# Using cascading Bloom filters to improve the memory usage for de Brujin graphs

arXiv: https://arxiv.org/abs/1302.7278 · HTML: https://mlsanity.com/paper/1302.7278/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2013.

## Built on by (most cited first, 1 of 1)

- [Cascaded Learned Bloom Filter for Optimizing Model-Filter Size Balance and Fast Rejection](https://mlsanity.com/paper/2502.03696/) (2025)

## Abstract (arXiv)

De Brujin graphs are widely used in bioinformatics for processing next-generation sequencing data. Due to a very large size of NGS datasets, it is essential to represent de Bruijn graphs compactly, and several approaches to this problem have been proposed recently. In this work, we show how to reduce the memory required by the algorithm of [3] that represents de Brujin graphs using Bloom filters. Our method requires 30% to 40% less memory with respect to the method of [3], with insignificant impact to construction time. At the same time, our experiments showed a better query time compared to [3]. This is, to our knowledge, the best practical representation for de Bruijn graphs.