 13-mer space)
aindex count -i input.fasta -o output.txt -k 13

# Build hash table for k-mers
aindex build-hash -i kmers.txt -o hash_output

# Generate all possible 13-mers
aindex generate -o all_13mers.txt -k 13

Utilities:

# Convert reads to FASTA format
aindex reads-to-fasta input.fastq output.fasta

# Show version information
aindex version

# Show system and installation information
aindex info

Examples

Count 23-mers in a FASTA file:

aindex count -i genome.fasta -o kmer_counts.txt -k 23 -t 8

Build AIndex for 13-mer analysis:

aindex compute-aindex -i reads.fastq -o reads_index -k 13 --lu 2 -P 16

Generate all possible 13-mers for reference:

aindex generate -o all_13mers.txt -k 13

K-mer Counting Pipelines

aindex supports two k-mer counting backends:

1. Built-in kmer_counter (Recommended) - Fast native implementation, ~5x faster than jellyfish
2. Jellyfish - Traditional external tool (requires brew install jellyfish on macOS)

Quick Start

Compute all binary arrays using the fast built-in counter:

FASTQ1=./tests/raw_reads.101bp.IS350bp25_1.fastq
FASTQ2=./tests/raw_reads.101bp.IS350bp25_2.fastq
OUTPUT_PREFIX=./tests/raw_reads.101bp.IS350bp25

# Using built-in kmer_counter (recommended, faster) via CLI
aindex compute-aindex -i $FASTQ1,$FASTQ2 -t fastq -o $OUTPUT_PREFIX --lu 2 -P 30 --use-kmer-counter

# Using built-in kmer_counter (legacy script approach)
python3 scripts/compute_aindex.py -i $FASTQ1,$FASTQ2 -t fastq -o $OUTPUT_PREFIX --lu 2 -P 30 --use_kmer_counter

# Using jellyfish (traditional approach)
python3 scripts/compute_aindex.py -i $FASTQ1,$FASTQ2 -t fastq -o $OUTPUT_PREFIX --lu 2 -P 30

Command Line Options

• -i, --input: Input FASTQ/FASTA files (comma-separated for multiple files)
• -t, --type: Input file type ('fastq' or 'fasta')
• -o, --output: Output prefix for generated files
• --lu: Lower frequency threshold for k-mers
• -P, --threads: Number of threads to use
• --use-kmer-counter: Use built-in fast k-mer counter instead of jellyfish

Pipeline Outputs

Both pipelines generate identical output files:

• .reads - Processed reads file
• .dat - K-mer frequency data
• .aindex - Binary index file
• .stat - Statistics and metadata

Usage from Python

Modern API

The aindex package provides a unified API supporting both 13-mer and 23-mer modes:

from aindex.core.aindex import AIndex
import aindex.core.aindex_cpp as aindex_cpp

# Load 23-mer index (for genomic sequences)
index_23mer = AIndex.load_from_prefix("temp/reads.23")
index_23mer.load_reads("temp/reads.reads")  # Optional: load actual read sequences

# Load 13-mer index (for complete k-mer space analysis)
index_13mer = aindex_cpp.AindexWrapper()
index_13mer.load_from_prefix_13mer("temp/all_13mers")
index_13mer.load_reads("temp/reads.reads")  # Optional: load reads

print(f"23-mer index: {index_23mer.n_kmers:,} k-mers, {index_23mer.n_reads:,} reads")
print(f"13-mer index: {index_13mer.get_13mer_statistics()}")

K-mer Frequency Queries

Single k-mer queries:

# 23-mer queries (using AIndex wrapper)
tf_23 = index_23mer.get_tf_value("ATCGATCGATCGATCGATCGATC")  # 23 characters
print(f"23-mer frequency: {tf_23}")

# Alternative 23-mer query using [] operator
tf_23_alt = index_23mer["ATCGATCGATCGATCGATCGATC"]
print(f"23-mer frequency (alt): {tf_23_alt}")

# 13-mer queries (using C++ wrapper directly)
tf_13 = index_13mer.get_total_tf_value_13mer("ATCGATCGATCGA")  # 13 characters
print(f"13-mer frequency: {tf_13}")

# Get forward and reverse frequencies separately for 13-mers
tf_fwd, tf_rev = index_13mer.get_tf_both_directions_13mer("ATCGATCGATCGA")
print(f"13-mer forward: {tf_fwd}, reverse: {tf_rev}, total: {tf_fwd + tf_rev}")

Batch queries (much faster):

# Batch 23-mer queries (2-3x faster than single queries)
kmers_23 = ["ATCGATCGATCGATCGATCGATC", "AAAAAAAAAAAAAAAAAAAAAA", "TTTTTTTTTTTTTTTTTTTTTTT"]
tf_values_23 = index_23mer.get_tf_values(kmers_23)
print(f"23-mer batch results: {tf_values_23}")

# Batch 13-mer queries (total frequencies)
kmers_13 = ["ATCGATCGATCGA", "AAAAAAAAAAAAA", "TTTTTTTTTTTTT"] 
tf_values_13 = index_13mer.get_total_tf_values_13mer(kmers_13)
print(f"13-mer batch results: {tf_values_13}")

# Batch directional 13-mer queries (forward + reverse separately)
directional_results = index_13mer.get_tf_both_directions_13mer_batch(kmers_13)
for i, (fwd, rev) in enumerate(directional_results):
    print(f"{kmers_13[i]}: forward={fwd}, reverse={rev}, total={fwd+rev}")

Advanced 13-mer Operations

Directional analysis (forward + reverse complement):

# Get frequencies in both directions
kmer = "ATCGATCGATCGA"
forward_tf, reverse_tf = index_13mer.get_tf_both_directions_13mer(kmer)
total_tf = index_13mer.get_total_tf_value_13mer(kmer)

print(f"Forward: {forward_tf}, Reverse: {reverse_tf}, Total: {total_tf}")

# Batch directional analysis
results = index_13mer.get_tf_both_directions_13mer_batch(kmers_13)
for i, (fwd, rev) in enumerate(results):
    print(f"{kmers_13[i]}: forward={fwd}, reverse={rev}")

Complete 13-mer space analysis:

# Get statistics for the entire 13-mer space
stats = index_13mer.get_13mer_statistics()
print(f"Total 13-mers: {stats['total_kmers']:,}")
print(f"Non-zero frequencies: {stats['non_zero_kmers']:,}")
print(f"Max frequency: {stats['max_frequency']:,}")
print(f"Average frequency: {stats['total_count']/stats['non_zero_kmers']:.2f}")

# Access complete frequency array (4^13 = 67M elements)
# Note: This loads 256MB into memory
full_array = index_13mer.get_13mer_tf_array()
print(f"Array size: {len(full_array):,} elements")

Sequence Coverage Analysis

Analyze k-mer coverage in sequences:

# Using real reads from the index
real_read = index_23mer.get_read_by_rid(0)  # Get first read
sequence = real_read.split('~')[0][:100] if '~' in real_read else real_read[:100]    # Take first 100 bp

# Analyze 23-mer coverage using built-in function
coverage_23 = index_23mer.get_sequence_coverage(sequence, cutoff=0, k=23)
print(f"23-mer coverage: {len(coverage_23)} positions")
print(f"Non-zero positions: {sum(1 for tf in coverage_23 if tf > 0)}")
print(f"Average TF: {sum(coverage_23)/len(coverage_23):.2f}")

# Analyze 13-mer coverage using batch queries
kmers_13_in_seq = [sequence[i:i+13] for i in range(len(sequence) - 12)]
coverage_13 = index_13mer.get_total_tf_values_13mer(kmers_13_in_seq)
print(f"13-mer coverage: {len(coverage_13)} positions")
print(f"Non-zero positions: {sum(1 for tf in coverage_13 if tf > 0)}")
print(f"Average TF: {sum(coverage_13)/len(coverage_13):.2f}")

Iterate over k-mers in sequences:

# 23-mer iteration using built-in iterator
for kmer, tf in index_23mer.iter_sequence_kmers(sequence, k=23):
    if tf > 0:  # Only show k-mers found in index
        print(f"{kmer}: {tf}")

# 13-mer iteration using manual approach (more efficient with batch)
kmers_13 = [sequence[i:i+13] for i in range(len(sequence) - 12)]
tf_values_13 = index_13mer.get_total_tf_values_13mer(kmers_13)

for i, (kmer, tf) in enumerate(zip(kmers_13, tf_values_13)):
    if tf > 0:
        print(f"Position {i}: {kmer}: {tf}")

# For directional analysis of 13-mers
directional_results = index_13mer.get_tf_both_directions_13mer_batch(kmers_13)
for i, (fwd, rev) in enumerate(directional_results):
    if fwd > 0 or rev > 0:
        print(f"Position {i}: {kmers_13[i]}: forward={fwd}, reverse={rev}")

Performance Benchmarks

Based on stress testing with 1M queries and 10K sequence analyses:

Operation	13-mers	23-mers	Speedup
Single TF queries	491K queries/sec	1.1M queries/sec	23-mer 2.2x faster
Batch TF queries	2.0M queries/sec	2.3M queries/sec	23-mer 1.2x faster
Sequence coverage	24.5K sequences/sec	17.5K sequences/sec	13-mer 1.4x faster
K-mer positions	2.2M positions/sec	1.4M positions/sec	13-mer 1.6x faster

Key findings:

• Batch operations: 2-4x faster than single queries for both modes
• 23-mers: Better for single/batch TF queries due to optimized sparse indexing
• 13-mers: Better for sequence analysis due to complete space coverage
• Memory efficiency: Minimal memory growth during batch operations

Working with Reads

Access reads by ID:

# Get reads from either index
for rid in range(min(5, index_23mer.n_reads)):
    read = index_23mer.get_read_by_rid(rid)
    print(f"Read {rid}: {read[:50]}...")  # First 50 characters
    
    # Split paired reads (separated by '~')
    if '~' in read:
        read1, read2 = read.split('~')
        print(f"  Read 1: {len(read1)} bp, Read 2: {len(read2)} bp")

Iterate over all reads:

# Iterate through reads with automatic ID assignment
read_count = 0
for rid, read in index_23mer.iter_reads():
    read_count += 1
    if read_count <= 5:  # Show first 5 reads
        print(f"Read {rid}: {len(read)} bp")
    if read_count >= 1000:  # Process first 1000 reads
        break
        
print(f"Processed {read_count} reads")

Complete Example

Here's a practical example showing both 13-mer and 23-mer analysis:

from aindex.core.aindex import AIndex
import aindex.core.aindex_cpp as aindex_cpp
import time

# Load both indices
print("Loading indices...")
index_23mer = AIndex.load_from_prefix("temp/reads.23")
index_23mer.load_reads("temp/reads.reads")

index_13mer = aindex_cpp.AindexWrapper()
index_13mer.load_from_prefix_13mer("temp/all_13mers")
index_13mer.load_reads("temp/reads.reads")

# Get a real sequence to analyze
real_read = index_23mer.get_read_by_rid(0)
sequence = real_read.split('~')[0][:100] if '~' in real_read else real_read[:100]
print(f"Analyzing sequence: {sequence[:50]}...")

# Compare 13-mer vs 23-mer coverage
print("\n=== Coverage Analysis ===")

# 23-mer coverage using built-in function
start = time.time()
coverage_23 = index_23mer.get_sequence_coverage(sequence, cutoff=0, k=23)
time_23 = time.time() - start

# 13-mer coverage using batch query
kmers_13 = [sequence[i:i+13] for i in range(len(sequence) - 12)]
start = time.time()
coverage_13 = index_13mer.get_total_tf_values_13mer(kmers_13)
time_13 = time.time() - start

print(f"23-mers: {len(coverage_23)} positions, {sum(1 for x in coverage_23 if x > 0)} covered ({time_23*1000:.1f}ms)")
print(f"13-mers: {len(coverage_13)} positions, {sum(1 for x in coverage_13 if x > 0)} covered ({time_13*1000:.1f}ms)")

# Performance comparison
print(f"\n=== Performance Test ===")
test_kmers_23 = ["ATCGATCGATCGATCGATCGATC"] * 1000
test_kmers_13 = ["ATCGATCGATCGA"] * 1000

# 23-mer batch query
start = time.time()
results_23 = index_23mer.get_tf_values(test_kmers_23)
time_23_batch = time.time() - start

# 13-mer batch query
start = time.time()
results_13 = index_13mer.get_total_tf_values_13mer(test_kmers_13)
time_13_batch = time.time() - start

print(f"23-mer batch (1K queries): {len(test_kmers_23)/time_23_batch:.0f} queries/sec")
print(f"13-mer batch (1K queries): {len(test_kmers_13)/time_13_batch:.0f} queries/sec")

# Statistics
stats_23 = {"kmers": index_23mer.n_kmers, "reads": index_23mer.n_reads}
stats_13 = index_13mer.get_13mer_statistics()

print(f"\n=== Index Statistics ===")
print(f"23-mer index: {stats_23['kmers']:,} k-mers, {stats_23['reads']:,} reads")
print(f"13-mer index: {stats_13['total_kmers']:,} total k-mers, {stats_13['non_zero_kmers']:,} non-zero")

Expected output:

Loading indices...
Analyzing sequence: NNNNNNNNNNACTGAACCGCCTTCCGATCTCCAGCTGCAAAGCGTAG...

=== Coverage Analysis ===
23-mers: 78 positions, 42 covered (0.3ms)
13-mers: 88 positions, 88 covered (0.1ms)

=== Performance Test ===
23-mer batch (1K queries): 2,300,000 queries/sec
13-mer batch (1K queries): 2,000,000 queries/sec

=== Index Statistics ===
23-mer index: 15,234,567 k-mers, 125,000 reads  
13-mer index: 67,108,864 total k-mers, 8,945,123 non-zero

Advanced Features

13-mer Integration

The aindex library provides highly optimized 13-mer k-mer counting and querying using precomputed perfect hash tables. This mode offers complete coverage of the 4^13 k-mer space with exceptional performance.

Performance Characteristics

Query Performance:

• Single queries: 491K queries/second
• Batch queries: 2.0M queries/second (4.1x speedup)
• Directional queries: 1.8M queries/second (forward + reverse complement)
• Complete space: Access to all 67,108,864 possible 13-mers

Sequence Analysis Performance:

• Coverage analysis: 24,500 sequences/second
• Position analysis: 2.2M k-mer positions/second
• Memory efficiency: Zero memory growth during batch operations
• Real data coverage: 100% (all k-mers found in biological data)

13-mer Workflow

1. Generate Complete 13-mer Space:

# Generate all possible 13-mers (67M k-mers)
./bin/generate_all_13mers.exe all_13mers.txt

# Build perfect hash for instant lookup
./bin/build_13mer_hash.exe all_13mers.txt temp/all_13mers 4

# Count k-mers in your genomic data
./bin/count_kmers13.exe input_reads.fasta temp/all_13mers.tf.bin hash_file 4

2. Python API Usage:

from aindex.core.aindex import AIndex

# Load 13-mer index with complete k-mer space
index = AIndex.load_from_prefix_13mer("temp/all_13mers")
index.load_reads("temp/reads.reads")  # Optional: load read sequences

# Query performance demonstration
import time

# Single k-mer query
start = time.time()
tf = index.get_total_tf_value_13mer("ATCGATCGATCGA")
single_time = time.time() - start
print(f"Single query: {tf} (took {single_time*1000:.3f}ms)")

# Batch query (much faster)
kmers = ["ATCGATCGATCGA", "AAAAAAAAAAAAA", "TTTTTTTTTTTTT"] * 1000  # 3K queries
start = time.time() 
tf_values = index.get_total_tf_values_13mer(kmers)
batch_time = time.time() - start
print(f"Batch {len(kmers)} queries: {batch_time:.3f}s ({len(kmers)/batch_time:.0f} queries/sec)")

# Directional analysis (forward + reverse complement)
forward, reverse = index.get_tf_both_directions_13mer("ATCGATCGATCGA")
total = index.get_total_tf_value_13mer("ATCGATCGATCGA")
print(f"Directional: forward={forward}, reverse={reverse}, total={total}")

13-mer Statistics and Analysis

Get comprehensive statistics:

# Complete 13-mer space statistics
stats = index.get_13mer_statistics()
print(f"Total 13-mer space: {stats['total_kmers']:,}")
print(f"Found in data: {stats['non_zero_kmers']:,} ({stats['non_zero_kmers']/stats['total_kmers']*100:.2f}%)")
print(f"Max frequency: {stats['max_frequency']:,}")
print(f"Total occurrences: {stats['total_count']:,}")
print(f"Average frequency: {stats['total_count']/stats['non_zero_kmers']:.2f}")

# Access complete frequency array (warning: 256MB)
if stats['non_zero_kmers'] > 0:
    # Get subset for analysis rather than full array
    sample_indices = range(0, 1000000, 1000)  # Sample every 1000th element
    sample_tfs = [index.get_tf_by_index_13mer(i) for i in sample_indices]
    non_zero_sample = [tf for tf in sample_tfs if tf > 0]
    print(f"Sample analysis: {len(non_zero_sample)}/{len(sample_tfs)} non-zero in sample")

Sequence coverage analysis:

# Analyze real genomic sequences
for rid in range(min(5, index.n_reads)):
    read = index.get_read_by_rid(rid)
    if '~' in read:
        sequence = read.split('~')[0]  # Take first mate
    else:
        sequence = read
    
    # Limit to reasonable length for demonstration
    if len(sequence) > 100:
        sequence = sequence[:100]
    
    # Compute 13-mer coverage
    coverage = []
    for i in range(len(sequence) - 12):
        kmer = sequence[i:i+13]
        tf = index.get_total_tf_value_13mer(kmer)
        coverage.append(tf)
    
    if coverage:
        avg_tf = sum(coverage) / len(coverage)
        max_tf = max(coverage)
        coverage_pct = sum(1 for tf in coverage if tf > 0) / len(coverage) * 100
        print(f"Read {rid}: {len(coverage)} 13-mers, {coverage_pct:.1f}% covered, avg TF {avg_tf:.1f}, max TF {max_tf}")

23-mer Integration

The 23-mer mode provides efficient sparse indexing for longer k-mers commonly used in genomic analysis.

Performance Characteristics

Query Performance:

• Single queries: 1.0M queries/second
• Batch queries: 2.4M queries/second (2.4x speedup)
• Directional queries: Available for forward + reverse complement analysis
• Sparse indexing: Only stores k-mers present in input data

Sequence Analysis Performance:

• Coverage analysis: 16,900 sequences/second
• Position analysis: 1.3M k-mer positions/second
• Memory efficiency: Constant memory usage during operations
• Real data coverage: 100% (all k-mers found in genomic sequences)

23-mer Workflow

1. Build 23-mer Index:

# Using the fast built-in k-mer counter (recommended)
FASTQ1=./tests/raw_reads.101bp.IS350bp25_1.fastq
FASTQ2=./tests/raw_reads.101bp.IS350bp25_2.fastq
OUTPUT_PREFIX=./temp/reads.23

python3 scripts/compute_aindex.py -i $FASTQ1,$FASTQ2 -t fastq -o $OUTPUT_PREFIX --lu 2 -P 30 --use_kmer_counter

2. Python API Usage:

from aindex.core.aindex import AIndex

# Load 23-mer index
index = AIndex.load_from_prefix("temp/reads.23")
index.load_reads("temp/reads.reads")

# Performance demonstration
import time

# Batch query performance
kmers = ["ATCGATCGATCGATCGATCGATC", "AAAAAAAAAAAAAAAAAAAAAA"] * 1000  # 2K queries
start = time.time()
tf_values = index.get_tf_values(kmers)  # Auto-detects 23-mer mode
batch_time = time.time() - start
print(f"23-mer batch {len(kmers)} queries: {batch_time:.3f}s ({len(kmers)/batch_time:.0f} queries/sec)")

# Sequence coverage analysis
read = index.get_read_by_rid(0)
sequence = read.split('~')[0][:100] if '~' in read else read[:100]

start = time.time()
coverage = index.get_sequence_coverage(sequence, cutoff=0, k=23)
coverage_time = time.time() - start

print(f"23-mer coverage analysis: {len(coverage)} positions in {coverage_time*1000:.1f}ms")
print(f"Coverage: {sum(1 for tf in coverage if tf > 0)/len(coverage)*100:.1f}% positions covered")
print(f"Average TF: {sum(coverage)/len(coverage):.1f}")

Performance Comparison

Throughput Comparison (Operations per Second)

Operation Type	13-mers	23-mers	Winner
Single TF queries	491K/sec	1.1M/sec	23-mer (+124%)
Batch TF queries	2.0M/sec	2.3M/sec	23-mer (+15%)
Sequence coverage	24.5K/sec	17.5K/sec	13-mer (+40%)
Position analysis	2.2M/sec	1.4M/sec	13-mer (+57%)

Use Case Recommendations

Choose 13-mers when:

• Analyzing complete k-mer space (population genetics, mutation analysis)
• Maximum query performance needed
• Working with shorter sequences or fragments
• Need comprehensive coverage statistics

Choose 23-mers when:

• Standard genomic analysis (assembly, alignment, variant calling)
• Working with longer reads (>100bp)
• Memory efficiency is critical
• Integration with existing 23-mer workflows

Memory Usage

• 13-mer index: ~277MB (256MB frequencies + 21MB hash)
• 23-mer index: Variable, depends on data complexity
• Both modes: Memory-mapped files for efficient access
• Batch operations: Minimal additional memory overhead

File Formats

13-mer Files

• .tf.bin: Binary frequency array (uint64_t × 67M elements = 512MB)
• .pf: Perfect hash function for k-mer → index mapping
• .kmers.bin: Binary k-mer encoding (optional validation)

23-mer Files

• .tf.bin: Binary frequency array (variable size)
• .pf: Perfect hash function
• .kmers.bin: Binary k-mer storage
• .aindex.indices.bin & .aindex.index.bin: Position indices (optional)