MONI

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                                            ver 0.2.0

A Pangenomics Index for Finding MEMs.

MONI index uses the prefix-free parsing of the text [2][3] to build the Burrows-Wheeler Transform (BWT) of the reference genomes, the suffix array (SA) samples at the beginning and at the end of each run of the BWT, and the threshold positions of [1].

Construction of the index:

usage: moni build [-h] -r REFERENCE [-w WSIZE] [-p MOD] [-t THREADS] [-k] [-v]
                  [-f] [--moni-ms] [--spumoni]
  -h, --help            show this help message and exit
  -r REFERENCE, --reference REFERENCE
                        reference file name (default: None)
  -o OUTPUT, --output OUTPUT
                        output directory path (default: same as reference)
  -w WSIZE, --wsize WSIZE
                        sliding window size (default: 10)
  -p MOD, --mod MOD     hash modulus (default: 100)
  -t THREADS, --threads THREADS
                        number of helper threads (default: 0)
  -k                    keep temporary files (default: False)
  -v                    verbose (default: False)
  -f                    read fasta (default: False)
  -g GRAMMAR, --grammar GRAMMAR
                        select the grammar [plain, shaped] (default: plain)

Computing the matching statistics with MONI:

usage: moni ms [-h] -i INDEX -p PATTERN [-o OUTPUT] [-t THREADS]
  -h, --help            show this help message and exit
  -i INDEX, --index INDEX
                        reference index base name (default: None)
  -p PATTERN, --pattern PATTERN
                        the input query (default: None)
  -o OUTPUT, --output OUTPUT
                        output directory path (default: .)
  -t THREADS, --threads THREADS
                        number of helper threads (default: 1)
  -g GRAMMAR, --grammar GRAMMAR
                        select the grammar [plain, shaped] (default: plain)

Computing the matching statistics with MONI:

usage: moni mems [-h] -i INDEX -p PATTERN [-o OUTPUT] [-t THREADS]
  -h, --help            show this help message and exit
  -i INDEX, --index INDEX
                        reference index base name (default: None)
  -p PATTERN, --pattern PATTERN
                        the input query (default: None)
  -o OUTPUT, --output OUTPUT
                        output directory path (default: .)
  -t THREADS, --threads THREADS
                        number of helper threads (default: 1)
  -g GRAMMAR, --grammar GRAMMAR
                        select the grammar [plain, shaped] (default: plain)

Computing the MEM extension with MONI and ksw2:

usage: moni extend [-h] -i INDEX -p PATTERN [-o OUTPUT] [-t THREADS] [-b BATCH] [-g GRAMMAR] [-L EXTL] [-A SMATCH] [-B SMISMATCH] [-O GAPO] [-E GAPE]

optional arguments:
  -h, --help            show this help message and exit
  -i INDEX, --index INDEX
                        reference index folder (default: None)
  -p PATTERN, --pattern PATTERN
                        the input query (default: None)
  -o OUTPUT, --output OUTPUT
                        output directory path (default: .)
  -t THREADS, --threads THREADS
                        number of helper threads (default: 1)
  -b BATCH, --batch BATCH
                        number of reads per thread batch (default: 100)
  -g GRAMMAR, --grammar GRAMMAR
                        select the grammar [plain, shaped] (default: plain)
  -L EXTL, --extl EXTL  length of reference substring for extension (default: 100)
  -A SMATCH, --smatch SMATCH
                        match score value (default: 2)
  -B SMISMATCH, --smismatch SMISMATCH
                        mismatch penalty value (default: 4)
  -O GAPO, --gapo GAPO  coma separated gap open penalty values (default: 4,13)
  -E GAPE, --gape GAPE  coma separated gap extension penalty values (default: 2,1)

Example

Install prerequisite packages

apt-get update
apt-get install -y build-essential cmake git python3 zlib1g-dev

Download

git clone https://github.com/maxrossi91/moni

Compile

mkdir build
cd build
cmake ..
make

Install

make install

This command will install the binaries to the default install location (e.g., /usr/local/bin for Ubuntu users). If the user wants the binary in some other custom location, this can be done using cmake -DCMAKE_INSTALL_PERFIX=<dest> .. instead of cmake .. in the compile sequence of commands, where <dest> is the preferred destination directory.

Run

Build the index for SARS-CoV2.1k.fa.gz in the data/SARS-CoV2 folder

moni build -r data/SARS-CoV2/SARS-CoV2.1k.fa.gz -o sars-cov2 -f

It produces three files sars-cov2.plain.slp, sars-cov2.thrbv.ms, and sars-cov2.idx in the current folder which contain the grammar, the rlbwt and the thresholds, and the starting position and name of each fasta sequence in the reference file respectively.

Compute the matching statistics of reads.fastq.gz against SARS-CoV2.1k.fa.gz in the data/SARS-CoV2 folder

moni ms -i sars-cov2 -p data/SARS-CoV2/reads.fastq.gz -o reads

It produces two output files reads.lengths and reads.pointers in the current folder which store the lengths and the positions of the matching statistics of the reads against the reference in a fasta-like format.

Compute the MEMs of reads.fastq.gz against SARS-CoV2.1k.fa.gz in the data/SARS-CoV2 folder

moni mems -i sars-cov2 -p data/SARS-CoV2/reads.fastq.gz -o reads

It produces one output file reads.mems in the current folder which store the MEMs reposted as pairs of position and lengths in a fasta-like format.

Compute the MEM extension of reads.fastq.gz against SARS-CoV2.1k.fa.gz in the data/SARS-CoV2 folder

moni extend -i sars-cov2 -p data/SARS-CoV2/reads.fastq.gz -o reads

It produces one output file reads.sam in the current folder which stores the information of the MEM extensions in SAM format.

External resources

• Big-BWT
  • gSACA-K
  • malloc_count
• sdsl-lite
  • Divsufsort
• klib
• ksw2
• r-index
• pfp-thresholds
• bigrepair
• shaped_slp

Citation

Please, if you use this tool in an academic setting cite the following papers:

@article{RossiOLGB21,
author      = { Massimiliano Rossi and 
                Marco Oliva and
                Ben Langmead and
                Travis Gagie and
                Christina Boucher},
title       = {MONI: A Pangenomics Index for Finding Maximal Exact Matches},
booktitle   = {Research in Computational Molecular Biology - 25th Annual 
                International Conference, {RECOMB} 2021, Padova, Italy},
journal     = {Journal of Computational Biology},
volume      = {29},
number      = {2},
pages       = {169--187},
year        = {2022},
publisher   = {Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…}
}

Authors

Theoretical results:

• Christina Boucher
• Travis Gagie
• Ben Langmead
• Massimiliano Rossi

Implementation:

• Massimiliano Rossi

Experiments

• Marco Oliva
• Massimiliano Rossi

Why "MONI"?

Moni is the Finnish word for multi.

References

[1] Hideo Bannai, Travis Gagie, and Tomohiro I, "Refining ther-index", Theoretical Computer Science, 812 (2020), pp. 96–108

[2] Christina Boucher, Travis Gagie, Alan Kuhnle and Giovanni Manzini, "Prefix-Free Parsing for Building Big BWTs", In Proc. of the 18th International Workshop on Algorithms in Bioinformatics (WABI 2018).

[3] Christina Boucher, Travis Gagie, Alan Kuhnle, Ben Langmead, Giovanni Manzini, and Taher Mun. "Prefix-free parsing for building big BWTs.", Algorithms for Molecular Biology 14, no. 1 (2019): 13.