Software: Minia

Minia is a short-read assembler based on a de Bruijn graph, capable of assembling a human genome on a desktop computer in a day. The output of Minia is a set of contigs. Minia produces results of similar contiguity and accuracy to other de Bruijn assemblers (e.g. Velvet).



For the impatient

A typical Minia command line looks like:

./minia reads.fa 31 3 4500000 output_prefix


for a quick explanation of the parameters.

There is also a short manual in


KmerGenie can be used to determine the best k-mer size, minimum abundance of correct k-mers, and genome size estimation for your dataset.


Please use Biostars (dynamic FAQ system, click "New Post", top right corner) to report bugs or ask any question. (An archive of posts between 2012 and 2014 can be found here.)


Minia is mainly described in the WABI 2012 article. Different authors proposed and implemented improvements in a WABI 2013 article. Minia now uses these improvements by default.

Original article: Space-efficient and exact de Bruijn graph representation based on a Bloom filter


The de Bruijn graph data structure is widely used in next-generation sequencing (NGS). Many programs, e.g. de novo assemblers, rely on in-memory representation of this graph. However, current techniques for representing the de Bruijn graph of a human genome require a large amount of memory (> 30 GB).

We propose a new encoding of the de Bruijn graph, which occupies an order of magnitude less space than current representations. The encoding is based on a Bloom filter, with an additional structure to remove critical false positives. An assembly software implementing this structure, Minia, performed a complete de novo assembly of human genome short reads using 5.7 Gb of memory in 23 hours.


In this example, the de Bruijn graph consists of seven nodes (green). They are hashed and inserted in a Bloom filter. The Bloom filter creates false positives (black and red). Only the false positives that are immediate neighbors of the true nodes are critical for assembly (red). By storing only the Bloom filter and the set of critical false positives, we can perform assembly on the exact de Bruijn graph.

PDF and Citation

R. Chikhi, G. Rizk. Space-efficient and exact de Bruijn graph representation based on a Bloom filter, WABI 2012

 author = {Chikhi, Rayan and Rizk, Guillaume},
 title = {Space-Efficient and Exact de Bruijn Graph Representation Based on a Bloom Filter.},
 booktitle = {WABI},
 pages = {236-248},
 publisher = {Springer},
 series = {Lecture Notes in Computer Science},
 volume = 7534,
 year = 2012

Steps to reproduce the human genome assembly from the article

Download these raw human genome reads: SRX016231, convert them to FASTA and concatenate them into a single file (HG_reads_all_untrimed.fa).

Launch Minia as follows:

./minia HG_reads_all_untrimed.fa 31 3 2700000000 results_prefix


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


De Brujin graphs are widely used in bioinformatics for processing next-generation sequencing (NGS) data. Due to the 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 Chikhi and Rizk (WABI, 2012) that represents de Brujin graphs using Bloom filters. Our method requires 30% to 40% less memory with respect to their method, with insignificant impact to construction time. At the same time, our experiments showed a better query time compared to their method. This is, to our knowledge, the best practical representation for de Bruijn graphs.

PDF and Citation

K. Salikhov, G. Sacomoto and G. Kucherov. Using cascading Bloom filters to improve the memory usage for de Brujin graphs, WABI 2013

  title={Using cascading Bloom filters to improve the memory usage for de Brujin graphs},
  author={Salikhov, Kamil and Sacomoto, Gustavo and Kucherov, Gregory},
  booktitle={Algorithms in Bioinformatics},