An FM-index of 400k SARS-CoV-2 genomes
Leonardo Martins tweeted that xz can compress a 1.4 million SARS-CoV-2 genomes in a 39GB FASTA down to 74MB. That is a very impressive compression ratio! This reminds me of my earlier work on FM-index construction.
For an experiment, I downloaded ~400k SARS-CoV-2 genomes from EBI’s COVID-19 data portal (GISAID has ~1.5M genomes but imposes restrictions) and generated an FM-index of these sequences in both strands with ropebwt2
ropebwt2 -do sars-cov-2.fmd sequences_fasta_2021-05-15.fa.gz
The command line took ~30 minutes. The output file
sars-cov-2.fmd is 33MB in
size. It keeps the BWT and the necessary information for backward/forward
search. You can find this file at Zenodo.
Here are a few things you can do with this file, using fermi2:
# uncompress the FM-index; forward and reverse strands are interleaved fermi2 unpack sars-cov-2.fmd | less -S # count 61-mers occurring 10 times or more using 4 threads (5 sec on my laptop) fermi2 count -k61 -o10 -t4 sars-cov-2.fmd | less # count how many times a sequence in FASTA occurs in the FM-index fermi2 match sars-cov-2.fmd query.fa # get the count of every 61-mer in query sequences fermi2 kprof -k61 sars-cov-2.fmd query.fa # find supermaximal exact matches (SMEMs) fermi2 match -p sars-cov-2.fmd query.fa # find SMEMs at least 200bp occurring 1000 times or more, using 8 threads fermi2 match -pt8 -l200 -n1000 sars-cov-2.fmd # generate sampled suffix array and output positions fermi2 sa -t8 sars-cov-2.fmd > sars-cov-2.fmd.sa # slooow fermi2 match -pm1000 -s sars-cov-2.fmd.sa sars-cov-2.fmd query.fa # can be very sloow
Although the original input sequences are totaled 12GB in length (or 24GB if we consider both strands), all but the last operations take ~33MB in RAM, the size of the index. That is the advantage of FM-index or similar indices.
PS: I don’t study SARS-CoV-2 genomes. I did the above for fun only. Let me know if you feel some of these might be useful to your research and want to learn more.
blog comments powered by Disqus