Efficient lossless compression of genomic sequences
As a compression tool, JARVIS is able to provide additional compression gains over GeCo [ https://github.com/pratas/geco ] and GeCo2 [ https://github.com/pratas/geco2 ], however it uses slightly higher computational resources (RAM and processing time). JARVIS only affords reference-free compression. The core of the JARVIS method is a competitive prediction between two different classes of models: weighted stochastic repeat models and weighted context models. The latter models can be either context models or substitutional tolerant context models.
Install Miniconda, then run the following:
conda install -y -c bioconda jarvisgit clone https://github.com/pratas/jarvis.git cd jarvis/src/ make
Run JARVIS using level 3:
./JARVIS -v -l 3 File.seq
To see the possible options type
./JARVIS
or
./JARVIS -h
This will print the following options:
SYNOPSIS
./JARVIS [OPTION]... [FILE]
SAMPLE
Run Compression : ./JARVIS -v -l 4 sequence.txt
Run Decompression : ./JARVIS -v -d sequence.txt.jc
DESCRIPTION
Compress and decompress lossless genomic sequences for
storage and analysis purposes.
Measure an upper bound of the sequence entropy.
-h, --help
usage guide (help menu).
-V, --version
Display program and version information.
-F, --force
force mode. Overwrites old files.
-v, --verbose
verbose mode (more information).
-s, --show-levels
show pre-computed compression levels (configured).
-l [NUMBER], --level [NUMBER]
Compression level (integer).
Default level: 1.
It defines compressibility in balance with computational
resources (RAM & time). Use -s for levels perception.
-cm [NB_C]:[NB_D]:[NB_I]:[NB_G]/[NB_S]:[NB_E]:[NB_I]:[NB_A]
Template of a context model.
Parameters:
[NB_C]: (integer [1;20]) order size of the regular context
model. Higher values use more RAM but, usually, are
related to a better compression score.
[NB_D]: (integer [1;5000]) denominator to build alpha, which
is a parameter estimator. Alpha is given by 1/[NB_D].
Higher values are usually used with higher [NB_C],
and related to confiant bets. When [NB_D] is one,
the probabilities assume a Laplacian distribution.
[NB_I]: (integer {0,1}) number to define if a sub-program
which addresses the specific properties of DNA
sequences (Inverted repeats) is used or not. The
number 1 turns ON the sub-program using at the same
time the regular context model. The number 0 does
not contemple its use (Inverted repeats OFF). The
use of this sub-program increases the necessary time
to compress but it does not affect the RAM.
[NB_G]: (real [0;1)) real number to define gamma. This value
represents the decayment forgetting factor of the
regular context model in definition.
[NB_S]: (integer [0;20]) maximum number of editions allowed
to use a substitutional tolerant model with the same
memory model of the regular context model with
order size equal to [NB_C]. The value 0 stands for
turning the tolerant context model off. When the
model is on, it pauses when the number of editions
is higher that [NB_C], while it is turned on when
a complete match of size [NB_C] is seen again. This
is probabilistic-algorithmic model very usefull to
handle the high substitutional nature of genomic
sequences. When [NB_S] > 0, the compressor used more
processing time, but uses the same RAM and, usually,
achieves a substantial higher compression ratio. The
impact of this model is usually only noticed for
[NB_C] >= 14.
[NB_R]: (integer {0,1}) number to define if a sub-program
which addresses the specific properties of DNA
sequences (Inverted repeats) is used or not. It is
similar to the [NR_I] but for tolerant models.
[NB_E]: (integer [1;5000]) denominator to build alpha for
substitutional tolerant context model. It is
analogous to [NB_D], however to be only used in the
probabilistic model for computing the statistics of
the substitutional tolerant context model.
[NB_A]: (real [0;1)) real number to define gamma. This value
represents the decayment forgetting factor of the
substitutional tolerant context model in definition.
Its definition and use is analogus to [NB_G].
... (you may use several context models)
-rm [NB_R]:[NB_C]:[NB_A]:[NB_B]:[NB_L]:[NB_G]:[NB_I]
Template of a repeat model.
Parameters:
[NB_R]: (integer [1;10000] maximum number of repeat models
for the class. On very repetive sequences the RAM
increases along with this value, however it also
improves the compression capability.
[NB_C]: (integer [1;20]) order size of the repeat context
model. Higher values use more RAM but, usually, are
related to a better compression score.
[NB_A]: (real (0;1]) alpha is a real value, which is a
parameter estimator. Higher values are usually used
in lower [NB_C]. When [NB_A] is one, the
probabilities assume a Laplacian distribution.
[NB_B]: (real (0;1]) beta is a real value, which is a
parameter for discarding or maintaining a certain
repeat model.
[NB_L]: (integer (1;20]) a limit threshold to play with
[NB_B]. It accepts or not a certain repeat model.
[NB_G]: (real [0;1)) real number to define gamma. This value
represents the decayment forgetting factor of the
regular context model in definition.
[NB_I]: (integer {0,1}) number to define if a sub-program
which addresses the specific properties of DNA
sequences (Inverted repeats) is used or not. The
number 1 turns ON the sub-program using at the same
time the regular context model. The number 0 does
not contemple its use (Inverted repeats OFF). The
use of this sub-program increases the necessary time
to compress but it does not affect the RAM.
-z [NUMBER], --selection [NUMBER]
Size of the context selection model (integer).
Default context selection: 12.
[FILE]
Input sequence filename (to compress) -- MANDATORY.
File to compress is the last argument.
If you are not interested in setting the template for each model, then use the levels mode. To see the possible levels type:
./JARVIS -s
On using this software/method please cite:
Pratas D, Hosseini M, Silva JM, Pinho AJ. A Reference-Free Lossless Compression Algorithm for DNA Sequences Using a Competitive Prediction of Two Classes of Weighted Models. Entropy. 2019 Nov;21(11):1074.
For any issue let us know at issues link.
GPL v3.
For more information:
http://www.gnu.org/licenses/gpl-3.0.html