Bloocoo is a k-mer spectrum-based read error corrector, designed to correct large datasets with a very low memory footprint.
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README.md

Bloocoo

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#What is Bloocoo? Bloocoo is a k-mer spectrum-based read error corrector, designed to correct large datasets with a very low memory footprint. It uses the disk streaming k-mer counting algorithm contained in the GATB library, and inserts solid k-mers in a bloom-filter. The correction procedure is similar to the Musket multistage approach. Bloocoo yields similar results while requiring far less memory: as an example, it can correct whole human genome re-sequencing reads at 70 x coverage with less than 4GB of memory.

G. Benoit, D. Lavenier, C. Lemaitre, G. Rizk. (2015) Bloocoo, a memory efficient read corrector. Inria-HAL.

Getting the latest source code

Requirements

CMake 2.6+; see http://www.cmake.org/cmake/resources/software.html

c++ compiler; compilation was tested with gcc and g++ version>=4.5 (Linux) and clang version>=4.1 (Mac OSX).

Instructions

# get a local copy of source code
git clone --recursive https://github.com/GATB/bloocoo.git

# compile the code an run a simple test on your computer
cd bloocoo
sh INSTALL

User manual

Description

Bloocoo is a kmer-spectrum based read error corrector. In a first pass, all k-mers are counted, then k-mers more abundant than a given threshold are kept, i.e. “solid k-mers”.

Correction is then performed by scanning k-mers of a read. For example, a single isolated error generates a gap of k non solid k-mers making the detection of its exact location easy. Correction is made by trying the three different possible nucleotides at the error site, and checking if corresponding k-mers are in the set of solid k-mers.

When several close errors occurs, the pattern is more complex, errors are corrected via a vote algorithm similar to the one in the Musket software (http://musket.sourceforge.net/).

What makes Bloocoo different is the k-mer counting stage and the way solid k-mers are stored in memory. k-mer counting is conducted via the DSK algorithm included in the GATB library, which requires constant-memory. Solid k-mers are stored in a Bloom filter which is fast and memory-efficient : we use only 11 bits of memory per solid k-mers. Therefore, correction of a whole human genome sequencing read set needs only 4GB of memory.

Usage

A typical command line is:

Bloocoo -file reads.fasta -kmer-size 27  -abundance 4

There is 1 mandatory argument:

-file : the read file name, can be fasta, fastq, gzipped or not.

Two important arguments:

-kmer-size : the k-mer size (typically ~31)
-abundance-min : the minimal abundance threshold defining solid k-mers (typically  between 3 and 6, but depends on the read depth, you can also use 'auto' and it is automatically inferred from the data)

Additional useful options :

-nb-cores  : number of threads used
-high-recall  :  correct more errors but can also introduce more mistakes
-slow : slower modes with more pass, but better correction
-high-precision :  correct safely, correct less errors but introduce less mistakes
-ion : (experimental) mode for correcting indels present in  ion torrent reads

Examples

./Bloocoo -file reads.fasta
-> generates the file reads_corrected.fasta

Note : In order to use k values larger than 31, recompilation is necessary (for the moment, this will be improved in next versions).

In the sequence of commands given in the INSTALL file, change the command:

cmake ..

by

cmake -DKSIZE_LIST="64" ..

this will allow to use k<63

For larger k, change the value such that it is a multiple of 32

#Contact

To contact a developer, request help, etc: https://gatb.inria.fr/contact/