K2R is a tool capable of indexing a set of read data (FASTA) by associating each k-mer with the reads in which they appear.
git clone --recurse-submodules https://github.com/LeaVandamme/K2R.git
cd K2R/ ;
make -j
K2R can index a set of reads from a FASTA file. Several options and parameters are available to adapt the creation of the index to your needs :
./k2r_index -r read_file.fasta -b path_to_binary/binary_prefix -t nb_threads [OPTIONS]
Arguments :
-r : Build index from file (FASTA allowed)
-b : Write index in binary files (default : binary_index)
-t : Number of threads used (default: 1)
Options :
-k : k-mer size (default : 31)
-m : Minimizer size (default : 15)
-s : Counting bloom filter size (log(2), default 32)
-h : Homocompression of reads
--min-ab : Minimizers minimum abundance (default: 2)
--max-ab : Minimizers maximum abundance (default: 1000)
--keep-all : Keep all minimizers (minimum abundance = 1, no maximum ; default : false)
K2R can launch several queries from a file of file (fof), containing paths to fasta files.
./k2r_query -f /path_to_fof/fof.txt -o output_prefix -b path_to_index/index_prefix [OPTIONS]
But can also launch a single request from a FASTA file :
./k2r_query -s /path_to_fof/seq.fasta -o output_prefix -b path_to_index/index_prefix [OPTIONS]
Several options and parameters are available to adapt the queries :
Arguments :
-s OR -f : Sequence file (FASTA) if a unique sequence is queried (-s), file of file if several sequences are queried (-f)
-o : Write output reads in fasta file (default : query_output)
-b : Index binary files prefix (default: binary_index)
Option :
-r : Rate of minimizer found in the read to keep it in results (between 0 and 1, default : 0.4)
K2R creates 2 binary files for each index :
- [binary_prefix]_mmer.bin, which contains the association between each minimizer and its color identifier.
- [binary_prefix]_color.bin, which contains the association between each color identifier and its color.
K2R creates 1 file for each query.
For example if the queries are launched on a file of file containing 100 paths, 100 files will be created containing the reads in which the sequences appear.
Here is an example of how to use K2R, using the data provided in the example folder. The dataset contains reads (HiFi) from the E.Coli genome, with a 10X coverage.
We choose to reduce the filter size to 2^26, as the dataset is sufficiently small.
./k2r_index -r example/reads/reads.fasta -b example/output/output_binary -s 26
Once the index has been created, the sequences can be queried using the following command :
./k2r_query -f example/sequences/fof.txt -o example/output/query_output -b example/output/output_binary -r 0.2
To find out more about the structure, a preprint is available here :
Lea Vandamme, Bastien Cazaux, Antoine Limasset : Tinted de Bruijn Graphs for efficient read extraction from sequencing datasets (https://doi.org/10.1101/2024.02.15.580442)
Contact : lea.vandamme@univ-lille.fr