Copyright Bjørn Bredesen, 2011-2021
MOCCA (Motif Occurrence Combinatorics Classification Algorithms) is a suite for modelling DNA cis-regulatory element (CRE) sequences. With MOCCA, we include the first polished, efficient and configurable implementation of the Support Vector Machine Motif Occurrence Combinatorics Classification Algorithm (SVM-MOCCA), a method that we previously presented and found to improve generalization to Polycomb/Trithorax Response Elements (PREs) (Bredesen et al. 2019), a class of cis-regulatory elements (CREs) that maintains epigenetic memory. SVM-MOCCA is a hierarchical method based on Support Vector Machines (SVMs) and motifs, where one SVM is trained per motif to classify its occurrences, with the feature space consisting of local dinucleotide and motif occurrence frequencies. Positively classified motif occurrences are subsequently combined using a log-odds model for a final prediction score. SVM-MOCCA distinguishes itself from classical use of SVMs with motifs for the modelling of CRE sequences, where SVMs are trained with motif occurrence frequencies or k-spectra, whereas the MOCCA methods train one model per motif and combine predictions. MOCCA also includes a derivative method based on Random Forests called the Random Forest Motif Occurrence Combinatorics Classification Algorithm (RF-MOCCA). In addition, MOCCA implements support for training log-odds models and classical SVM and RF models using a variety of feature space formulations. MOCCA includes functionality for the generation of negative data, threshold calibration and genome-wide prediction, and also an automated mode that requires only that the user specifies positive sequences, motifs and a genome.
- Bredesen et al. 2021: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04143-2
- Bredesen et al. 2019: https://academic.oup.com/nar/article/47/15/7781/5538007
- Ringrose et al. 2003: https://www.sciencedirect.com/science/article/pii/S153458070300337X
On Debian-based systems, the easiest way to install MOCCA is using apt-get. Ubuntu builds of MOCCA are available via a PPA on launchpad. Run
sudo add-apt-repository ppa:bjornbredesen/mocca && sudo apt-get update && sudo apt-get install mocca
To build, run
cmake . && make.
MOCCA can optionally link with Shogun, for support for additional supervised learning methods. In order to link with Shogun (version 18), run
cmake -USE_SHOGUN=ON . && make.
After building, MOCCA can be installed by running
sudo make install.
To build a Debian-package, run
debuild -b.
... or
gbp buildpackage.
This requires the installation of git-buildpackage. For the first build, adding the --git-force-create is necessary.
Example:
mocca -auto:FASTA KahnPcG.fa -genome:FASTA dmel-all-chromosome-r5.57.fasta -motif:XML motifs2019.xml -predict:GFF predictions.gff -predict:Wig predictions.wig
See the tutorial/ folder and mocca --help for more information.
- Models
- Dummy PREdictor
- CPREdictor
- SVM-MOCCA (the Support Vector Machine Motif Occurrence Combinatorics Classification Algorithm)
- RF-MOCCA (the Random Forest Motif Occurrence Combinatorics Classification Algorithm)
- Log-odds models with motif-based feature spaces
- Support Vector Machines with motif-based feature spaces
- Random Forests with motif-based feature spaces
- Motif handling
- Command-line specification of IUPAC motifs
- Loading of IUPAC motifs from XML
- Generation of random IUPAC motifs
- Full k-mer sets
- IUPAC motif occurrence parsing Finite State Machine
- Position Weight Matrix motifs
- Feature spaces
- Motif occurrence frequency spectrum
- Motif pair occurrence frequency spectrum, with distance cutoff, and multiple distancing and overlap modes
- Motif distancing kernels
- Periodic motif occurrence kernels
- Motif pairing kernels that incorporate positional information
- Core usage features
- Training with FASTA sequence files
- Validation with FASTA sequence files
- Prediction threshold calibration for a desired precision
- Genome-wide prediction of candidate CREs to General Feature Format files
- Genome-wide prediction to Wiggle files
- Saving of sequence scores to table
- Scoring of sequence files to Wiggle curves
- Automatic construction of negative training/test/calibration data
If you use MOCCA in published research, please cite: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04143-2. If you use SVM-MOCCA, for the method, please additionally cite: https://academic.oup.com/nar/article/47/15/7781/5538007.
MIT License
Copyright (c) 2021 Bjørn André Bredesen
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Copyright (c) 2000-2013 Chih-Chung Chang and Chih-Jen Lin All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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Neither name of copyright holders nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
MIT License
Copyright (c) [2014-2018] [Marvin N. Wright]
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Copyright (c) 2006, 2007 Marcin Kalicinski
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Copyright (c) Shogun Machine Learning Toolbox developers [email protected] All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
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Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.