GSOC2011 Mocapy
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* Formulate probabilistic models using Python-Mocapy++. Apply the models to solve biological problems. Examples of problems that can be solved using dynamic Bayesian networks include deciding if a pair of sequences is evolutionarily related, finding sequences which are homologous to a known evolutionary family and predicting RNA secondary structure. | * Formulate probabilistic models using Python-Mocapy++. Apply the models to solve biological problems. Examples of problems that can be solved using dynamic Bayesian networks include deciding if a pair of sequences is evolutionarily related, finding sequences which are homologous to a known evolutionary family and predicting RNA secondary structure. | ||
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== Project Code == | == Project Code == | ||
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− | ==== Testing and | + | ==== Testing and Improving Mocapy Bindings ==== |
Before integrating to Biopython, some unit testing was required, to detect possible errors and make sure future changes that break functionality won't go unnoticed. | Before integrating to Biopython, some unit testing was required, to detect possible errors and make sure future changes that break functionality won't go unnoticed. | ||
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</python> | </python> | ||
− | ==== | + | |
+ | ==== Building and Distributing Mocapy as a Package ==== | ||
+ | |||
+ | [http://docs.python.org/distutils/index.html Distutils] was used to distribute Mocapy's Python modules. | ||
+ | |||
+ | Besides distributing the python code, it was also necessary to build the extension modules. | ||
+ | [http://wiki.python.org/moin/boost.python/BuildingExtensions Building Extensions with boost.python] describes ways to build extensions using distutils. | ||
+ | |||
+ | Mocapy's setup.py can be found at http://mocapy.svn.sourceforge.net/viewvc/mocapy/branches/gSoC11/python/setup.py?revision=418&view=markup | ||
+ | |||
+ | Using the setup script, mocapy installation is done in a few steps: | ||
+ | * Build the mocapy library using cmake (usual procedure described in mocapy docs); | ||
+ | * Issue "python setup.py build", to build the extension modules; | ||
+ | * Issue "python setup.py install", to install the package (normally, the install procedure does the step above in case you didn't). | ||
=== Integration with Biopython === | === Integration with Biopython === | ||
− | ==== | + | ==== API Design ==== |
− | |||
In order to use Mocapy in conjunction with Biopython, a new module for PDB-specific features was added to Bio.PDB. This is where the API is being designed. | In order to use Mocapy in conjunction with Biopython, a new module for PDB-specific features was added to Bio.PDB. This is where the API is being designed. | ||
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* automatically decide on the best model using the BIC criterion. | * automatically decide on the best model using the BIC criterion. | ||
− | |||
− | + | ==== Barnacle ==== | |
+ | |||
+ | Frellsen J, Moltke I, Thiim M, Mardia KV, Ferkinghoff-Borg J, et al. 2009 [http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000406 A Probabilistic Model of RNA Conformational Space]. PLoS Comput Biol 5(6): e1000406. doi:10.1371/journal.pcbi.1000406. | ||
+ | |||
+ | RNA 3-D structure prediction methods require an accurate energy function and a conformational sampling procedure. Barnacle focuses on the problem of conformational sampling. | ||
+ | |||
+ | The aim of BARNACLE (BAyesian network model of RNA using Circular distributions and maximum Likelihood Estimation) is to capture both the marginal distributions of each of the angles and the local dependencies between them. Barnacle describes RNA structure in a natural continuous space. It can be used purely as a proposal distribution, but also as an energy term enforcing realistic local conformations. The model combines a dynamic Bayesian network (DBN) with directional statistics. | ||
+ | |||
+ | [[File:Journal.pcbi.1000406.g002.png|600px|thumb|center|Barnacle DBN (doi:10.1371/journal.pcbi.1000406.g002)]] | ||
+ | |||
+ | The DBN represents nine consecutive dihedral angles, where the seven central angles originate from a single nucleotide. Each slice j (a column of three variables) corresponds to one dihedral angle in an RNA fragment. The variables in each slice are: an angle identifier, Dj, a hidden variable, Hj, and an angular variable, Aj. The angle identifier keeps track of which dihedral angle is represented by a slice, while the angular node models the actual dihedral angle value. The hidden nodes induce dependencies between all angles along the sequence (and not just between angles in consecutive slices). | ||
+ | |||
+ | |||
+ | The original source code for Barnacle, which contains an embedded version of Mocapy written in Python, can be found at http://sourceforge.net/projects/barnacle-rna. | ||
+ | |||
+ | The modified version of Barnacle, changed to work with the Mocapy bindings can be found at https://github.com/mchelem/biopython/tree/master/Bio/PDB/Barnacle. | ||
+ | |||
+ | Here is an example of use: | ||
+ | <python> | ||
+ | model = Barnacle('ACCU') | ||
+ | model.sample() | ||
+ | print 'log likelihood = ', model.get_log_likelihood() | ||
+ | model.save_structure('structure01.pdb') | ||
+ | </python> | ||
+ | |||
+ | |||
+ | ==== TorusDBN ==== | ||
Wouter Boomsma, Kanti V. Mardia, Charles C. Taylor, Jesper Ferkinghoff-Borg, Anders Krogh, and Thomas Hamelryck. A generative, probabilistic model of local protein structure. Proc Natl Acad Sci U S A. 2008 July 1; 105(26): 8932–8937. | Wouter Boomsma, Kanti V. Mardia, Charles C. Taylor, Jesper Ferkinghoff-Borg, Anders Krogh, and Thomas Hamelryck. A generative, probabilistic model of local protein structure. Proc Natl Acad Sci U S A. 2008 July 1; 105(26): 8932–8937. | ||
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2440424/ | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2440424/ | ||
− | + | TorusDBN aims at predicting the 3D structure of a biomolecule given its amino-acid sequence. It is a continuous probabilistic model of the local sequence–structure preferences of proteins in atomic detail. The backbone of a protein can be represented by a sequence of dihedral angle pairs, φ and ψ that are well known from the [http://en.wikipedia.org/wiki/Ramachandran_plot Ramachandran plot]. Two angles, both with values ranging from −180° to 180°, define a point on the torus. Hence, the backbone structure of a protein can be fully parameterized as a sequence of such points. | |
− | + | ||
− | + | ||
− | + | [[File:Torus_dbn.png|600px|thumb|center|TorusDBN (doi: 10.1073/pnas.0801715105)]] | |
− | + | ||
− | + | ||
− | The TorusDBN model is implemented as part of the backboneDBN package, which is freely available at | + | The circular nodes represent stochastic variables. The rectangular boxes along the arrows illustrate the nature of the conditional probability distribution between them. A hidden node emits angle pairs, amino acid information, secondary structure labels and cis/trans information. |
+ | |||
+ | |||
+ | The TorusDBN model is originally implemented as part of the backboneDBN package, which is freely available at | ||
http://sourceforge.net/projects/phaistos/. | http://sourceforge.net/projects/phaistos/. | ||
+ | |||
+ | |||
+ | A new version of the TorusDBN model was implemented in the context of this project and can be found at | ||
+ | https://github.com/mchelem/biopython/tree/master/Bio/PDB/TorusDBN. | ||
+ | |||
+ | The TorusDBNTrainer can be used to train a model with a given training set: | ||
+ | <python> | ||
+ | trainer = TorusDBNTrainer() | ||
+ | trainer.train(training_set) # training_set is a list of files | ||
+ | model = trainer.get_model() | ||
+ | </python> | ||
+ | |||
+ | Then the model can be used to sample new sequences: | ||
+ | <python> | ||
+ | model.set_aa('ACDEFGHIK') | ||
+ | model.sample() | ||
+ | print model.get_angles() # The sampled angles. | ||
+ | </python> | ||
+ | |||
+ | When creating a model, it is possible to create a new DBN specifying the size of the hidden node or loading the DBN from a file. | ||
+ | <python> | ||
+ | model = TorusDBNModel() | ||
+ | model.create_dbn(hidden_node_size=10) | ||
+ | model.save_dbn('test.dbn') | ||
+ | </python> | ||
+ | |||
+ | <python> | ||
+ | model = TorusDBNModel() | ||
+ | model.load_dbn('test.dbn') | ||
+ | model.set_aa('ACDEFGHIK') | ||
+ | model.sample() | ||
+ | print model.get_angles() # The sampled angles. | ||
+ | </python> | ||
+ | |||
+ | It is also possible to choose the best size for the hidden node using the find_optimal_model method: | ||
+ | <python> | ||
+ | trainer = TorusDBNTrainer() | ||
+ | hidden_node_size, IC = trainer.find_optimal_model(training_set) | ||
+ | model = trainer.get_model() | ||
+ | </python> | ||
+ | |||
+ | IC is either the [http://en.wikipedia.org/wiki/Bayesian_information_criterion Bayesian Information Criterion] (BIC) or the [http://en.wikipedia.org/wiki/Akaike_information_criterion Akaike Information Criterion] (AIC) (Defaults to BIC. AIC can be specified by setting the use_aic flag). | ||
+ | |||
+ | For more details on the model API, see the test files: | ||
+ | https://github.com/mchelem/biopython/blob/master/Tests/test_TorusDBNTrainer.py and https://github.com/mchelem/biopython/blob/master/Tests/test_TorusDBNModel.py. | ||
+ | |||
+ | === Performance === | ||
+ | |||
+ | A few performance measurements were made comparing test cases implemented both in C++ and in Python. The tests were run in a computer with the following specification: | ||
+ | Core 2 Duo T7250 2.00GHz, Memory Dual Channel 4.0GB (2x2048) 667 MHz DDR2 SDRAM, Hard Drive 200GB 7200RPM. | ||
+ | |||
+ | |||
+ | There were no significant performance differences. | ||
+ | For both implementations the methods responsible for consuming most cpu time were the same: | ||
+ | |||
+ | [[File:Hmm_discrete.png|400px|thumb|left|DBN with discrete nodes, C++ implementation ]] | ||
+ | [[File:Hmm_discrete_py.png|400px|thumb|center|DBN with discrete nodes, Python implementation ]] | ||
+ | |||
+ | |||
+ | |||
+ | The profiling tests were made using [http://valgrind.org/info/tools.html#callgrind Callgrind] and visualized using [http://kcachegrind.sourceforge.net/ Kcachegrind]. | ||
+ | |||
+ | Here are the average running time of the examples available with Mocapy (10 runs): | ||
+ | |||
+ | {| class="wikitable" border="1" | ||
+ | |- | ||
+ | ! Test name | ||
+ | ! C++ (s) | ||
+ | ! Python (s) | ||
+ | |- | ||
+ | | hmm_simple | ||
+ | | 0.52 | ||
+ | | 0.58 | ||
+ | |- | ||
+ | | hmm_discrete | ||
+ | | 48.12 | ||
+ | | 43.45 | ||
+ | |- | ||
+ | | discrete_hmm_with_prior | ||
+ | | 55.95 | ||
+ | | 50.09 | ||
+ | |- | ||
+ | | hmm_dirichlet | ||
+ | | 340.72 | ||
+ | | 353.98 | ||
+ | |- | ||
+ | | hmm_factorial | ||
+ | | 0.01 | ||
+ | | 0.12 | ||
+ | |- | ||
+ | | hmm_gauss_1d | ||
+ | | 53.97 | ||
+ | | 63.39 | ||
+ | |- | ||
+ | | hmm_gauss | ||
+ | | 16.02 | ||
+ | | 16.96 | ||
+ | |- | ||
+ | | hmm_multinomial | ||
+ | | 134.64 | ||
+ | | 125.83 | ||
+ | |- | ||
+ | | hmm_poisson | ||
+ | | 11.00 | ||
+ | | 10.60 | ||
+ | |- | ||
+ | | hmm_vonmises | ||
+ | | 7.22 | ||
+ | | 7.36 | ||
+ | |- | ||
+ | | hmm_torus | ||
+ | | 53.79 | ||
+ | | 53.65 | ||
+ | |- | ||
+ | | hmm_kent | ||
+ | | 61.35 | ||
+ | | 61.06 | ||
+ | |- | ||
+ | | hmm_bippo | ||
+ | | 40.66 | ||
+ | | 41.81 | ||
+ | |- | ||
+ | | infenginehmm | ||
+ | | 0.01 | ||
+ | | 0.12 | ||
+ | |- | ||
+ | | infenginemm | ||
+ | | 0.01 | ||
+ | | 0.15 | ||
+ | |} | ||
+ | |||
+ | |||
+ | ==== TorusDBN ==== | ||
+ | |||
+ | Even though the PDB files are read, parsed and transformed in a format mocapy can understand, the most time consuming methods are the ones performing mathematical operations during the sampling process (Chebyshev and exp, for example). | ||
+ | |||
+ | [[File:TorusDBN.png|400px|thumb|right|Training of the TorusDBN model ]] | ||
+ | |||
+ | The model has been trained with a training set consisting of about 950 chains with maximum 20% homology, resolution below 1.6 Å and R-factor below 25%. It took about 67 minutes to read and train the whole dataset. | ||
+ | |||
+ | The resulting DBN is available at https://github.com/mchelem/biopython/blob/master/Tests/TorusDBN/pisces_dataset.dbn and can be loaded directly into the model as explained in the TorusDBN section above. | ||
+ | |||
+ | === Future work === | ||
+ | |||
+ | The summer is over, but the work continues... There are still a lot of things I intend to work on: | ||
+ | |||
+ | * Test the trained models to check their effectiveness in protein structure prediction. | ||
+ | |||
+ | * Try to reduce dynamic allocation as it is responsible for a lot of running time. | ||
+ | |||
+ | * Guarantee there are no memory leaks in the bindings. |
Latest revision as of 15:31, 24 August 2011
Mocapy++ is a machine learning toolkit for training and using Bayesian networks. It has been used to develop probabilistic models of biomolecular structures. The goal of this project is to develop a Python interface to Mocapy++ and integrate it with Biopython. This will allow the training of a probabilistic model using data extracted from a database. The integration of Mocapy++ with Biopython will provide a strong support for the field of protein structure prediction, design and simulation.
Contents |
Introduction
Discovering the structure of biomolecules is one of the biggest problems in biology. Given an amino acid or base sequence, what is the three dimensional structure? One approach to biomolecular structure prediction is the construction of probabilistic models. A Bayesian network is a probabilistic model composed of a set of variables and their joint probability distribution, represented as a directed acyclic graph. A dynamic Bayesian network is a Bayesian network that represents sequences of variables. These sequences can be time-series or sequences of symbols, such as protein sequences. Directional statistics is concerned mainly with observations which are unit vectors in the plane or in three-dimensional space. The sample space is typically a circle or a sphere. There must be special directional methods which take into account the structure of the sample spaces. The union of graphical models and directional statistics allows the development of probabilistic models of biomolecular structures. Through the use of dynamic Bayesian networks with directional output it becomes possible to construct a joint probability distribution over sequence and structure. Biomolecular structures can be represented in a geometrically natural, continuous space. Mocapy++ is an open source toolkit for inference and learning using dynamic Bayesian networks that provides support for directional statistics. Mocapy++ is excellent for constructing probabilistic models of biomolecular structures; it has been used to develop models of protein and RNA structure in atomic detail. Mocapy++ is used in several high-impact publications, and will form the core of the molecular modeling package Phaistos, which will be released soon. The goal of this project is to develop a highly useful Python interface to Mocapy++, and to integrate that interface with the Biopython project. Through the Bio.PDB module, Biopython provides excellent functionality for data mining biomolecular structure databases. Integrating Mocapy++ and Biopython will allow training a probabilistic model using data extracted from a database. Integrating Mocapy++ with Biopython will create a powerful toolkit for researchers to quickly implement and test new ideas, try a variety of approaches and refine their methods. It will provide strong support for the field of biomolecular structure prediction, design, and simulation.
Author & Mentors
Michele Silva michele.silva@gmail.com
Mentors
- Thomas Hamelryck
- Eric Talevich
Project Schedule
Work Plan
Gain understanding of SEM and directional statistics
- Review the theory behind machine learning for bioinformatics, Markov chain Monte Carlo and dynamic Bayesian networks.
- Build the theoretical background on the algorithms used in Mocapy++, such as parameter learning of Bayesian networks using Stochastic Expectation Maximization (SEM).
Study Mocapy++'s use cases
- Read several papers and attempt to replicate part of the experiments described using Mocapy++.
- Get a better understanding of biological sequence analysis done through probabilistic models of proteins and nucleic acids.
Work with Mocapy++
- Understand Mocapy++'s internal architecture and algorithms by exploring its source code and running its test cases.
- Research other applications of Mocapy++ in Bioinformatics.
Design Mocapy++'s Python interface
- Explore the source code of Biopython to understand its design and implementation. The Mocapy++ interface to be included in Biopython must be made compatible with the methods of solving problems in Biopython.
- Design a Python interface for Mocapy++, based on its data structures and algorithms. Examine Mocapy++'s use cases and existing test cases to provide guidance for the interface design.
Implement Python bindings
- Implement test cases in Python using the new interface to Mocapy++.
- Implement python bindings for the defined interface.
Explore Mocapy++'s applications
- Develop example applications that involve data mining of biomolecular structure databases using Biopython.
- Formulate probabilistic models using Python-Mocapy++. Apply the models to solve biological problems. Examples of problems that can be solved using dynamic Bayesian networks include deciding if a pair of sequences is evolutionarily related, finding sequences which are homologous to a known evolutionary family and predicting RNA secondary structure.
Project Code
Hosted at the gSoC11 Mocapy branch
Project Progress
Options to create Python bindings to C++ code
Swig
There is already an effort to provide bindings for Mocapy++ using Swig. However, Swig is not the best option if performance is to be required. The Sage project aims at providing an open source alternative to Mathematica or Maple. Cython was developed in conjunction with Sage (it is an independent project, though), thus it is based on Sage's requirements. They tried Swig, but declined it for performance issues. According to the Sage programming guide "The idea was to write code in C++ for SAGE that needed to be fast, then wrap it in SWIG. This ground to a halt, because the result was not sufficiently fast. First, there is overhead when writing code in C++ in the first place. Second, SWIG generates several layers of code between Python and the code that does the actual work". This was written back in 2004, but it seems things didn't evolve much. The only reason I would consider Swig is for future including Mocapy++ bindings on BioJava and BioRuby projects.
Boost Python
Boost Python is comprehensive and well accepted by the Python community. I would go for it for its extensive use and testing. I would decline it for being hard to debug and having a complicated building system. I don't think it would be worth including a boost dependency just for the sake of creating the Python bindings, but since Mocapy++ already depends on Boost, using it becomes a more attractive option. In my personal experience, Boost Python is very mature and there are no limitations on what one can do with it. When it comes to performance, Cython still overcomes it. Have a look at the Cython C++ wrapping benchmarks and check the timings of Cython against Boost Python. There are also previous benchmarks comparing Swig and Boost Python.
Cython
It is incredibly faster than other options to create python bindings to C++ code, according to several benchmarks available on the web. Check the Simple benchmark between Cython and Boost.Python. It is also very clean and simple, yet powerful. Python's doc on porting extension modules mentions cython: "If you are writing a new extension module, you might consider Cython." Cython has now support for efficient interaction with numpy arrays. it is a young, but developing language and I would definitely give it a try for its leanness and speed.
Since Boost is well supported and Mocapy++ already relies on it, we decided to use Boost.Python for the bindings.
For further information see Mocapy++Biopython - Box of ideas.
Bindings Prototype
The source code for the prototype is on the gSoC11 branch: http://mocapy.svn.sourceforge.net/viewvc/mocapy/branches/gSoC11/bindings_prototype/
Bindings for a few Mocapy++ features and a couple of examples to find possible implementation and performance issues.
Procedure
- Implemented the examples hmm_discrete and discrete_hmm_with_prior in Python, assuming the interface Mocapy++ already provides.
- Implemented the bindings to provide a minimum subset of functionality, in order to run the implemented examples.
- Compared the performance of C++ and Python versions.
Mocapy++’s interface remained unchanged, so the tests look similar to the ones in Mocapy/examples.
In the prototype the bindings were all implemented in a single module. For the actual implementation, we could mirror the src packages structure, having separated bindings for each package such as discrete, inference, etc.
It was possible to implement all the functionality required to run the examples. It was not possible to use the vector_indexing_suite when creating bindings for vectors of MDArrays. A few operators (in the MDArray) must be implemented in order to export indexable C++ containers to Python.
Two Mocapy++ examples that use discrete nodes were implemented in Python. There was no problem in exposing Mocapy’s data structures and algorithms. The performance of the Python version is very close to the original Mocapy++.
For additional details have a look at the Mocapy++ Bindings Prototype report.
Bindings Implementation
Bindings for the core functions and data structures
Data structures
Mocapy uses an internal data structure to represent arrays: MDArray. In order to make it easier for the user to interact with Mocapy's API, it was decided to provide an interface that accepts numpy arrays. Therefore, it was necessary to implement a translation between a numpy array and an MDArray.
The translation from MDArray to python was done through the use of Boost.Python to_python_converter. We've implemented a template method convert_MDArray_to_numpy_array, which converts an MDArray of any basic type to a corresponding numpy array. In order to perform the translation the original array's shape and internal data are copied into a new numpy array.
The numpy array was created using the Numpy Array API. The creation of a new PyArrayObject using existing data (PyArray_SimpleNewFromData) doesn't copy the array data, it just stores a pointer to it. Thus, one can only free the data when there is no reference to the object. This was done through the use of a Capsule. Besides encapsulating the data, the capsule also stores a destructor to be used when the array is destroyed. The PyArrayObject has a field named "base" which points to the capsule.
The translation from Python to C++, i.e. creating an MDArray from a numpy array is slightly more complex. Boost.Python will provide a chunk of memory into which the new C++ object must constructed in-place. See the How to write boost.python converters article for more details.
A translation between std::vector of basic types (double, int...) and Python list was also implemented. For std::vector of custom types, such as Node, the translation to a Python list was not performed. If done the same ways as for basic types, a type error is raised: "TypeError: No to_python (by-value) converter found for C++ type". When using vector_indexing_suite this problem was already solved. See Wrapping std::vector<AbstractClass*>. The only inconvenience of using the vector_indexing_suite is creating new types such as vector_Node, instead of using a standard Python list.
The code for the translations is in the mocapy_data_structures module.
Core functions
The mocapy Python packages follow Mocapy's current source tree. For each package, a shared library with the bindings was created. This makes compilation faster and debug easier. Also, if a single library was created it wouldn't be possible to define packages.
Each of the libraries is called libmocapy_<nameofthepackage>. For example, libmocapy_gaussian provides bindings for the gaussian nodes and probability distributions. The libmocapy_data_structures is used by other libraries and, therefore, must be imported first. This is done on the Python side. Each of the libmocapy_* libraries is imported in the corresponding package. See Creating Packages.
The bindings code can be found in the Bindings directory.
Currently, tests to the just created interface are being developed. There are a few tests already implemented under the framework package: mocapy/framework/tests
Bindings for the remaining Mocapy++ functionality
Data structures
While implementing the bindings for the remaining Mocapy++ functionality there were problems with methods that take pointers and references to an mdarray:
- It is not possible to call a method which takes a pointer if the object is created on the python side. See how to call a function that expects a pointer?.
- It is not possible to automatically translate a non const reference. The custom rvalue converters only match functions with the following signatures:
void foo(std::vector<double> const& array); // pass by const-reference void foo(std::vector<double> array); // pass by value
For further details see How can I wrap functions which take C++ containers as arguments?
The mdarray is created in python using a numpy.array that is translated to c++ using custom converters. The custom converters are registered in the global Boost.Python registry near the top of the module initialization function. Once flow control has passed through the registration code the automatic conversions from and to Python.
Because of this automatic conversions, it was necessary to create wrappers for functions which take pointers as arguments and change the functions which take references, to get const references. Because Mocapy++ is not const correct, changes are needed to use the const references properly. While the changes are being done, some const_cast have been used. When using const_cast one must be aware it is not always safe.
The call policies were also reviewed. When using an incorrect return value policy, you won't get a compile error, but your code will crash at runtime.
Examples
Mocapy++'s examples were implemented in Python, using the exposed API and data type conversions. http://mocapy.svn.sourceforge.net/viewvc/mocapy/branches/gSoC11/python/examples/
Testing and Improving Mocapy Bindings
Before integrating to Biopython, some unit testing was required, to detect possible errors and make sure future changes that break functionality won't go unnoticed.
For every Python package, it was created a "tests" directory which contains the unit tests created for each module. Here is one example of the tests created for the framework package: http://mocapy.svn.sourceforge.net/viewvc/mocapy/branches/gSoC11/python/mocapy/framework/tests/
While testing the code, a few issues were detected:
- Object ownership:
When passing an object created on the C++ side to a method that takes a pointer as an argument, one should be careful about the life time of that object.
For example, the set_random_gen method takes a pointer to a RandomGen object. The following code works just fine.
random_gen = RandomGen() node.set_random_gen(random_gen=random_gen)
But if instead of doing that, we do the following:
node.set_random_gen(random_gen=RandomGen())
The reference count has not been incremented and therefore the object can be destroyed.
The way to solve the problem is to make sure the C++ object is held by auto_ptr:
class_<RandomGen, std::auto_ptr<RandomGen> >("RandomGen")
Then make a thin wrapper function which takes an auto_ptr parameter:
void node_set_random_gen(Node& node, std::auto_ptr<RandomGen> random_gen) { node.set_random_gen(random_gen.get()); node.release(); }
For further details, see How can I wrap a function which needs to take ownership of a raw pointer?
Pointers returned via manage_new_object will also be held by auto_ptr, so the transfer-of-ownership works correctly. When using this call policy the caller is responsible for deleting the C++ object from the heap.
- Translation from numpy.array to a float mdarray
If the numpy array is an integer array, the translation creates an mdarray<int> and this is passed to a method which expects an mdarray of floats. This generates incorrect results.
The way to deal with that from the user perspective is either using floating pointer numbers to create the array or setting the ndtype parameter when creating the array:
x = numpy.array([[1,2,3,4,5,6]], dtype=numpy.float64)
Building and Distributing Mocapy as a Package
Distutils was used to distribute Mocapy's Python modules.
Besides distributing the python code, it was also necessary to build the extension modules. Building Extensions with boost.python describes ways to build extensions using distutils.
Mocapy's setup.py can be found at http://mocapy.svn.sourceforge.net/viewvc/mocapy/branches/gSoC11/python/setup.py?revision=418&view=markup
Using the setup script, mocapy installation is done in a few steps:
- Build the mocapy library using cmake (usual procedure described in mocapy docs);
- Issue "python setup.py build", to build the extension modules;
- Issue "python setup.py install", to install the package (normally, the install procedure does the step above in case you didn't).
Integration with Biopython
API Design
In order to use Mocapy in conjunction with Biopython, a new module for PDB-specific features was added to Bio.PDB. This is where the API is being designed.
Mocapy is added as an optional dependency in Biopython. Inside the function or module that requires Mocapy, "import mocapy" is wrapped in a try/except block. A MissingPythonDependencyError is issued if the import fails.
Things that are being studied to be included in the module:
- extract the backbone dihedral angles from a given set of structures;
- use this data to train a TorusDBN-like model;
- automatically decide on the best model using the BIC criterion.
Barnacle
Frellsen J, Moltke I, Thiim M, Mardia KV, Ferkinghoff-Borg J, et al. 2009 A Probabilistic Model of RNA Conformational Space. PLoS Comput Biol 5(6): e1000406. doi:10.1371/journal.pcbi.1000406.
RNA 3-D structure prediction methods require an accurate energy function and a conformational sampling procedure. Barnacle focuses on the problem of conformational sampling.
The aim of BARNACLE (BAyesian network model of RNA using Circular distributions and maximum Likelihood Estimation) is to capture both the marginal distributions of each of the angles and the local dependencies between them. Barnacle describes RNA structure in a natural continuous space. It can be used purely as a proposal distribution, but also as an energy term enforcing realistic local conformations. The model combines a dynamic Bayesian network (DBN) with directional statistics.
The DBN represents nine consecutive dihedral angles, where the seven central angles originate from a single nucleotide. Each slice j (a column of three variables) corresponds to one dihedral angle in an RNA fragment. The variables in each slice are: an angle identifier, Dj, a hidden variable, Hj, and an angular variable, Aj. The angle identifier keeps track of which dihedral angle is represented by a slice, while the angular node models the actual dihedral angle value. The hidden nodes induce dependencies between all angles along the sequence (and not just between angles in consecutive slices).
The original source code for Barnacle, which contains an embedded version of Mocapy written in Python, can be found at http://sourceforge.net/projects/barnacle-rna.
The modified version of Barnacle, changed to work with the Mocapy bindings can be found at https://github.com/mchelem/biopython/tree/master/Bio/PDB/Barnacle.
Here is an example of use:
model = Barnacle('ACCU') model.sample() print 'log likelihood = ', model.get_log_likelihood() model.save_structure('structure01.pdb')
TorusDBN
Wouter Boomsma, Kanti V. Mardia, Charles C. Taylor, Jesper Ferkinghoff-Borg, Anders Krogh, and Thomas Hamelryck. A generative, probabilistic model of local protein structure. Proc Natl Acad Sci U S A. 2008 July 1; 105(26): 8932–8937. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2440424/
TorusDBN aims at predicting the 3D structure of a biomolecule given its amino-acid sequence. It is a continuous probabilistic model of the local sequence–structure preferences of proteins in atomic detail. The backbone of a protein can be represented by a sequence of dihedral angle pairs, φ and ψ that are well known from the Ramachandran plot. Two angles, both with values ranging from −180° to 180°, define a point on the torus. Hence, the backbone structure of a protein can be fully parameterized as a sequence of such points.
The circular nodes represent stochastic variables. The rectangular boxes along the arrows illustrate the nature of the conditional probability distribution between them. A hidden node emits angle pairs, amino acid information, secondary structure labels and cis/trans information.
The TorusDBN model is originally implemented as part of the backboneDBN package, which is freely available at
http://sourceforge.net/projects/phaistos/.
A new version of the TorusDBN model was implemented in the context of this project and can be found at
https://github.com/mchelem/biopython/tree/master/Bio/PDB/TorusDBN.
The TorusDBNTrainer can be used to train a model with a given training set:
trainer = TorusDBNTrainer() trainer.train(training_set) # training_set is a list of files model = trainer.get_model()
Then the model can be used to sample new sequences:
model.set_aa('ACDEFGHIK') model.sample() print model.get_angles() # The sampled angles.
When creating a model, it is possible to create a new DBN specifying the size of the hidden node or loading the DBN from a file.
model = TorusDBNModel() model.create_dbn(hidden_node_size=10) model.save_dbn('test.dbn')
model = TorusDBNModel() model.load_dbn('test.dbn') model.set_aa('ACDEFGHIK') model.sample() print model.get_angles() # The sampled angles.
It is also possible to choose the best size for the hidden node using the find_optimal_model method:
trainer = TorusDBNTrainer() hidden_node_size, IC = trainer.find_optimal_model(training_set) model = trainer.get_model()
IC is either the Bayesian Information Criterion (BIC) or the Akaike Information Criterion (AIC) (Defaults to BIC. AIC can be specified by setting the use_aic flag).
For more details on the model API, see the test files: https://github.com/mchelem/biopython/blob/master/Tests/test_TorusDBNTrainer.py and https://github.com/mchelem/biopython/blob/master/Tests/test_TorusDBNModel.py.
Performance
A few performance measurements were made comparing test cases implemented both in C++ and in Python. The tests were run in a computer with the following specification: Core 2 Duo T7250 2.00GHz, Memory Dual Channel 4.0GB (2x2048) 667 MHz DDR2 SDRAM, Hard Drive 200GB 7200RPM.
There were no significant performance differences.
For both implementations the methods responsible for consuming most cpu time were the same:
The profiling tests were made using Callgrind and visualized using Kcachegrind.
Here are the average running time of the examples available with Mocapy (10 runs):
Test name | C++ (s) | Python (s) |
---|---|---|
hmm_simple | 0.52 | 0.58 |
hmm_discrete | 48.12 | 43.45 |
discrete_hmm_with_prior | 55.95 | 50.09 |
hmm_dirichlet | 340.72 | 353.98 |
hmm_factorial | 0.01 | 0.12 |
hmm_gauss_1d | 53.97 | 63.39 |
hmm_gauss | 16.02 | 16.96 |
hmm_multinomial | 134.64 | 125.83 |
hmm_poisson | 11.00 | 10.60 |
hmm_vonmises | 7.22 | 7.36 |
hmm_torus | 53.79 | 53.65 |
hmm_kent | 61.35 | 61.06 |
hmm_bippo | 40.66 | 41.81 |
infenginehmm | 0.01 | 0.12 |
infenginemm | 0.01 | 0.15 |
TorusDBN
Even though the PDB files are read, parsed and transformed in a format mocapy can understand, the most time consuming methods are the ones performing mathematical operations during the sampling process (Chebyshev and exp, for example).
The model has been trained with a training set consisting of about 950 chains with maximum 20% homology, resolution below 1.6 Å and R-factor below 25%. It took about 67 minutes to read and train the whole dataset.
The resulting DBN is available at https://github.com/mchelem/biopython/blob/master/Tests/TorusDBN/pisces_dataset.dbn and can be loaded directly into the model as explained in the TorusDBN section above.
Future work
The summer is over, but the work continues... There are still a lot of things I intend to work on:
- Test the trained models to check their effectiveness in protein structure prediction.
- Try to reduce dynamic allocation as it is responsible for a lot of running time.
- Guarantee there are no memory leaks in the bindings.