Minia 2.0.1 is Released

Minia 2.0.1 is Released

Long-term readers probably remember Minia by Rayan Chikhi et al., one of the most memory-efficient contig assembler from short reads. We first mentioned it here and then analyzed the code in this post - Minia Assembler and French Revolution. Minia later got reincorporated into GATB library.

Rayan recently announced in biostar about releasing Minia and DSK 2.x.x.

Minia is a low-memory short-read assembler for large genomes. It creates contigs.

DSK is a low-memory k-mer counter.

We have ported Minia and DSK to a new codebase that uses the GATB library. To make the change clear, from now on, Minia and DSK using the new codebase will have versions 2.x.x.

New features:

Minia 2.0.1,

Faster (multi-core parallelism)

Slightly more accurate (has coverage information in the graph, for better discrimination between sequencing errors and polymorphism)

Less disk usage (because of DSK)

Can output unitigs

DSK 2.0.1,

Faster (multi-core parallelism)

Less disk usage

comparable performance to KMC2 (we’re using their techniques :)

Download (Linux 64 bits):



For legacy, the final versions of Minia and DSK (old codebase) are and .

However we recommend using the 2.x.x versions, as results are expected to be identical (in the case of DSK) or slightly better (Minia), while 2.x.x performance is significantly better (2x-4x) than versions.

You might be tempted to reply to this post in case you find a bug, or an installation problem, etc… But please make a new Biostar post instead:

Post a question / bug report regarding Minia

Post a question / bug report regarding DSK

For a recent user review of Minia, please check -

Genome Assembly without the RAM

Assembling genomes with little memory.

De novo assembly is the construction of genomes without reference to existing known genomes. There is a wide variety of de novo assembly tools available for constructing genomes. Its well-known that many of these tools require substantial amounts of memory for assembling large genomes. For these assemblers the definition of large genome usually refers to something greater than about 1 Gb (gigabase, 1B nucleotides), although there are genomes that exceed this length by a considerable amount.

From experience we know that some assemblers require several hundred GB (gigabytes) up to 1 TB (terabyte) of RAM, per compute node, in a cluster computing environment, in order to assemble large genomes. This can be very expensive. Therefore, were evaluating a relatively new de novo assembler, Minia, as an alternative to some of the traditional assemblers. Apparently, Minia can assemble human scale genomes with about 5 GB RAM. Lets see what our evaluation discovered.

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Written by M. //