Package: gnocis
GnocisBjørn André Bredesen-Aa, 2018-StabilityAboutLarge parts of complex genomes of multi-cellular organisms are non-coding.Cis-regulatory elements (CREs) are non-coding sequences that establish or modify gene transcription by multiple mechanisms. Multiple classes of CREs have been identified, including promoters, enhancers, silencers, insulators and Polycomb/Trithorax Response Elements. CREs can be identified experimentally or by means ofin silicoprediction. Experimental identification of CREs can depend on the cells that are used. Genome-widein silicoprediction, on the other hand, can potentially comprehensively predict CREs in a genome. In order to use machine learning for CRE prediction, a variety of functionality is required. A variety of packages exist for Python 3 for machine learning and sequence analysis, but successfully combining them requires the implementation of interfacing between them. Ensuring that the solution is efficient is important for large genomes, but can be challenging for end-users.Gnocis is a system in Python 3 for the interactive and reproducible analysis and modelling of CRE DNA sequences. A broad suite of tools is implemented for data preparation, feature set definition, model formulation, training, cross-validation and genome-wide prediction. Gnocis employs Cython and a variety of techniques in order to optimally implement the glue necessary in order to apply machine learning for CRE analysis and prediction.InstallingThe recommended way to install Gnocis is through the PyPI package manager. In order to install via the PyPI package manager, open a terminal and execute:pip install gnocisAlternatively, Gnocis can be built from source. To build a wheel and install it, run:make wheel
pip install dist/*.whlFinally, Gnocis can be used by building from source and including the entiregnocisdirectory in the source tree. In order to do so, runmake allInstalling dependencies for tutorialsudo apt-get install python3-sphinx
sudo pip3 install notebook pandas numpy matplotlib cupy sklearn tensorflowDocumentationFor the complete manual, see:https://bjornbredesen.github.io/gnocis/For an in-depth tutorial, see the Jupyter Notebooks in thetutorial/folder.FeaturesDNA sequence handlingFile format support - Loading and streamingFASTA2bitFile format support - SavingFASTAOperationsPrintingSliding window extractionReverse complement generationSequence region handlingFile format support - Loading and savingGFFBEDCoordinate lists (chromosome:start..end)OperationsOverlap acquisitionNon-overlap acquisitionMerged set generationExclusion set generationSequence region extractionModellingGenerative DNA sequence models, with training and sequence generationI.i.d.N'th order Markov chainsConfusion matricesGeneration from model statisticsPrintingReceiver Operating Characteristic curve generationPrecision Recall Curve generationArea Under the Curve calculationFeature modelsLog-oddsDummySupport Vector Machines (via sklearn)Random Forest (via sklearn)Featuresk-mer spectrumMotif occurrence spectrumMotif pair occurrence spectrumMotifsTypesIUPAC nucleotide motifsPosition Weight Matricesk-mer spectraFeature networksDirected acyclic graphs of featuresTransformations of feature sets: filtering; concatenation; scaling; square; ...Feature network nodes for constructing modelsApplication to sequencesOptionally integrates with established packagesNumpy – for integration with external methodsPandas – for integration with external methodsScikit-learn – for extended analyses and classic machine learningTensorFlow – for neural networksJupyter Notebooks – for interactive and reproducible analysis and modellingEasy to useObjects are represented by classes, with human-readable descriptionsOptimized with Cython...RequirementsPython 3.6, 3.7, 3.8, 3.9Windows, MacOS or LinuxC++ compiler when installing on LinuxOptional: Cython – required only when building from sourceOptional: sklearn – for SVM and RF modellingOptional: CuPy and CUDA – for CUDA-optimized SVMOptional: TensorFlow – for neural networksCitingIf you use Gnocis in published research, Gnocis must be cited. An article for Gnocis is in the process of being submitted for peer review. Please check back for an updated citation policy.LicenseMIT LicenseCopyright (c) 2018- Bjørn André Bredesen-AaPermission 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
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.LogoLogo: Copyright Bjørn André Bredesen-Aa