GSOC

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m (Added 2011 proposals)
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=== 2012 ===
 
=== 2012 ===
==== SearchIO ====
+
==== [http://biopython.org/wiki/SearchIO SearchIO] ====
 
;  Rationale
 
;  Rationale
 
:  Biopython has general APIs for parsing and writing assorted sequence file formats (SeqIO), multiple sequence alignments (AlignIO), phylogenetic trees (Phylo) and motifs (Bio.Motif). An obvious omission is something equivalent to BioPerl's SearchIO. The goal of this proposal is to develop an easy-to-use Python interface in the same style as SeqIO, AlignIO, etc but for pairwise search results. This would aim to cover EMBOSS muscle & water, BLAST XML, BLAST tabular, HMMER, Bill Pearson's FASTA alignments, and so on.
 
:  Biopython has general APIs for parsing and writing assorted sequence file formats (SeqIO), multiple sequence alignments (AlignIO), phylogenetic trees (Phylo) and motifs (Bio.Motif). An obvious omission is something equivalent to BioPerl's SearchIO. The goal of this proposal is to develop an easy-to-use Python interface in the same style as SeqIO, AlignIO, etc but for pairwise search results. This would aim to cover EMBOSS muscle & water, BLAST XML, BLAST tabular, HMMER, Bill Pearson's FASTA alignments, and so on.
Line 39: Line 39:
 
:  Medium/Hard depending on how many objectives are attempted. The student needs to be fluent in Python and have knowledge of the BioPython codebase. Experience with all of the command line tools listed would be clear advantages, as would first hand experience using BioPerl's SearchIO. You will also need to know or learn the git version control system.
 
:  Medium/Hard depending on how many objectives are attempted. The student needs to be fluent in Python and have knowledge of the BioPython codebase. Experience with all of the command line tools listed would be clear advantages, as would first hand experience using BioPerl's SearchIO. You will also need to know or learn the git version control system.
 
;  Mentors
 
;  Mentors
:  Peter Cock
+
[http://www.hutton.ac.uk/staff/peter-cock Peter Cock]
  
====  Representation and manipulation of genomic variants ====
+
====  [http://arklenna.tumblr.com/tagged/gsoc2012 Representation and manipulation of genomic variants] ====
 
;  Rationale
 
;  Rationale
 
:  Computational analysis of genomic variation requires the ability to reliably communicate and manipulate variants. The goal of this project is to provide facilities within BioPython to represent sequence variation objects, convert them to and from common human and file representations, and provide common manipulations on them.
 
:  Computational analysis of genomic variation requires the ability to reliably communicate and manipulate variants. The goal of this project is to provide facilities within BioPython to represent sequence variation objects, convert them to and from common human and file representations, and provide common manipulations on them.
Line 60: Line 60:
 
:  Easy-to-Medium depending on how many objectives are attempted. The student will need have skills in most or all of: basic molecular biology (genomes, transcripts, proteins), genomic variation, Python, BioPython, Perl, BioPerl, NCBI Eutilities and/or Ensembl API. Experience with computer grammars is highly desirable. You will also need to know or learn the git version control system.
 
:  Easy-to-Medium depending on how many objectives are attempted. The student will need have skills in most or all of: basic molecular biology (genomes, transcripts, proteins), genomic variation, Python, BioPython, Perl, BioPerl, NCBI Eutilities and/or Ensembl API. Experience with computer grammars is highly desirable. You will also need to know or learn the git version control system.
 
;  Mentors
 
;  Mentors
:  Reece Hart (Locus Development, San Francisco); Brad Chapman; James Casbon
+
[http://www.linkedin.com/in/reece Reece Hart]
 +
:  [https://github.com/chapmanb Brad Chapman]
 +
:  [http://casbon.me/ James Casbon]
 
=== 2011 ===
 
=== 2011 ===
 +
====  [http://biopython.org/wiki/GSoC2011_mtrellet Biomolecular Interface Analysis] ====
 +
;  Student
 +
: Mikael Trellet
 +
;  Rationale
 +
:  Analysis of protein-protein complexes interfaces at a residue level yields significant information on the overall binding process. Such information can be broadly used for example in binding affinity studies, interface design, and enzymology. To tap into it, there is a need for tools that systematically and automatically analyze protein structures, or that provide means to this end. Protorop (http://www.bioinformatics.sussex.ac.uk/protorp/) is an example of such a tool and the elevated number of citations the server has had since its publication acknowledge its importance. However, being a webserver, Protorop is not suited for large-scale analysis and it leaves the community dependent on its maintainers to keep the service available. On the other hand, Biopython’s structural biology module, Bio.PDB, provides the ideal parsing machinery and programmatic structures for the development of an offline, open-source library for interface analysis. Such a library could be easily used in large-scale analysis of protein-protein interfaces, for example in the CAPRI experiment evaluation or in benchmark statistics. It would be also reasonable, if time permits, to extend this module to deal with protein-DNA or protein-RNA complexes, as Biopython supports nucleic acids already.
 +
;  Approach & Goals
 +
* Add the new module backbone in current Bio.PDB code base
 +
** Evaluate possible code reuse and call it into the new module
 +
** Try simple calculations to be sure that there is stability between the different modules (parsing for example) and functions
 +
* Define a stable benchmark
 +
** Select few PDB files among interface size and proteins size would be different
 +
* Extend IUPAC.Data module with residue information
 +
** Deduce residues weight from Atom instead of direct dictionary storage
 +
** Polar/charge character (dictionary or influenced by pH)
 +
** Hydrophobicity scale(s)
 +
* Implement Extended Residue class as a subclass of Residue
 +
* Implement Interface object and InterfaceAnalysis module
 +
* Develop functions for interface analysis
 +
** Calculation of interface polar character statistics (% of polar residues, apolar, etc)
 +
** Calculation of BSA calling MSMS or HSA
 +
** Calculation of SS element statistics in the interface through DSSP
 +
** Unit tests and use of results as input for further calculations by other tools and scripts
 +
* Develop functions for Interface comparison
 +
* Code organization and final testing
 +
 +
;  Difficulty and needed skills
 +
:  Easy/Medium. Working knowledge of the Bio.PDB module of BioPython. Knowledge of structural biology in general and associated file formats (PDB).
 +
;  Mentors
 +
:  [http://nmr.chem.uu.nl/~joao João Rodrigues]
 +
:  [http://etal.myweb.uga.edu/ Eric Talevich]
 +
 +
====  [http://biopython.org/wiki/GSOC2011_Mocapy A Python bridge for Mocapy++] ====
 +
;  Student
 +
: Michele Silva
 +
;  Rationale
 +
: 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.
 +
;  Approach & Goals
 +
: 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.
 +
;  Mentors
 +
:  [http://etal.myweb.uga.edu/ Eric Talevich]
 +
:  [http://wiki.binf.ku.dk/User:Thomas_Hamelryck Thomas Hamelryck]
 +
 +
====  [http://biopython.org/wiki/GSOC2011_MocapyExt MocapyExt] ====
 +
; Student
 +
: Justinas V. Daugmaudis
 +
;  Rationale
 +
:  BioPython is a very popular library in Bioinformatics and Computational Biology. Mocapy++ is a machine learning toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs), which encode probabilistic relationships among random variables in a domain. Mocapy++ is freely available under the GNU General Public Licence (GPL) from SourceForge. The library supports a wide spectrum of DBN architectures and probability distributions, including distributions from directional statistics. Notably, Kent distribution on the sphere and the bivariate von Mises distribution on the torus, which have proven to be useful in formulating probabilistic models of protein and RNA structure.
 +
Such a highly useful and powerful library, which has been used in such projects as TorusDBN, Basilisk, FB5HMM with great success, is the result of the long-term effort. The original Mocapy implementation dates back to 2004, and since then the library has been rewritten in C++. However, C++ is a statically typed and compiled programming language, which does not facilitate rapid prototyping. As a result, currently Mocapy++ has no provisions for dynamic loading of custom node types, and a mechanism to plug-in new node types that would not require to modify and recompile the library is of interest. Such a plug-in interface would assist rapid prototyping by allowing to quickly implement and test new probability distributions, which, in turn, could substantially reduce development time and effort; the user would be empowered to extend Mocapy++ without modifications and subsequent recompilations. Recognizing this need, the project (herein referred as MocapyEXT), with the aim to improve the current Mocapy++ node type extension mechanism, has been proposed by T. Hamelryck.
 +
;  Approach & Goals
 +
: The MocapyEXT project is largely an engineering effort to bring a transparent Python plug-in interface to Mocapy++, where built-in and dynamically loaded node types could be used in a uniform manner. Also, externally implemented and dynamically loaded nodes could be modified by a user and these changes will not necessitate the recompilation of the client program, nor the accompanying Mocapy++ library. This will facilitate rapid prototyping, ease the adaptation of currently existing code, and improve the software interoperability whilst introducing minimal changes to the existing Mocapy++ interface, thus facilitating a smooth acceptance of the changes introduced by MocapyEXT.
 +
;  Mentors
 +
:  [http://etal.myweb.uga.edu/ Eric Talevich]
 +
:  [http://wiki.binf.ku.dk/User:Thomas_Hamelryck Thomas Hamelryck]
 +
 
=== 2010 ===
 
=== 2010 ===
 
=== 2009 ===
 
=== 2009 ===
 +
 +
<!--
 +
=== XXXX ===
 +
====  Mock Proposal ====
 +
;  Rationale
 +
:  aaa
 +
;  Approach & Goals
 +
: zzz
 +
;  Difficulty and needed skills
 +
:  yyy
 +
;  Mentors
 +
:  xxx
 +
-->

Revision as of 22:44, 8 March 2013

Contents

Introduction

The Open Bioinformatics foundation successfully applied to participate in the Google Summer of Code.

Please read the GSoC page at the Open Bioinformatics Foundation and the main Google Summer of Code page for more details about the program.

Mentor List

Usually, each BioPython proposal has one or more mentors assigned to it. Nevertheless, we encourage potential students to contact the mailing list with their own ideas for proposals. There is therefore not a set list of 'available' mentors, since it highly depends on which projects are proposed every year.

Past mentors include:

Proposals

2013

The BioPython proposals for 2013 will be published here once discussed. We encourage potential students to join the mailing lists and actively participate in these discussions, either by submitting their own ideas or contributing to improving existing ones.

Past Proposals

2012

SearchIO

Rationale
Biopython has general APIs for parsing and writing assorted sequence file formats (SeqIO), multiple sequence alignments (AlignIO), phylogenetic trees (Phylo) and motifs (Bio.Motif). An obvious omission is something equivalent to BioPerl's SearchIO. The goal of this proposal is to develop an easy-to-use Python interface in the same style as SeqIO, AlignIO, etc but for pairwise search results. This would aim to cover EMBOSS muscle & water, BLAST XML, BLAST tabular, HMMER, Bill Pearson's FASTA alignments, and so on.
Approach
Much of the low level parsing code to handle these file formats already exists in Biopython, and much as the SeqIO and AlignIO modules are linked and share code, similar links apply to the proposed SearchIO module when using pairwise alignment file formats. However, SearchIO will also support pairwise search results where the pairwise sequence alignment itself is not available (e.g. the default BLAST tabular output). A crucial aspect of this work will be to design a pairwise-search-result object heirachy that reflects this, probably with a subclass inheriting from both the pairwise-search-result and the existing MultipleSequenceAlignment object. Beyond the initial challenge of an iterator based parsing and writing framework, random access akin to the Bio.SeqIO.index and index_db functionality would be most desirable for working with large datasets.
Challenges
The project will cover a range of important file formats from major Bioinformatics tools, thus will require familiarity with running these tools, and understanding their output and its meaning. Inter-converting file formats is part of this.
Difficulty and needed skills
Medium/Hard depending on how many objectives are attempted. The student needs to be fluent in Python and have knowledge of the BioPython codebase. Experience with all of the command line tools listed would be clear advantages, as would first hand experience using BioPerl's SearchIO. You will also need to know or learn the git version control system.
Mentors
Peter Cock

Representation and manipulation of genomic variants

Rationale
Computational analysis of genomic variation requires the ability to reliably communicate and manipulate variants. The goal of this project is to provide facilities within BioPython to represent sequence variation objects, convert them to and from common human and file representations, and provide common manipulations on them.
Approach & Goals
  • Object representation
    • identify variation types to be represented (SNV, CNV, repeats, inversions, etc)
    • develop internal machine representation for variation types
    • ensure coverage of essential standards, including HGVS, GFF, VCF
  • External representations
    • write parser and generators between objects and external string and file formats
  • Manipulations
    • canonicalize variations with more than one valid representation (e.g., ins versus dup and left shifting repeats).
    • develop coordinate mapping between genomic, cDNA, and protein sequences (HGVS)
  • Other
    • release code to appropriate community efforts and write short manuscript
    • implement web service for HGVS conversion
Difficulty and needed skills
Easy-to-Medium depending on how many objectives are attempted. The student will need have skills in most or all of: basic molecular biology (genomes, transcripts, proteins), genomic variation, Python, BioPython, Perl, BioPerl, NCBI Eutilities and/or Ensembl API. Experience with computer grammars is highly desirable. You will also need to know or learn the git version control system.
Mentors
Reece Hart
Brad Chapman
James Casbon

2011

Biomolecular Interface Analysis

Student
Mikael Trellet
Rationale
Analysis of protein-protein complexes interfaces at a residue level yields significant information on the overall binding process. Such information can be broadly used for example in binding affinity studies, interface design, and enzymology. To tap into it, there is a need for tools that systematically and automatically analyze protein structures, or that provide means to this end. Protorop (http://www.bioinformatics.sussex.ac.uk/protorp/) is an example of such a tool and the elevated number of citations the server has had since its publication acknowledge its importance. However, being a webserver, Protorop is not suited for large-scale analysis and it leaves the community dependent on its maintainers to keep the service available. On the other hand, Biopython’s structural biology module, Bio.PDB, provides the ideal parsing machinery and programmatic structures for the development of an offline, open-source library for interface analysis. Such a library could be easily used in large-scale analysis of protein-protein interfaces, for example in the CAPRI experiment evaluation or in benchmark statistics. It would be also reasonable, if time permits, to extend this module to deal with protein-DNA or protein-RNA complexes, as Biopython supports nucleic acids already.
Approach & Goals
  • Add the new module backbone in current Bio.PDB code base
    • Evaluate possible code reuse and call it into the new module
    • Try simple calculations to be sure that there is stability between the different modules (parsing for example) and functions
  • Define a stable benchmark
    • Select few PDB files among interface size and proteins size would be different
  • Extend IUPAC.Data module with residue information
    • Deduce residues weight from Atom instead of direct dictionary storage
    • Polar/charge character (dictionary or influenced by pH)
    • Hydrophobicity scale(s)
  • Implement Extended Residue class as a subclass of Residue
  • Implement Interface object and InterfaceAnalysis module
  • Develop functions for interface analysis
    • Calculation of interface polar character statistics (% of polar residues, apolar, etc)
    • Calculation of BSA calling MSMS or HSA
    • Calculation of SS element statistics in the interface through DSSP
    • Unit tests and use of results as input for further calculations by other tools and scripts
  • Develop functions for Interface comparison
  • Code organization and final testing
Difficulty and needed skills
Easy/Medium. Working knowledge of the Bio.PDB module of BioPython. Knowledge of structural biology in general and associated file formats (PDB).
Mentors
João Rodrigues
Eric Talevich

A Python bridge for Mocapy++

Student
Michele Silva
Rationale
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.
Approach & Goals
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.
Mentors
Eric Talevich
Thomas Hamelryck

MocapyExt

Student
Justinas V. Daugmaudis
Rationale
BioPython is a very popular library in Bioinformatics and Computational Biology. Mocapy++ is a machine learning toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs), which encode probabilistic relationships among random variables in a domain. Mocapy++ is freely available under the GNU General Public Licence (GPL) from SourceForge. The library supports a wide spectrum of DBN architectures and probability distributions, including distributions from directional statistics. Notably, Kent distribution on the sphere and the bivariate von Mises distribution on the torus, which have proven to be useful in formulating probabilistic models of protein and RNA structure.

Such a highly useful and powerful library, which has been used in such projects as TorusDBN, Basilisk, FB5HMM with great success, is the result of the long-term effort. The original Mocapy implementation dates back to 2004, and since then the library has been rewritten in C++. However, C++ is a statically typed and compiled programming language, which does not facilitate rapid prototyping. As a result, currently Mocapy++ has no provisions for dynamic loading of custom node types, and a mechanism to plug-in new node types that would not require to modify and recompile the library is of interest. Such a plug-in interface would assist rapid prototyping by allowing to quickly implement and test new probability distributions, which, in turn, could substantially reduce development time and effort; the user would be empowered to extend Mocapy++ without modifications and subsequent recompilations. Recognizing this need, the project (herein referred as MocapyEXT), with the aim to improve the current Mocapy++ node type extension mechanism, has been proposed by T. Hamelryck.

Approach & Goals
The MocapyEXT project is largely an engineering effort to bring a transparent Python plug-in interface to Mocapy++, where built-in and dynamically loaded node types could be used in a uniform manner. Also, externally implemented and dynamically loaded nodes could be modified by a user and these changes will not necessitate the recompilation of the client program, nor the accompanying Mocapy++ library. This will facilitate rapid prototyping, ease the adaptation of currently existing code, and improve the software interoperability whilst introducing minimal changes to the existing Mocapy++ interface, thus facilitating a smooth acceptance of the changes introduced by MocapyEXT.
Mentors
Eric Talevich
Thomas Hamelryck

2010

2009

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