A library for counting short DNA sequences for use in Bioinformatics. Helix consists of tools for data extraction as well as an ultra-low memory hash table called DNA Hash specialized for counting DNA sequences. DNA Hash stores sequence counts by their up2bit encoding - a two-way hash that exploits the fact that each DNA base need only 2 bits to be fully encoded. Accordingly, DNA Hash uses less memory than a lookup table that stores raw gene sequences. In addition, DNA Hash's layered Bloom filter eliminates the need to explicitly store counts for sequences that have only been seen once.

• Ultra-low memory footprint
• Compatible with FASTA and FASTQ formats
• Supports canonical sequence counting
• Open-source and free to use commercially

Note: The maximum sequence length is platform dependent. On a 64-bit machine, the max length is 31. On a 32-bit machine, the max length is 15.

Note: Due to the probabilistic nature of the Bloom filter, DNA Hash may over count sequences at a bounded rate.

Install into your project using Composer:

$ composer require scienide/helix

• PHP 7.4 or above

use Helix\DNAHash;
use Helix\Extractors\FASTA;
use Helix\Tokenizers\Canonical;
use Helix\Tokenizers\Kmer;

$extractor = new FASTA('example.fa');

$tokenizer = new Canonical(new Kmer(25));

$hashTable = new DNAHash(0.001);

foreach ($extractor as $sequence) {
    $tokens = $tokenizer->tokenize($sequence);

    foreach ($tokens as $token) {
        $hashTable->increment($token);
    }
}

$top10 = $hashTable->top(10);

print_r($top10);

Array
(
    [GCTATAAAAAGAAAATTTTGGAATA] => 19
    [ATTCCAAAATTTTCTTTTTATAGCC] => 19
    [TAAAAAGAAAATTTTGGAATAAAAA] => 18
    [ATAAAAAGAAAATTTTGGAATAAAA] => 18
    [TATAAAAAGAAAATTTTGGAATAAA] => 18
    [CTATAAAAAGAAAATTTTGGAATAA] => 18
    [AAATAATTTCAATTTTCTATCTCAA] => 17
    [AAAATAATTTCAATTTTCTATCTCA] => 17
    [CAAAATAATTTCAATTTTCTATCTC] => 17
    [AGATAGAAAATTGAAATTATTTTGA] => 17
)

To run the unit tests:

$ composer test

To run static code analysis:

$ composer analyze

To run the benchmarks:

$ composer benchmark

• [1] https://github.com/JohnLonginotto/ACGTrie/blob/master/docs/UP2BIT.md.
• [2] P. Melsted et al. (2011). Efficient counting of k-mers in DNA sequences using a bloom filter.
• [3] S. Deorowicz et al. (2015). KMC 2: fast and resource-frugal k-mer counting.