Constructing a BIGSI

1. Extract k-mers from your data

You can just any tool you want to extract unique k-mers from your raw data. We recommend mccortex as you can use it's error cleaning methods to extract error cleaned k-mers. However, you can also use a k-mer counter software like Jellyfish or a custom script.

mccortex/bin/mccortex31 build -k 31 -s test1 -1 example-data/kmers.txt example-data/test1.ctx
mccortex/bin/mccortex31 build -k 31 -s test2 -1 example-data/kmers.txt example-data/test2.ctx

2. Initialise the BISI

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Choosing BIGSI parameters

See https://bigsi.readme.io/v1.0/docs/choosing-bigsi-parameters to decide on parameters k, m and h.

bigsi init test-bigsi --k 31 --m 1000 --h 1

3. Construct the bloom filters

bigsi bloom --db test-bigsi -c example-data/test1.ctx example-data/test1.bloom
bigsi bloom --db test-bigsi -c example-data/test1.ctx example-data/test2.bloom

4. Insert the bloom filters into the index

bigsi build test-bigsi example-data/test1.bloom example-data/test2.bloom -s s1 -s s2

5. Query the index

bigsi search -o tsv --db test-bigsi -s CGGCGAGGAAGCGTTAAATCTCTTTCTGACG

If you've installed with docker

PWD=`pwd`
docker run -v $PWD/example-data:/data phelimb/bigsi mccortex/bin/mccortex31 build -k 31 -s test1 -1 /data/kmers.txt /data/test1.ctx
docker run -v $PWD/example-data:/data phelimb/bigsi mccortex/bin/mccortex31 build -k 31 -s test2 -1 /data/kmers.txt /data/test2.ctx

docker run -v $PWD/example-data:/data phelimb/bigsi bigsi  init /data/test.bigsi --k 31 --m 1000 --h 1

docker run -v $PWD/example-data:/data phelimb/bigsi bigsi bloom --db /data/test.bigsi -c /data/test1.ctx /data/test1.bloom    
docker run -v $PWD/example-data:/data phelimb/bigsi bigsi bloom --db /data/test.bigsi -c /data/test1.ctx /data/test2.bloom

docker run -v $PWD/example-data:/data phelimb/bigsi bigsi build /data/test.bigsi /data/test1.bloom /data/test2.bloom -s s1 -s s2
docker run -v $PWD/example-data:/data phelimb/bigsi bigsi search -o tsv --db /data/test.bigsi -s CGGCGAGGAAGCGTTAAATCTCTTTCTGACG