This software is an implementation of the eGap algorithm described in External memory BWT and LCP computation for sequence collections with applications by L. Egidi, F. A. Louza, G. Manzini, and G. P. Telles, Algorithms for Molecular Biology (2019).
Copyright 2017-2019 by the authors.
- A relatively recent version of gcc
- Python 3.X
git clone https://github.com/felipelouza/egap.git
cd egap
make
./eGap --lcp -m 4096 dataset/reads.fastq
This will produce the file dataset/reads.fastq.bwt
and dataset/reads.fastq.2.lcp
containing the BWT and LCP array (the latter using 2 bytes per entry). The computation will use 4GB (4096 MB) of RAM
Tool to build the BWT and optionally the LCP, DA and SA array for a collection of sequences in external memory. There are two different usages depending on whether you already have the BWT of the input files:
-
If you do have the BWTs use option -b: you must specify the file names on the command line and use the option -o to specify an output basename. For example:
eGap -b --lcp -o merge -m 4096 file1.bwt file2.bwt
will produce the output files: merge.bwt, merge.2.lcp, merge.da. Globbing is accepted: multiple file names can be denoted for example as file?.bwt -
If you don't have the BWTs then your input must consists of a single file with extension
.fasta/.fa
(one input document per sequence).fastq
(one input document per sequence).txt
(one input document per line) and it is not mandatory to specify the output basename. For example:eGap --lcp -m 4096 file.fasta
will produce the output files: file.fasta.bwt, files.fasta.2.lcp
All input and output files are uncompressed. The value 0 is used as the eof symbol in the output BWT.
-m, --mem
specify memory assigned to the algorithm in MB. This is a mandatory option. Note: do not assign all the available RAM to the algorithm: leave at least 5% to the operating system.
-o, --out
specify basename for output and temporary files
-b, --bwt
inputs are bwt files (requires -o)
-l, --lcp
compute LCP Array
--rev
compute data structures for the reversed string
--lbytes
number of bytes for each LCP entry (def. 2)
-v
verbose output in the log file
-h, --help
show usage
Use the options:
-d, --da
compute Document Array
-s, --sa
compute Suffix Array
--dbytes
number of bytes for each DA entry (def. 4)
--sbytes
number of bytes for each SA entry (def. 4)
In the case you want to merge BWT files and later compute the Document Array, you must provide DA and file.docs
with the following option:
--da --docs
compute Document Array and output the number of documents into file.docs
(required to use --bwt --da)
Example
./eGap -m 4096 dataset/file1.fastq -o file1 --da --docs
./eGap -m 4096 dataset/file2.fastq -o file2 --da --docs
./eGap -m 4096 --bwt -o merge file1.bwt file2.bwt --da
The running time of eGap can be decreased if, instead of the true LCP values, the user settles for computing the LCP values up to a certain threshold k. Using the option --trlcp k, as an altenative to --lcp, the algorithm computes an LCP array in which all values greater than k are replaced by the value k.
--trlcp compute LCP values only up to TRLCP (truncated LCP)
Another option offered by eGap, alternative to (truncated) LCP, is to compute the info required for the construction of the succinct (BOSS) representation of the de Bruijn graph associated to the input sequences. Using the option --deB k eGap compute two bitfiles with extension .lcpbit0 and .lcpbit1 which, together with the BWT, can be used to compute the BOSS representation of the de Bruijn graph as described in the Application section of the AMB paper.
--deB
compute info for order-DEB deBruijn graph
We have compared eGap with the available BWT/LCP construction tools on the following collections
Name | SizeGBs | Num Docs | Max DocLen | Ave DocLen | Max LCP | Ave LCP | Download Link |
---|---|---|---|---|---|---|---|
Shortreads | 8.0 | 85,899,345 | 100 | 100 | 99 | 27.90 | .tar.gz |
Longreads | 8.0 | 28,633,115 | 300 | 300 | 299 | 90.28 | .tar.gz |
Pacbio.1000 | 8.0 | 8,589,934 | 1,000 | 1,000 | 876 | 18.05 | .tar.gz |
Pacbio | 8.0 | 942,248 | 71,561 | 9,116 | 3,084 | 18.32 | .tar.gz |
We have also used versions of the above collections shortened to 1GB. The shortened versions can be obtained by the above files using simple command line instructions. Check all md5sums after dowloading and extraction.
The results of our experiments are reported on the above AMB paper.
Thanks to Pierre Peterlongo by helpful suggestions and debugging.