xenon-core 0.4.0

High-performance computational biology library in Rust.

xenon-core 🧬⚡

Xenon-Core v0.4.0: The Complete High-Performance Suite.

xenon-core is a blazingly fast bioinformatics extension that offloads computationally intensive tasks to Rust. It leverages manual parallel threading, zero-copy memory management, and optimized lookup tables to achieve speedups of up to ~500x over standard Python tools.

Why use Xenon-Core?
Stop waiting for Python loops to finish. Replace Biopython heavy lifting with Xenon-Core and get instant results.

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🚀 Performance Benchmarks

All benchmarks performed on a standard Mac ARM chip (M1/M2/M3).

1. DNA Utility Functions

Vs Biopython (Sequences of 1M - 3M bases)

Operation	Xenon-Core	Biopython	Speedup
GC Content	0.17 ms	3.64 ms	21x 🚀
Reverse Complement	0.25 ms	0.54 ms	2.2x
Translate (3M bp)	11.4 ms	101.5 ms	8.9x ⚡
Trim Low Quality	0.31 ms	16.4 ms	52x

2. K-mer Counting

Vs Pure Python and Rayon-based Implementations (50MB dataset)

Implementation	Time
Xenon-Core (Manual Parallel + Zero-Copy)	0.08 s
Previous Rust Implementation (File I/O)	0.22 s
Python Dictionary Loop	~36.00 s

> Xenon-Core is ~500x faster than pure Python loops.

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✨ Features

• Advanced K-mer Counting:
  • Zero-Copy architecture reads Python bytes directly without cloning.
  • Manual threading utilizing all CPU cores.
  • Returns efficient KmerCounts object (dict-like) to avoid massive allocation.
• Ultra-Fast Utilities:
  • gc_content: Direct byte scanning.
  • reverse_complement: SIMD-friendly table lookup.
  • translate: Standard Genetic Code translation with stop codon support.
  • trim_low_quality: Phred+33 aware quality trimming.
• Sequence Filtering:
  • filter_reads: Rapidly parse and filter FASTA/FASTQ files by length.

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📦 Installation

pip install xenon-core

Requires a Python 3.8+ environment.

Building from Source

git clone https://github.com/Dishant707/Xenon-core.git
cd Xenon-core
maturin develop --release

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🛠 Usage

import xenon_core

# 1. High-Performance Translation
dna = "ATGCGT..."
protein = xenon_core.translate(dna)
# > "MR..." (Stops at Stop Codons)

# 2. Parallel K-mer Counting (Zero-Copy)
# Pass a list of byte objects for maximum speed
seqs = [b"ATCG...", b"GGTA..."] 
counts = xenon_core.count_kmers_manual(seqs, k=5)

print(f"Count of 'AAAAA': {counts['AAAAA']}")
# Iterable like a dict
for kmer, count in counts.items():
    print(kmer, count)

# 3. Filtering Reads
# Quickly get list of reads longer than threshold
long_reads = xenon_core.filter_reads("genome.fa", min_length=150)

# 4. Utilities
gc = xenon_core.gc_content("ATCG...")
rev = xenon_core.reverse_complement("ATCG...")
trimmed = xenon_core.trim_low_quality("ATCG...", "IIII#...", 20)

📄 License

MIT