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1 GSoC 2014
2 2014 Student Projects
3 2014 Projects Idea

GSoC 2014

The Open Bioinformatics Foundation has been accepted as a mentoring organization for Google Summer of Code 2014, with 6 student projects funded!

2014 Student Projects

Sarah Berkemer

Mentored by Christian Höner zu Siederdissen and Ketil Malde (BioHaskell)

Project: "High-performance Transalign algorithm in Haskell"
Blog: http://biohaskell.org/GSoC_blog
Source code: https://github.com/bsarah/transalign

Loris Cro

Mentored by Francesco Strozzi and Raoul Bonnal (BioRuby)

Project: "An ultra-fast scalable RESTful API to query large numbers of VCF datapoints"
Blog: http://kappaloris.github.io/GSoC-2014-OBF/
Source code: https://github.com/kappaloris/GSoC-2014-OBF

Victor Kofia

Mentored by Sarah Keating and Alex Thomas (JSBML)

Project: "Redesign the implementation of mathematical formulas in JSBML"
Blog: http://kofiav.blogspot.ca/
Source code: http://svn.code.sf.net/p/jsbml/code/trunk/core/src/org/sbml/jsbml/math/

Evan Parker

Mentored by Wibowo Arindrarto and Peter Cock (BioPython)

Project: "Addition of a lazy-loading sequence parser to Biopython’s SeqIO package"
Blog: http://evanaparker.com/
Source code: https://github.com/eparker05/biopython/tree/lazy-load

Ibrahim Vazirabad

Mentored by Andreas Dräger and Alex Thomas (JSBML)

Project: "Improving the Plug-in interface for CellDesigner"
Blog: http://jsbmlcelldesigner2014.blogspot.com/
Source code: http://sourceforge.net/projects/jsbml/

Leandro Watanabe

Mentored by Nicolas Rodriguez and Chris Myers (JSBML)

Project: "Dynamic Modeling of Cellular Populations via arrays in JSBML"
Blog: http://lhwatanabe.blogspot.com
Source code: http://sourceforge.net/projects/jsbml/ or http://svn.code.sf.net/p/jsbml/code/trunk/extensions/arrays/

2014 Projects Idea

The details of each of our project ideas are listed below, including potential mentors. Interested mentors and students should subscribe to the OBF/GSoC mailing list and announce their interest.

See the main OBF Google Summer of Code page for more information about the GSoC program and additional ways to get in touch with us.

Cross-project ideas

OBF is an umbrella organization which represents many different programming languages used in bioinformatics. In addition to working with each of the "Bio*" projects (listed below), this year we are also accepting a category of "cross-project" ideas that cover multiple programming languages or projects. These collaborative ideas are broadly defined and can be thought of as "unfinished" — interested students should adapt the ideas to their own strengths and goals, and are responsible for the quality of the final proposed idea in their application.

Feel free to propose your own entirely new idea. You can also draw ideas from Genome Informatics (GMOD) and the National Evolutionary Synthesis Center (NESCent).

Language APIs for the Systems Biology Markup Language (SBML) through the JVM

Rationale: The standard Java implementation of SBML, JSBML, is used as a parser for various Java-based systems biology applications. This fulfills one niche, but the versatility of the JVM can be utilized to employ JSBML as a parser for systems biology applications that are written in other languages. Also, JSBML undergoes an active community effort to be up-to-date with current SBML standards.

Approach: This project will aim to present language APIs for languages that may want to employ the SBML structure without building a parser from scratch. Matlab, Mathematica, and Python APIs will be the focus for this project.

Languages and skill: Java, optional: Matlab, Python, (other language)

Mentors: Andreas Dräger, Alex Thomas

WormBase: data visualization

Rationale: WormBase is a central data repository supporting the nematode research community. There are several areas of improvement for data visualization on the website, including some key points raised by the WormBase community.

Approach

Here are a couple requests we've received from the community, but we are open to other ideas:

Create a chromosome map tool - allow users to input and visualize the position of genetic loci. (See community request #1103)
Create a central dogma view to tie together our gene/protein/sequence pages. (See community request #557)

The website's back-end is written in Perl, using some BioPerl as well as custom code. If you do significant work on the back-end, this could lead to or involve BioPerl improvements.

Languages and skills: Front-end: Javascript, HTML5, JS graphical library of your choice (e.g. D3). Back-end: some Perl, including BioPerl.

Mentor(s): Abigail Cabunoc <abigail.cabunoc@oicr.on.ca>, others welcome

Improve SegAnnDB interactive genomic segmentation web app

Rationale: SegAnnDB is an open-source web site for interactive genomic segmentation of DNA copy number profiles, published in Bioinformatics. It combines previous work on data visualization, computer vision, and machine learning into a web site that uses annotated regions to build a user-specific segmentation model. YouTube videos explain how it works: basic annotation, annotating and exporting high-density profiles. The goal of this project is to add features to SegAnnDB.

Approach: The ideal student project would propose to

Add social features for sharing annotations. SegAnnDB currently lets a user login using Mozilla Persona and then add user-specific annotations. These annotations are currently only accessible to the user that creates them, but it would be nice to be able to share them with others. For example, Alice annotates some data then types the email address of her friend Bob into a web form. Bob then receives an email with a web link where he can view Alice's annoatations.
Add adminstrative features. SegAnnDB uses BerkeleyDB, which is very fast but makes updating and deleting profiles a bit tricky. I have started writing a view (plotter.views.delete_profiles) for profile deletion along with some database support (plotter.db.Profile.delete).
Add unit tests using Pyramid recommendations, for example when a profile is processed (plotter.db.Profile.process) test for presence of objects in the database, and then when the profile is deleted, check for deletion of relvant objects and files (PNG scatterplots, probes.bedGraph.gz data).
Add support for browsers that do not render large PNG images. SegAnnDB uses very large PNGs to efficiently visualize DNA copy number profiles, but these are not supported by all browsers and you can test your browser's support on this web page.
Attempt integration with Galaxy, possibly as a Visualization Plugin.
Integration of SegAnnot and PrunedDP extension modules into BioPython.

Required skills: JavaScript and Python. Experience with D3 and Pyramid web framework a plus.

Code: SegAnnDB is implemented as a Pyramid web app with a D3/JavaScript interface. Download the source code with

svn checkout svn://scm.gforge.inria.fr/svnroot/breakpoints/webapp/pyramid SegAnnDB

and then check 00_INSTALL.sh for installation instructions, and email me if anything is unclear.

Mentors: Toby Dylan Hocking tdhock5@gmail.com plus anyone else with experience with D3/Pyramid is welcome to help!

Integrate basic biological data analysis capabilities in fastR (Java/R)

Rationale: R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Despite this, R is hard to mantain and evolve, lacks portability features and is characterized by low performance. The FastR project (http://www.oracle.com/technetwork/java/jvmls2013vitek-2013524.pdf) aims to rethink how to implement R, by leveraging well tested technologies to build a high performance VM. FastR is an implementation of the R programming language in Java using AST interpretation and specialisation for improved performance. The implementation has been extended to support JIT compilation, leading to performance improvements.

The fastR project has been presented to the useR! conference 2013, and it is now being actively developed at https://bitbucket.org/allr/fastr.

Approach: The goal of this project would be to carry on the FastR development by expanding the set of supported R data types, possibly integrating some basic functionalities from Bioconductor which will be extended in the future. The strength of this approach lies in the possibility for Java users to access the vast R ecosystem, and to expand further the inter-operability of R packages and workflows by using other languages built on Java, like JRuby and Jython.

Required skills: Moderate technical difficulty, an interest in statistical problem solving is a plus but not essential. This project requires mid/advanced Java programming skills

Reference to other projects: BioJava, R, BioRuby, Jruby, Jython

Mentors: Jan Vitek, Alberto Arrigoni [arrigonialberto86@gmail.com]

BioInterchange: Convert and Exchange Biological File Formants using RESTful web service

Rationale: BioInterchange Interchange data using the Resource Description Framework (RDF) and let BioInterchange automagically create RDF triples from your TSV, XML, GFF3, GVF, Newick and other files common in Bioinformatics. BioInterchange helps you transform your data sets into linked data for sharing and data integration via command line, web-service, or API. BioInterchange was conceived and designed during NBDC/DBCLS's BioHackathon 2012. Architecture and RDF serialization implementations were provided by Joachim Baran, Geraint Duck provided JSON and XML deserialization implementations and contributed to architecture decisions, guidance on ontology use and applications were given by Kevin B. Cohen and Michel Dumontier, where Michel brought forward and extended the Semanticscience Integrated Ontology (SIO). Jin-Dong Kim helped to define ontology relationships for RDFizing DBCLS' PubAnnotation category annotations. The main idea is to have a central service with can be used as a validator and as interchange service for different languages.

Approach: The project will identify the most common and used file formants for all the currently used language under OBF and will design a RESTful API and will project an implementation for all the supported languages. BioInterchange was developed with Ruby but the scope of the project is to have an agnostic system which let use implement a converter using the best language for that functionality. It expected to have a high traffic for the service so an appropriate refactoring or reimplementation using parallel techniques or languages devoted to parallel programming would be possible.

Difficulty and needed skills: The project is mid / high difficulty, aimed at talented students. Previous knowledge of Ruby or other scripting language is preferred and flexibility in learning other languages is required.
The project requires: Knowledge of advanced programming languages and meta-programming and some concept in parallelizing and web services design.

Mentors: Raoul J.P. Bonnal, Francesco Strozzi, Toshiaki Katayama, Joachim Baran

bionode - A Node.js JavaScript library for client and server side bioinformatics

Rationale: During the development of a web front-end or software with a web-interface, it often becomes clear that a component implemented for the server-side must be performed on the client side, or vice-versa. This generally requires functionalities implemented in one language to be reimplemented in another. Recent developments in Javascript make this unnecessary. Indeed, JavaScript has become a “write once run everywhere” full stack programming language that can be executed in the browser as well as on the server (thanks to Node.js). The web-development community is enthusiastically embracing this technology. In the last year, Node.js modules increased 2.6-fold to a total of 61656. The average growth rate is 175 modules per day, which means that it will quickly surpass Java (71906) and Ruby (71446) having already surpassed all the other languages (e.g., Perl: 29097; Python: 40455).

Consistent with this, web applications for visualizing or interacting with biological data increasingly rely on javascript (e.g. jBrowse, WebApollo, Biodalliance, etc). Surprisingly however, no generic javascript bioinformatics library yet exists, leading independent projects to redundantly implement basic functionality. Here, we propose to develop bionode a core javascript library for handling and analyzing bioinformatics data - mirroring the core functionality of established bio* libraries (e.g. bioperl, bioruby).

This will be done in close collaboration with the developers of BioJS (who build reusable components for visualization of biological data) and of WebApollo/Afra (a gene prediction curation software) for immediate short-term applications. Importantly, we expect the long-term interest for a bioinformatics javascript library to be huge.

Approach: We already seeded this project: bionode is a Node.js module with some bioinformatics methods that work on the client and server side.; Under the supervision of the mentor(s), the GSoC student will add more methods/algorithms to the bionode project. The student could take inspiration from other similar libraries in other programming languages, such as bioperl or bioruby. Methods for input/output and wrangling basic data types should be given a higher priority at the beginning of this projects. For example, implementing some of the functions of biopython.SeqIO could be a project. Another source for inspiration could come from Massive Open Online Course, such as the “Bioinformatics Algorithms” from the University of California, San Diego.

Challenges: Methods provided should be able to run on a server environment (via Node.js) or browser environment (via webpack or browserify). Thus, the module pattern used should be the one used by Node.js (CommonJS).; Good programming practices should be used, the code should be clear, well documented and unit tested. Furthermore, relevant examples should be provided for novice users (i.e. biologists who are just learning to program).

Difficulty and needed skills: The student should ideally have an very good knowledge of JavaScript and basic knowledge of bioinformatics/biology.; Experience using GitHub for collaboration would be a big plus.

Mentors: Bruno Vieira (@bmpvieira) <mail@bmpvieira.com>, Yannick Wurm (@yannick__)

BioPerl

Mailing lists
IRC: #bioperl on Freenode
Information for new developers
Source code browser for bioperl-live (the main BioPerl code base), and all BioPerl sub-projects
Priority list of things that need work, as another source for student-conceived project ideas

NGS-friendly BioPerl code

Rationale: BioPerl is known to be slow re: any data sets, but particularly when dealing with very large data (e.g. anything related to NGS analysis. Can we make it better? Where should we focus our efforts?

Approach

Under the supervision of their mentor(s), the GSoC student will:

Benchmark bottlenecks that lead to loss in performance for NGS analyses
Refactor old classes or develop new optimized code for NGS analysis

Challenges: This can be a self-contained project, but will require a lot of discussion on what areas to focus on.

Difficulty and needed skills

easy to hard, depending on student's familiarity with the tools to be used. Student will need:

excellent Perl programming skills, including familiarity with NGS datasets
knowledge of modern Perl practices.

Mentors: Chris Fields, others?

Convert BioPerl-DB to use DBIx::Class

Rationale: Bioperl-db (the BioPerl bindings to BioSQL) in essence constitute a self-made ORM, invented at a time when DBIx::Class didn't exist yet. As such, it has some advantages (if you are willing to count overly clever features to be counted in this category), but arguably many more disadvantages, chief among them being the unsustainably small (you could also say non-existent) developer community supporting it, and the fact that DBIx::Class now has existed for years, and is fairly mature. So, rewriting Bioperl-db with a DBIx::Class (or another well-supported generic ORM) would stand to make a considerable impact on our ability to further develop Bioperl's relational storage capabilities, as well as BioSQL itself.

Approach

Under the supervision of their mentor(s), the GSoC student will:

Start working on conversion of BioPerl-DB classes to using DBIx::Class
write additional tests and improve documentation as needed

Challenges: BioPerl-DB is self-contained; this may require looking at the BioSQL schema and determining whether there are specific areas that need the most focus.

Difficulty and needed skills

easy to hard, depending on student's familiarity with the tools to be used. Student will need:

excellent Perl programming skills, including familiarity with:
- DBIx::Class

Mentors: Hilmar Lapp, others?

Major BioPerl Reorganization (Part II)

Rationale: The initial run at this project had some success, but more work needs to be done. The final goal of this project is to find and break out as many well-defined subsections of BioPerl as possible, releasing them to CPAN along the way.

Approach

Under the supervision of their mentor(s), the GSoC student will:

break current thousand-module monolithic distributions into smaller, more manageable pieces
improve characterization of dependencies
improve build and testing systems for new distributions
write additional tests and improve documentation as needed for the reorganization

Challenges: BioPerl contains nearly 2000 modules, with very complex relationships between them.

Difficulty and needed skills

easy to hard, depending on student's familiarity with the tools to be used. Student will need:

excellent Perl programming skills, including familiarity with:
- testing (prove, TAP::Harness)
- module authoring (Module::Build,Dist::Zilla,PAUSE)
good knowledge of command-line text-processing tools like ack, grep, and Perl one-liners.
version control systems (BioPerl uses git).

Mentors: Chris Fields, others?

Perl Run Wrappers for External Programs in a Flash

Rationale: BioPerl has a long tradition of providing wrapper objects for running external programs and parsing their output, mainly through the distribution called bioperl-run. Wrappers make it relatively easy to process data in highly customizable pipelines with the benefits of BioPerl objects and I/O. They also help to standardize the interfaces to typically idiosyncratic open-source utilities, reducing the burden on the developer. With new bioinformatics tools being released almost daily, however, it can be difficult for the BioPerl regulars to maintain a stable of run wrappers for the latest and greatest tools. Even harder is making the wrapper interfaces themselves conform to a standard API that users can count on.

Possible approaches

Integrate Galaxy's tool configuration file format in a pluggable way for developing a generic wrapper application.
Improve/tighten/extend the Bio::Tools::Run::WrapperBase and Bio::Tools::Run::WrapperBase::CommandExts system for very general run wrappers, making them work robustly with the new Bio::Tools::WrapperMaker module currently under development. The goal will be to get these modules ready for release into the trunk.
Are there any shortcomings to current schemes, such as Galaxy's or EMBOSS's acd format, that could be addressed with a newer schema?

See HOWTO:Wrappers and the above module documentation for more details.

Difficulty and needed skills: Medium. The student should understand or be willing to work hard at understanding BioPerl object-oriented style. Some familiarity with XML and XML Schema will help in getting up to speed. An interest in playing with new open-source bioinformatics tools, especially those for managing next-generation sequence assembly, would also be valuable.

Mentors: Chris Fields

Lightweight BioPerl modules

Rationale: Many current BioPerl classes are implemented in a greedy or heavy way, where all information is pulled into memory as objects. For instance, the current Bio::Seq implementation is the primary bottleneck for sequence parsing speed and can take up a ton of memory, particularly with whole-genome information and next-generation sequencing information. Storing the data in memory in a simple data structure and generating the objects lazily could help with speed. Alternatively, storing the data in a persistent manner would also help with memory issues, with the obvious trade-off for speed but having the nice side-benefit of consistent and possibly persistent ways of handling data.

Approach: Implement a Bio::Seq/Bio::PrimarySeq class (or other commonly-used BioPerl classes) that can deal with very large datasets in a memory-efficient manner. Implement at least one corresponding parser that can either parse records lazily (akin to an XML pull parser) or create lightweight objects. These could be considered two projects but they are interrelated (lightweight objects could have many different backends, including lazy parsing), so development should proceed with this in mind.

Difficulty and needed skills: medium to hard. Student should have an excellent command of Perl and data structures, experience with persistent storage mechanisms (such as a SQL-based RDBMS, CouchDB, etc), and some familiarity with parsing methodologies.

Prior art: Jason Stajich has started a SQLite-based lightweight Bio::Tree::Tree implementation on a GitHub branch at the recent GMOD Evolutionary Biology Hackathon at NESCent in Fall 2010.

Mentors: Chris Fields

BioPerl 2.0 and beyond

Rationale: Design or reimplement BioPerl classes without API constraint, using Modern Perl tools or Perl 6.

Approach: Most BioPerl code is over 6 years old and doesn't take advantage of Modern Perl tools, such as new methods available in Perl 5.10 and 5.12, Moose/MooseX, DBIx::Class, Catalyst, and more. Furthermore, a viable Perl6 implementation, Rakudo, is currently available. This gives us an enormous opportunity to redesign fundamental aspects of BioPerl without the necessity for development hindered by a requirement for backwards compatibility.

Two projects, Biome (Moose-based BioPerl) and BioPerl6 (Perl 6 BioPerl) have already started but are in a very early stage. One could participate in:

IO implementations for object iteration, or Perl6 grammars for common formats
Redesign of common BioPerl classes
etc.

This is an area ripe for new student project ideas. The more focused the better! Discussion is a must, either via IRC or email.

Difficulty: Project-dependent

Mentors: Chris Fields, Rob Buels

Bio::Assembly

Rationale: Although progress was made in the 2010 project "Alignment Subsystem Refactoring", continued refinement of AssemblyIO is still needed.

Approach: SAM or ACE files once imported should have similar handles and/or methods.

Difficulty and needed skills: Medium. Proficiency in Perl, familiarity with assembly tools.

Mentors: To be determined.

Semantic Web Support

Rationale: There are great development opportunities in information discovery for bioinformatics using semantic web, specially thinking in the implementation of SPARQL queries for a "discoverable bio-cloud".

Approach: Previous efforts can be adopted and extended, such as resulting code from BioHackathon 3 and the code provided by Expasy. Using the modules of the Semantic Web with Perl community, built around RDF::Trine low-level API. There are two main areas to explore:

Parsers and converters from and to RDF, including IO modules for GenBank, EMBL, several XML specifications, et cetera.
Storage and retrieval of information using SPARQL.

Difficulty and needed skills: Medium. Familiarity with SeqIO modules and Perl itself. The student should also be familiar with RDF format and the RDF triples concept for Semantic Web.

Mentors: To be determined. Kjetil Kjernsmo can help mentor students wishing to explore the RDF::Trine direction.

BioJava and JSBML

BioJava developer mailing list
JSBML developer mailing list
BioJava modules as another source for student-conceived project ideas
Source code for biojava-live (the main BioJava code base) and all BioJava sub-projects

For GSoC 2014, BioJava is partnering with the Systems Biology Markup Language (SMBL) team to bring enhancements to JSBML, the standard Java implementation of SBML, and bring SBML features to other Java-based systems biology software. See the SMBL website for more ideas from the SBML team.

Students working on these projects will interact with both the BioJava and JSBML communities, which overlap. Most development will happen on the JSBML codebase, although BioJava is used as a supporting library for some components.

Add support for Schema-based validation of SBML

Rationale: SBML files need to be validated carefully to ensure that they conform to the specification. Currently, the most complete implementation of SBML validation is embodied in libSBML, although the rules of SBML validity are defined in the SBML specification documents. It is possible to validate SBML from JSBML using either the Online SBML Validator or a Java package we provide for calling libSBML locally (i.e., without a network connection) but we want to move toward capturing all of the SBML's validity rules in schema languages.

Approach: Capture all of the SBML's validity rules in schema languages such as RELAX NG and Schematron, then have both libSBML and JSBML (and any other SBML-using system) use schema validation engines instead of hardcoded validation. This will be especially important as more SBML Level 3 packages become implemented. We have already made great strides in defining RELAX NG schemas for SBML Level 3, but we need to work on providing the hooks in JSBML to using those schemas for validating SBML files.

Languages and skills: Java, XML, RELAX NG, Schematron, SBML

Mentors: Sarah Keating, Andreas Dräger

Redesign the implementation of mathematical formulas in JSBML

Rationale: JSBML uses the concept of abstract syntax trees to work with mathematical expressions. At the moment, all different kinds of formulas are implemented in one complex class.

Approach: This project should implement a math package for JSBML, in which all different kinds of tree nodes that can occur in formulas (e.g., real numbers or algebraic symbols such as 'plus' or 'minus') would be represented with an own, specialized class. In this way, the handling of formulas would be much more straightforward and even more efficient.

Difficulty and skills: Medium; proficient in Java

Mentors: Andreas Dräger, Alex Thomas, Sarah Keating

Implement support for the SBML Multistate/Multicomponent Species package

Rationale: One of the many packages for SBML Level 3 is Multistate and multicomponent species. This packages define constructs for models and modelers to represent biochemical species that have internal structure or state properties. These may involve molecules that have multiple potential states, such as a protein that may be covalently modified, and molecules that combine to form heterogeneous complexes located among multiple compartments.

Approach: The JSBML team has already started implementation of the multi package, but more needs to be done.

Languages and skills: Java, some exposure to biochemistry

Mentors: Nicolas Rodriguez, Nicolas Le Novère

Improve the plugin interface for CellDesigner

Rationale: One of the most frequently used programs in computational systems biology is CellDesigner. JSBML provides an interface that facilitates the development of plugins for this program. This interface has recently been revised and improved.

Approach: Test cases and plugins for CellDesigner are to be implemented in order to make use of it and ensure its correct behavior. It is, for instance, possible to use CellDesigner's complex canvas user interface to create or manipulate biochemical networks and to conduct numerical computation.

Languages and skills: Java, some basic understanding of visualization algorithms

Mentors: Andreas Dräger

BioPython

Indexing & Lazy-loading Sequence Parsers

Rationale: Bio.SeqIO's indexing offers parsing on demand access to any sequence in a large file (or collection of files on disk) as a biopython:SeqRecord object. This works well when you have many small to medium sized sequences/genomes. However, this is not ideal for large genomes or chromosomes where only a sub-region may be needed. A lazy-loading parser would delay reading the record until requested. For example, if region record[3000:4000] is requested, then only those 1000 bases need to be loaded from disk into memory, plus any features in that region. This is how Biopython's biopython:BioSQL interface works. Tools like tabix and samtools have demonstrated efficient co-ordinate indexing which could be useful here.; Aside from being used via an index for random access, lazy-loading parsers could be used when iterating over a file as well. This can potentially offer speed ups for tasks where only a fraction of the data is used. For example, if calculating the GC content of a collection of genomes from GenBank, using Bio.SeqIO.parse(...) would currently needlessly load and parse all the annotation and features. A lazy-parser would only parse the sequence information.
Approach & Goals: Useful features include:

Internal indexing of multiple file formats, including FASTA and richly annotated sequence formats like GenBank/EMBL and GTF/GFF/GFF3.
Full compatibility with existing SeqIO parsers which load everything into memory as a `SeqRecord` object.

Difficulty and needed skills: Hard. Familiarity with the Biopython's existing sequence parsing essential. Understanding of indexing large files will be vital.
Possible Mentors: Wibowo Arindrarto, Peter Cock, others welcome

Interactive GenomeDiagram Module

Rationale: The GenomeDiagram genome/comparative genomics visualisation module currently produces static images (bitmap format), or images with relatively limited interactive capability such as click-throughs (.pdf, .svg). This is fine for its original intent of producing publication-quality graphics, but interactivity such as dynamic formatting, data selection, and box-outs would greatly enhance the value of the existing visualisations, and enable new uses.
Approach & Goals: The Bokeh interactive visualisation library uses the standalone BokehJS backend for in-browser visualisation. Targeting BokehJS with GenomeDiagram ought to be possible.
Difficulty and needed skills: Looks tricky to me. Introduction of BokehJS brings dependencies that may not be desirable for Biopython. BokehJS is in Coffeescript - the Python interface is not well documented. Familiarity with Javascript would be very useful.
Possible Mentors: Leighton Pritchard though I'd be learning as much as the student, so others very welcome.

BioRuby

An ultra-fast scalable RESTful API to query large numbers of genomic variations

Rationale: VCF files are the typical output of genome resequencing projects (http://www.1000genomes.org/node/101). They store the information on all the mutations and variations (SNPs and InDels) that are found by comparing the outputs of a NGS platform with a reference genome. These files are not incredibly large (a typical uncompressed VCF file is few gigabytes) but they are full with information on millions of positions in the genome where mutations are found. Large resequencing projects can produce hundreds or thousands of these files, one for each sample sequenced.; Existing tools (such as VCFTools or BCFTools) offer a convenient way to access these files and extract or convert the information present, but are limited in functionalities and speed when more complex queries need to be performed on these data. With existing tools it is very complicated, if not impossibile, to retrive information when working on many VCF files and samples together to compare, for instance, the variations found in 100 samples and extract all the mutations that are present in 50 samples but are not present in the other 50 and so on.

Approach: The project should develop a RESTful API to address the issues described in the rationale and to allow users to manipulate and compare genomics variation information for hundreds of samples. A database engine will be required to store the information and to support the data mining. Unstructured database engines such as noSQL databases or key-values stores can all be valid alternatives to combine high-speed with data flexibility. The decision on the best database engine to be used will be discussed between the student and the mentors and within the OpenBio community. Given the high amount of information that will need to be processed by such an application, scalable and fast languages such as JVM-based languages like Scala or JRuby will be a good choice. The project should also take care of the deploy of such an API, by creating a Ruby gem or a JAR that users can install and use right away with their datasets.

Difficulty and needed skills: The project has an average difficulty and it is aimed at talented students who wants to develop a fast API to address these problems.
The project requires: Knowledge of advanced programming languages. Some experience and knowledge of databases and data mining will help managing the information of VCF files.

Mentors: Francesco Strozzi, Raoul J.P. Bonnal

BioHaskell

Optimizing transalign, a novel, very sensitive alignment method

Rationale: A method and implementation for more sensitive pairwise alignments was recently developed and published (paper is here, and a copy here). The method appears to be the best of its type -- if nothing else, check the SCOP benchmark -- although it’s difficult to construct a good test case using more complex methods (training sets for HMMs and whatnot). The current implementation is in Haskell, and although it works correctly, it is a bit slow, and more problematic, it consumes too much memory (so going multi-threaded, although pretty easy, won’t be of any help).

Approach

The goal is to make this into a more practical tool by reducing resource requirements. A prospective student would either:

Optimize the Haskell program, primarily to reduce memory footprint, and secondarily to make use of multi-CPU systems.
Reimplement the algorithm (or parts of it) in a different language, and achieving the same as above.

Advantages of 1:

Already have a working program, and the type system makes it easy to refactor without introducing errors.
Haskell supports lots of good multi-threading programming models (like STM)
The author of the method, Ketil, knows Haskell pretty well and will mentor.

Disadvantages:

Haskell has some good debugging tools, but they tend to work really poorly for large memory (i.e. it takes a long time to generate profiles)
Needs somebody with a bit (or a lot) of experience optimizing Haskell, and good knowledge of high-perf libraries (like vector)

Advantages of 2:

Easier to get a student with adequate skills.
More predictable performance models in other languages.
Easier to compile and install for many users.

Disadvantages:

Ideally, should know enough Haskell to read and understand the code.
Likely needs a co-mentor with knowledge of the language in question.

Difficulty and skills needed: Medium to hard; Haskell proficiency and some knowledge of algorithm development

Mentor: Ketil Malde (ketil@malde.org)

Biocaml

Mailing List

Bioinformatics Js_of_ocaml Visualization Toolkit

Rationale: OCaml is a strong statically typed functional programming language. Usually one does not consider such languages for front-end development, but the Js_of_ocaml compiler is causing OCaml to be more widely used for building websites. Js_of_ocaml compiles OCaml code to pure Javascript and the generated Javascript has very good performance. On the other hand, bioinformatics data analysis needs to be conducted by a broader range of users, which requires more elegant user interfaces with high quality data visualization.

Approach: Write an OCaml library that can be used to visualize large data sets efficiently and interactively in the browser. The library should be smart enough to work on the client side when possible, but make server side calls when necessary. You may want to use Eliom for this purpose. You can connect to parsers and data structures available in Biocaml as needed. As demonstration of success, it should be possible to create genome visualizations like that of the UCSC genome browser and protein interaction networks like that of Cytoscape.

Difficulty and needed skills

This project is for intermediate to advanced programmers. You will need to be already familiar with OCaml (or closely related languages like F# and Haskell) and have a basic understanding of Javascript and client/server programming.

Mentor: Ashish Agarwal <agarwal1975@gmail.com>

Retrieved from "https://www.open-bio.org/w/index.php?title=Google_Summer_of_Code_2014&oldid=3742"

@@ Line 4: / Line 4: @@
 == 2014 Student Projects ==
+=== Sarah Berkemer ===
+Mentored by Christian Höner zu Siederdissen and Ketil Malde (BioHaskell)
+* Project: "High-performance Transalign algorithm in Haskell"
+* Blog: http://biohaskell.org/GSoC_blog
+* Source code:  https://github.com/bsarah/transalign
+=== Loris Cro ===
+Mentored by Francesco Strozzi and Raoul Bonnal (BioRuby)
+* Project: "An ultra-fast scalable RESTful API to query large numbers of VCF datapoints"
+* Blog: http://kappaloris.github.io/GSoC-2014-OBF/
+* Source code: https://github.com/kappaloris/GSoC-2014-OBF
+=== Victor Kofia ===
+Mentored by Sarah Keating and Alex Thomas (JSBML)
+* Project: "Redesign the implementation of mathematical formulas in JSBML"
+* Blog: http://kofiav.blogspot.ca/
+* Source code: http://svn.code.sf.net/p/jsbml/code/trunk/core/src/org/sbml/jsbml/math/
 === Evan Parker ===
@@ Line 9: / Line 33: @@
 Mentored by Wibowo Arindrarto and Peter Cock (BioPython)
-* Blog: [http://evanaparker.com/ Biopython Addition of a Lazy Loading Sequence Parser to Biopython’s SeqIO Package]
+* Project: "Addition of a lazy-loading sequence parser to Biopython’s SeqIO package"
+* Blog: http://evanaparker.com/
 * Source code: https://github.com/eparker05/biopython/tree/lazy-load
-=== Sarah Berkemer ===
+=== Ibrahim Vazirabad ===
+Mentored by Andreas Dräger and Alex Thomas (JSBML)
-:[http://biohaskell.org/GSoC_blog/First_Week High-performance Transalign algorithm in Haskell]
+* Project: "Improving the Plug-in interface for CellDesigner"
-:mentored by Christian Höner zu Siederdissen and Ketil Malde (BioHaskell)
+* Blog: http://jsbmlcelldesigner2014.blogspot.com/
+* Source code: http://sourceforge.net/projects/jsbml/
 === Leandro Watanabe ===
-:[http://lhwatanabe.blogspot.com Dynamic Modeling of Cellular Populations via arrays in JSBML]
+Mentored by Nicolas Rodriguez and Chris Myers (JSBML)
-:mentored by Nicolas Rodriguez and Chris Myers (JSBML)
+* Project: "Dynamic Modeling of Cellular Populations via arrays in JSBML"
+* Blog: http://lhwatanabe.blogspot.com
+* Source code: http://sourceforge.net/projects/jsbml/ or http://svn.code.sf.net/p/jsbml/code/trunk/extensions/arrays/
+== 2014 Projects Idea ==
+The details of each of our project ideas are listed below, including potential mentors. Interested mentors and students should subscribe to the OBF/GSoC [http://lists.open-bio.org/mailman/listinfo/gsoc mailing list] and announce their interest.
+See the main OBF [[Google Summer of Code]] page for more information about the GSoC program and additional ways to get in touch with us.
+===Cross-project ideas===
+OBF is an umbrella organization which represents many different programming languages used in [http://en.wikipedia.org/wiki/Bioinformatics bioinformatics]. In addition to working with each of the "Bio*" projects (listed below), this year we are also accepting a category of "cross-project" ideas that cover multiple programming languages or projects. These collaborative ideas are broadly defined and can be thought of as "unfinished" &mdash; interested students should adapt the ideas to their own strengths and goals, and are responsible for the quality of the final proposed idea in their application.
+'''Feel free to propose your own entirely new idea'''. You can also draw ideas from [http://gmod.org/wiki/GSoC#2014_Project_Ideas Genome Informatics (GMOD)] and the [http://informatics.nescent.org/wiki/Phyloinformatics_Summer_of_Code_2014 National Evolutionary Synthesis Center (NESCent)].
+==== Language APIs for the Systems Biology Markup Language (SBML) through the JVM ====
+; Rationale
+: The standard Java implementation of SBML, [http://sbml.org/GSoC2014 JSBML], is used as a parser for various Java-based systems biology applications. This fulfills one niche, but the versatility of the JVM can be utilized to employ JSBML as a parser for systems biology applications that are written in other languages. Also, JSBML undergoes an active community effort to be up-to-date with current SBML standards.
+; Approach
+: This project will aim to present language APIs for languages that may want to employ the SBML structure without building a parser from scratch. Matlab, Mathematica, and Python APIs will be the focus for this project.
+; Languages and skill
+: Java, optional: Matlab, Python, (other language)
+; Mentors
+: Andreas Dräger, Alex Thomas
+==== [http://gmod.org/wiki/GSoC#WormBase:_data_visualization WormBase: data visualization] ====
+;Rationale
+: [http://www.wormbase.org WormBase] is a central data repository supporting the nematode research community. There are several areas of improvement for data visualization on the website, including some key points raised by the WormBase community.
+;Approach
+: Here are a couple requests we've received from the community, but we are open to other ideas:
+:* Create a chromosome map tool - allow users to input and visualize the position of genetic loci. (See [https://github.com/WormBase/website/issues/1103 community request #1103])
+:* Create a central dogma view to tie together our gene/protein/sequence pages. (See [https://bitbucket.org/tharris/wormbase/issue/557/add-central-dogma-nav-to-overview community request #557])
+: The website's back-end is written in Perl, using some BioPerl as well as custom code. If you do significant work on the back-end, this could lead to or involve BioPerl improvements.
+;Languages and skills
+: Front-end: Javascript, HTML5, JS graphical library of your choice (e.g. D3). Back-end: some Perl, including BioPerl.
+;Mentor(s)
+: Abigail Cabunoc <abigail.cabunoc@oicr.on.ca>, others welcome
+==== Improve SegAnnDB interactive genomic segmentation web app ====
+;Rationale
+:[http://bioviz.rocq.inria.fr SegAnnDB] is an open-source web site for interactive genomic segmentation of DNA copy number profiles, [http://bioinformatics.oxfordjournals.org/content/early/2014/02/03/bioinformatics.btu072.short published in Bioinformatics]. It combines previous work on data visualization, computer vision, and machine learning into a web site that uses annotated regions to build a user-specific segmentation model. YouTube videos explain how it works: [https://www.youtube.com/watch?v=BuB5RNASHjU basic annotation], [https://www.youtube.com/watch?v=al0kk1JWsr0 annotating and exporting high-density profiles]. The goal of this project is to add features to SegAnnDB.
+;Approach
+:The ideal student project would propose to
+* Add social features for sharing annotations. SegAnnDB currently lets a user login using Mozilla Persona and then add user-specific annotations. These annotations are currently only accessible to the user that creates them, but it would be nice to be able to share them with others. For example, Alice annotates some data then types the email address of her friend Bob into a web form. Bob then receives an email with a web link where he can view Alice's annoatations.
+* Add adminstrative features. SegAnnDB uses BerkeleyDB, which is very fast but makes updating and deleting profiles a bit tricky. I have started writing a view (plotter.views.delete_profiles) for profile deletion along with some database support (plotter.db.Profile.delete).
+* Add unit tests using [http://docs.pylonsproject.org/en/latest/community/testing.html Pyramid recommendations], for example when a profile is processed (plotter.db.Profile.process) test for presence of objects in the database, and then when the profile is deleted, check for deletion of relvant objects and files (PNG scatterplots, probes.bedGraph.gz data).
+* Add support for browsers that do not render large PNG images. SegAnnDB uses very large PNGs to efficiently visualize DNA copy number profiles, but these are not supported by all browsers and [http://sugiyama-www.cs.titech.ac.jp/~toby/images/ you can test your browser's support on this web page].
+* Attempt integration with Galaxy, possibly as a [https://wiki.galaxyproject.org/VisualizationsRegistry Visualization Plugin].
+* Integration of [https://r-forge.r-project.org/scm/viewvc.php/python/?root=segannot SegAnnot and PrunedDP extension modules] into BioPython.
+;Required skills
+:JavaScript and Python. Experience with [http://d3js.org/ D3] and [http://www.pylonsproject.org/projects/pyramid/about Pyramid] web framework a plus.
+;Code
+:SegAnnDB is implemented as a Pyramid web app with a D3/JavaScript interface. Download the source code with
+ <nowiki>svn checkout svn://scm.gforge.inria.fr/svnroot/breakpoints/webapp/pyramid SegAnnDB</nowiki>
+and then check 00_INSTALL.sh for installation instructions, and email me if anything is unclear.
+;Mentors: Toby Dylan Hocking tdhock5@gmail.com plus anyone else with experience with D3/Pyramid is welcome to help!
+==== Integrate basic biological data analysis capabilities in fastR (Java/R) ====
+;Rationale
+:R is a free software programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Despite this, R is hard to mantain and evolve, lacks portability features and is characterized by low performance. The FastR project (http://www.oracle.com/technetwork/java/jvmls2013vitek-2013524.pdf) aims to rethink how to implement R, by leveraging well tested technologies to build a high performance VM. FastR is an implementation of the R programming language in Java using AST interpretation and specialisation for improved performance. The implementation has been extended to support JIT compilation, leading to performance improvements.
+The fastR project has been presented to the useR! conference 2013, and it is now being actively developed at https://bitbucket.org/allr/fastr.
+;Approach
+:The goal of this project would be to carry on the FastR development by expanding the set of supported R data types, possibly integrating some basic functionalities from Bioconductor which will be extended in the future. The strength of this approach lies in the possibility for Java users to access the vast R ecosystem, and to expand further the inter-operability of R packages and workflows by using other languages built on Java, like JRuby and Jython.
+;Required skills
+:Moderate technical difficulty, an interest in statistical problem solving is a plus but not essential. This project requires mid/advanced Java programming skills
+;Reference to other projects: BioJava, R, BioRuby, Jruby, Jython
+;Mentors: Jan Vitek, Alberto Arrigoni [arrigonialberto86@gmail.com]
+==== BioInterchange: Convert and Exchange Biological File Formants using RESTful web service ====
+;  Rationale
+: [http://www.biointerchange.org/index.html BioInterchange] Interchange data using the Resource Description Framework (RDF) and let BioInterchange automagically create RDF triples from your TSV, XML, GFF3, GVF, Newick and other files common in Bioinformatics. BioInterchange helps you transform your data sets into linked data for sharing and data integration via command line, web-service, or API. BioInterchange was conceived and designed during NBDC/DBCLS's [http://2012.biohackathon.org/ BioHackathon 2012]. Architecture and RDF serialization implementations were provided by Joachim Baran, Geraint Duck provided JSON and XML deserialization implementations and contributed to architecture decisions, guidance on ontology use and applications were given by Kevin B. Cohen and Michel Dumontier, where Michel brought forward and extended the Semanticscience Integrated Ontology (SIO). Jin-Dong Kim helped to define ontology relationships for RDFizing DBCLS' PubAnnotation category annotations. The main idea is to have a central service with can be used as a validator and as interchange service for different languages.
+;  Approach
+:The project will identify the most common and used file formants for all the currently used language under OBF and will design a RESTful API and will project an implementation for all the supported languages. BioInterchange was developed with Ruby but the scope of the project is to have an agnostic system which let use implement a converter using the best language for that functionality. It expected to have a high traffic for the service so an appropriate refactoring or reimplementation using parallel techniques or languages devoted to parallel programming would be possible.
+;  Difficulty and needed skills
+:The project is mid / high difficulty, aimed at talented students. Previous knowledge of Ruby or other scripting language is preferred and flexibility in learning other languages is required.
+;  The project requires
+:Knowledge of advanced programming languages and meta-programming and some concept in parallelizing and web services design.
+;  Mentors
+:  Raoul J.P. Bonnal, Francesco Strozzi, Toshiaki Katayama, Joachim Baran
+==== bionode - A Node.js JavaScript library for client and server side bioinformatics ====
+; Rationale
+:During the development of a web front-end or software with a web-interface, it often becomes clear that a component implemented for the server-side must be performed on the client side, or vice-versa. This generally requires functionalities implemented in one language to be reimplemented in another. Recent developments in Javascript make this unnecessary. Indeed, JavaScript has become a “write once run everywhere” full stack programming language that can be executed in the browser as well as on the server (thanks to Node.js). The web-development community is enthusiastically embracing this technology. In the last year, Node.js modules increased [http://modulecounts.com 2.6-fold to a total of 61656]. The average growth rate is 175 modules per day, which means that it will quickly surpass Java (71906) and Ruby (71446) having already surpassed all the other languages (e.g., Perl: 29097; Python: 40455).
+:Consistent with this, web applications for visualizing or interacting with biological data increasingly rely on javascript (e.g. [http://jbrowse.org jBrowse], [http://apollo.berkeleybop.org WebApollo], [http://biodalliance.org Biodalliance], etc). Surprisingly however, no generic javascript bioinformatics library yet exists, leading independent projects to redundantly implement basic functionality. Here, we propose to develop bionode a core javascript library for handling and analyzing bioinformatics data - mirroring the core functionality of established bio* libraries (e.g. bioperl, bioruby).
+:This will be done in close collaboration with the developers of [http://rostlab.org/services/biojs/gsoc.html BioJS] (who build reusable components for visualization of biological data) and of [http://apollo.berkeleybop.org WebApollo]/[http://github.com/yeban/afra Afra] (a gene prediction curation software) for immediate short-term applications. Importantly, we expect the long-term interest for a bioinformatics javascript library to be huge.
+; Approach
+:We already seeded this project: [http://bionode.io bionode] is a Node.js module with some bioinformatics methods that work on the client and server side.
+:Under the supervision of the mentor(s), the GSoC student will add more methods/algorithms to the bionode project. The student could take inspiration from other similar libraries in other programming languages, such as [http://bioperl.org/wiki/Main_Page bioperl] or [http://bioruby.open-bio.org/wiki bioruby]. Methods for input/output and wrangling basic data types should be given a higher priority at the beginning of this projects. For example, implementing some of the functions of [http://biopython.org/wiki/SeqIO biopython.SeqIO] could be a project. Another source for inspiration could come from Massive Open Online Course, such as the [http://www.coursera.org/course/bioinformatics “Bioinformatics Algorithms”] from the University of California, San Diego.
+;Challenges
+:Methods provided should be able to run on a server environment (via Node.js) or browser environment (via webpack or browserify). Thus, the module pattern used should be the one used by Node.js (CommonJS).
+:Good programming practices should be used, the code should be clear, well documented and unit tested. Furthermore, relevant examples should be provided for novice users (i.e. biologists who are just learning to program).
+;Difficulty and needed skills
+:The student should ideally have an very good knowledge of JavaScript and basic knowledge of bioinformatics/biology.
+:Experience using GitHub for collaboration would be a big plus.
+; Mentors
+: Bruno Vieira ([http://twitter.com/bmpvieira @bmpvieira]) <[mailto:mail@bmpvieira.com mail@bmpvieira.com]>, Yannick Wurm ([http://twitter.com/yannick__ @yannick__])
+===[http://bioperl.org/wiki/Google_Summer_of_Code BioPerl]===
+[[Image:BioPerl_logo_tiny.jpg|right|link=bp:Google Summer of Code]]
+* [[bp:Mailing_lists|Mailing lists]]
+* IRC: <code>#bioperl</code> on [http://freenode.net Freenode]
+* [[bp:Becoming_a_developer|Information for new developers]]
+* Source code browser for [https://github.com/bioperl/bioperl-live bioperl-live] (the main BioPerl code base), and [https://github.com/bioperl all BioPerl sub-projects]
+* [[bp:Project_priority_list|Priority list]] of things that need work, as another source for student-conceived project ideas
+==== [http://bioperl.org/wiki/Google_Summer_of_Code#NGS-friendly_BioPerl_code NGS-friendly BioPerl code] ====
+; Rationale : BioPerl is known to be slow re: any data sets, but particularly when dealing with very large data (e.g. anything related to NGS analysis.  Can we make it better?  Where should we focus our efforts?
+; Approach : Under the supervision of their mentor(s), the GSoC student will:
+:* Benchmark bottlenecks that lead to loss in performance for NGS analyses
+:* Refactor old classes or develop new optimized code for NGS analysis
+; Challenges : This can be a self-contained project, but will require a lot of discussion on what areas to focus on.
+; Difficulty and needed skills : easy to hard, depending on student's familiarity with the tools to be used.  Student will need:
+:* excellent Perl programming skills, including familiarity with NGS datasets
+;* knowledge of modern Perl practices.
+; Mentors : Chris Fields, others?
+==== [http://bioperl.org/wiki/Google_Summer_of_Code#Convert_BioPerl-DB_to_DBIx::Class Convert BioPerl-DB to use DBIx::Class] ====
+; Rationale : Bioperl-db (the BioPerl bindings to BioSQL) in essence constitute a self-made ORM, invented at a time when DBIx::Class didn't exist yet. As such, it has some advantages (if you are willing to count overly clever features to be counted in this category), but arguably many more disadvantages, chief among them being the unsustainably small (you could also say non-existent) developer community supporting it, and the fact that DBIx::Class now has existed for years, and is fairly mature.  So, rewriting Bioperl-db with a DBIx::Class (or another well-supported generic ORM) would stand to make a considerable impact on our ability to further develop Bioperl's relational storage capabilities, as well as BioSQL itself.
+; Approach : Under the supervision of their mentor(s), the GSoC student will:
+:* Start working on conversion of BioPerl-DB classes to using DBIx::Class
+:* write additional tests and improve documentation as needed
+; Challenges : BioPerl-DB is self-contained; this may require looking at the BioSQL schema and determining whether there are specific areas that need the most focus.
+; Difficulty and needed skills : easy to hard, depending on student's familiarity with the tools to be used.  Student will need:
+:* excellent Perl programming skills, including familiarity with:
+:** DBIx::Class
+; Mentors : Hilmar Lapp, others?
+==== [http://bioperl.org/wiki/Google_Summer_of_Code#Major_BioPerl_Reorganization.2C_part_2 Major BioPerl Reorganization (Part II)] ====
+; Rationale : The initial run at this project [http://bioperl.org/wiki/Google_Summer_of_Code#Major_BioPerl_reorganization had some success], but more work needs to be done.  The final goal of this project is to find and break out as many well-defined subsections of BioPerl as possible, releasing them to CPAN along the way.
+; Approach : Under the supervision of their mentor(s), the GSoC student will:
+:* break current thousand-module monolithic distributions into smaller, more manageable pieces
+:* improve characterization of dependencies
+:* improve build and testing systems for new distributions
+:* write additional tests and improve documentation as needed for the reorganization
+; Challenges : BioPerl contains nearly 2000 modules, with very complex relationships between them.
+; Difficulty and needed skills : easy to hard, depending on student's familiarity with the tools to be used.  Student will need:
+:* excellent Perl programming skills, including familiarity with:
+:** testing (<tt>prove</tt>, <tt>TAP::Harness</tt>)
+:** module authoring (<tt>Module::Build</tt>,<tt>Dist::Zilla</tt>,PAUSE)
+:* good knowledge of command-line text-processing tools like <tt>ack</tt>, <tt>grep</tt>, and Perl one-liners.
+:* version control systems (BioPerl uses [http://git-scm.com git]).
-=== Loris Cro ===
+; Mentors : Chris Fields, others?
+==== [http://bioperl.org/wiki/Google_Summer_of_Code#Perl_Run_Wrappers_for_External_Programs_in_a_Flash Perl Run Wrappers for External Programs in a Flash] ====
+; Rationale : BioPerl has a long tradition of providing wrapper objects for running external programs and parsing their output, mainly through the distribution called [http://bioperl.org/wiki/Bioperl-run bioperl-run]. Wrappers make it relatively easy to process data in highly customizable pipelines with the benefits of BioPerl objects and I/O. They also help to standardize the interfaces to typically idiosyncratic open-source utilities, reducing the burden on the developer. With new bioinformatics tools being released almost daily, however, it can be difficult for the BioPerl regulars to maintain a stable of run wrappers for the latest and greatest tools. Even harder is making the wrapper interfaces themselves conform to a standard API that users can count on.
+; Possible approaches:
+# Integrate Galaxy's tool configuration file format in a pluggable way for developing a generic wrapper application.
+# Improve/tighten/extend the <tt>Bio::Tools::Run::WrapperBase</tt> and <tt>Bio::Tools::Run::WrapperBase::CommandExts</tt> system for very general run wrappers, making them work robustly with the new <tt>Bio::Tools::WrapperMaker</tt> module currently under development. The goal will be to get these modules ready for release into the trunk.
+# Are there any shortcomings to current schemes, such as Galaxy's or EMBOSS's acd format, that could be addressed with a newer schema?
+See [http://bioperl.org/wiki/HOWTO:Wrappers HOWTO:Wrappers] and the above module documentation for more details.
+; Difficulty and needed skills : Medium. The student should understand or be willing to work hard at understanding BioPerl object-oriented style. Some familiarity with [[wp:XML|XML]] and [[wp:XML Schema|XML Schema]] will help in getting up to speed. An interest in playing with new open-source bioinformatics tools, especially those for managing next-generation sequence assembly, would also be valuable.
+; Mentors : [[User:Cjfields | Chris Fields]]
+==== [http://bioperl.org/wiki/Google_Summer_of_Code#Lightweight.2FLazy_BioPerl_Classes Lightweight BioPerl modules] ====
+; Rationale : Many current BioPerl classes are implemented in a greedy or heavy way, where all information is pulled into memory as objects.  For instance, the current <tt>Bio::Seq</tt> implementation is the primary bottleneck for sequence parsing speed and can take up a ton of memory, particularly with whole-genome information and next-generation sequencing information.  Storing the data in memory in a simple data structure and generating the objects lazily could help with speed.  Alternatively, storing the data in a persistent manner would also help with memory issues, with the obvious trade-off for speed but having the nice side-benefit of consistent and possibly persistent ways of handling data.
+; Approach : Implement a <tt>Bio::Seq</tt>/<tt>Bio::PrimarySeq</tt> class (or other commonly-used BioPerl classes) that can deal with very large datasets in a memory-efficient manner.  Implement at least one corresponding parser that can either parse records lazily (akin to an XML pull parser) or create lightweight objects.  These could be considered two projects but they are interrelated (lightweight objects could have many different backends, including lazy parsing), so development should proceed with this in mind.
+; Difficulty and needed skills : medium to hard.  Student should have an excellent command of Perl and data structures, experience with persistent storage mechanisms (such as a SQL-based RDBMS, CouchDB, etc), and some familiarity with parsing methodologies.
+; Prior art : Jason Stajich has started a SQLite-based lightweight <tt>Bio::Tree::Tree</tt> implementation on [http://github.com/bioperl/bioperl-live/tree/topic/tree_dbsqlite_memoryfix a GitHub branch] at the recent GMOD Evolutionary Biology Hackathon at NESCent in Fall 2010.
+; Mentors : Chris Fields
+==== [http://bioperl.org/wiki/Google_Summer_of_Code#BioPerl_2.0_.28and_beyond.29 BioPerl 2.0 and beyond] ====
+; Rationale : Design or reimplement BioPerl classes without API constraint, using Modern Perl tools or Perl 6.
+; Approach : Most BioPerl code is over 6 years old and doesn't take advantage of Modern Perl tools, such as new methods available in Perl 5.10 and 5.12, Moose/MooseX, DBIx::Class, Catalyst, and more.  Furthermore, a viable Perl6 implementation, Rakudo, is currently available.  This gives us an enormous opportunity to redesign fundamental aspects of BioPerl without the necessity for development hindered by a requirement for backwards compatibility.
+Two projects, Biome (Moose-based BioPerl) and BioPerl6 (Perl 6 BioPerl) have already started but are in a very early stage.  One could participate in:
+* IO implementations for object iteration, or Perl6 grammars for common formats
+* Redesign of common BioPerl classes
+* etc.
+This is an area ripe for new student project ideas.  The more focused the better!  Discussion is a must, either via IRC or email.
+; Difficulty : Project-dependent
+; Mentors : Chris Fields, Rob Buels
+==== [http://bioperl.org/wiki/Google_Summer_of_Code#Bio::Assembly Bio::Assembly] ====
+; Rationale
+: Although progress was made in the 2010 project "Alignment Subsystem Refactoring", continued refinement of AssemblyIO is still needed.
+; Approach
+: SAM or ACE files once imported should have similar handles and/or methods.
+; Difficulty and needed skills
+: Medium. Proficiency in Perl, familiarity with assembly tools.
+; Mentors
+: To be determined.
+==== [http://bioperl.org/wiki/Google_Summer_of_Code#Semantic_Web_Support Semantic Web Support] ====
+; Rationale : There are great development opportunities in information discovery for bioinformatics using semantic web, specially thinking in the implementation of SPARQL queries for a "discoverable bio-cloud".
+; Approach : Previous efforts can be adopted and extended, such as resulting code from [http://hackathon3.dbcls.jp/ BioHackathon 3] and the code provided by [http://dev.isb-sib.ch/projects/expasy-rdf/ Expasy].  Using the modules of the [http://www.perlrdf.org Semantic Web with Perl community], built around [https://metacpan.org/module/RDF::Trine RDF::Trine low-level API]. There are two main areas to explore:
+# Parsers and converters from and to RDF, including IO modules for GenBank, EMBL, several XML specifications, et cetera.
+# Storage and retrieval of information using SPARQL.
+; Difficulty and needed skills : Medium.  Familiarity with SeqIO modules and Perl itself.  The student should also be familiar with RDF format and the RDF triples concept for Semantic Web.
+; Mentors : To be determined. [http://bioperl.org/wiki/User:Kjetilk Kjetil Kjernsmo] can help mentor students wishing to explore the RDF::Trine direction.
+===[http://biojava.org/wiki/Google_Summer_of_Code BioJava] and JSBML===
+[[Image:Biojava_logo_tiny.jpg|right|link=http://biojava.org/wiki/Google_Summer_of_Code]]
+* [http://lists.open-bio.org/mailman/listinfo/biojava-dev BioJava developer mailing list]
+* [https://groups.google.com/forum/#!forum/jsbml-development JSBML developer mailing list]
+* [http://biojava.org/wiki/BioJava:Modules BioJava modules] as another source for student-conceived project ideas
+* Source code for [http://code.open-bio.org/svnweb/index.cgi/biojava/browse/biojava-live/trunk biojava-live] (the main BioJava code base) and [http://code.open-bio.org/svnweb/index.cgi/biojava/ all BioJava sub-projects]
+For GSoC 2014, BioJava is partnering with the Systems Biology Markup Language (SMBL) team to bring enhancements to JSBML, the standard Java implementation of SBML, and bring SBML features to other Java-based systems biology software. See [http://sbml.org/GSoC2014 the SMBL website] for more ideas from the SBML team.
+Students working on these projects will interact with both the BioJava and JSBML communities, which overlap. Most development will happen on the JSBML codebase, although BioJava is used as a supporting library for some components.
+==== Add support for Schema-based validation of SBML ====
+; Rationale
+: SBML files need to be validated carefully to ensure that they conform to the specification. Currently, the most complete implementation of SBML validation is embodied in libSBML, although the rules of SBML validity are defined in the SBML specification documents. It is possible to validate SBML from JSBML using either the Online SBML Validator or a Java package we provide for calling libSBML locally (i.e., without a network connection) but we want to move toward capturing all of the SBML's validity rules in schema languages.
+; Approach
+: Capture all of the SBML's validity rules in schema languages such as RELAX NG and Schematron, then have both libSBML and JSBML (and any other SBML-using system) use schema validation engines instead of hardcoded validation. This will be especially important as more SBML Level 3 packages become implemented. We have already made great strides in defining RELAX NG schemas for SBML Level 3, but we need to work on providing the hooks in JSBML to using those schemas for validating SBML files.
+; Languages and skills
+: Java, XML, RELAX NG, Schematron, SBML
+; Mentors
+: Sarah Keating, Andreas Dräger
+==== Redesign the implementation of mathematical formulas in JSBML ====
+; Rationale
+: JSBML uses the concept of abstract syntax trees to work with mathematical expressions. At the moment, all different kinds of formulas are implemented in one complex class.
+; Approach
+: This project should implement a math package for JSBML, in which all different kinds of tree nodes that can occur in formulas (e.g., real numbers or algebraic symbols such as 'plus' or 'minus') would be represented with an own, specialized class. In this way, the handling of formulas would be much more straightforward and even more efficient.
+; Difficulty and skills
+: Medium; proficient in Java
+; Mentors
+: Andreas Dräger, Alex Thomas, Sarah Keating
+==== Implement support for the SBML Multistate/Multicomponent Species package ====
+; Rationale
+: One of the many packages for SBML Level 3 is Multistate and multicomponent species. This packages define constructs for models and modelers to represent biochemical species that have internal structure or state properties. These may involve molecules that have multiple potential states, such as a protein that may be covalently modified, and molecules that combine to form heterogeneous complexes located among multiple compartments.
+; Approach
+: The JSBML team has already started implementation of the <tt>multi</tt> package, but more needs to be done.
+; Languages and skills
+: Java, some exposure to biochemistry
+; Mentors
+: Nicolas Rodriguez, Nicolas Le Novère
+==== Improve the plugin interface for CellDesigner ====
+; Rationale
+: One of the most frequently used programs in computational systems biology is [http://www.celldesigner.org/ CellDesigner]. JSBML provides an interface that facilitates the development of plugins for this program. This interface has recently been revised and improved.
+; Approach
+: Test cases and plugins for CellDesigner are to be implemented in order to make use of it and ensure its correct behavior. It is, for instance, possible to use CellDesigner's complex canvas user interface to create or manipulate biochemical networks and to conduct numerical computation.
+; Languages and skills
+: Java, some basic understanding of visualization algorithms
+; Mentors
+: Andreas Dräger
+===[[biopython:Google Summer of Code |BioPython]]===
+[[Image:Biopython_logo_tiny.png|right|link=biopython:Google Summer of Code]]
+* [[biopython:Mailing lists|Mailing lists]]
+* [[biopython:Contributing|Information for contributors]]
+* [[biopython:SourceCode| Source Code]]
+==== [http://biopython.org/wiki/Google_Summer_of_Code#Indexing_.26_Lazy-loading_Sequence_Parsers Indexing & Lazy-loading Sequence Parsers] ====
+;  Rationale
+: [[biopython:SeqIO|Bio.SeqIO]]'s indexing offers parsing on demand access to any sequence in a large file (or collection of files on disk) as a [[biopython:SeqRecord]] object. This works well when you have many small to medium sized sequences/genomes. However, this is not ideal for large genomes or chromosomes where only a sub-region may be needed. A lazy-loading parser would delay reading the record until requested. For example, if region ''record[3000:4000]'' is requested, then only those 1000 bases need to be loaded from disk into memory, plus any features in that region. This is how Biopython's [[biopython:BioSQL]] interface works. Tools like tabix and samtools have demonstrated efficient co-ordinate indexing which could be useful here.
+: Aside from being used via an index for random access, lazy-loading parsers could be used when iterating over a file as well. This can ''potentially'' offer speed ups for tasks where only a fraction of the data is used. For example, if calculating the GC content of a collection of genomes from GenBank, using Bio.SeqIO.parse(...) would currently needlessly load and parse all the annotation and features. A lazy-parser would only parse the sequence information.
+;  Approach & Goals
+: Useful features include:
+* Internal indexing of multiple file formats, including FASTA and richly annotated sequence formats like GenBank/EMBL and GTF/GFF/GFF3.
+* Full compatibility with existing SeqIO parsers which load everything into memory as a `SeqRecord` object.
+;  Difficulty and needed skills
+: Hard. Familiarity with the Biopython's existing sequence parsing essential. Understanding of indexing large files will be vital.
+;  Possible Mentors
+:  [https://github.com/bow Wibowo Arindrarto], [https://github.com/peterjc/ Peter Cock], others welcome
+==== [http://biopython.org/wiki/Google_Summer_of_Code#Interactive_GenomeDiagram_Module Interactive GenomeDiagram Module] ====
+;  Rationale
+:  The GenomeDiagram genome/comparative genomics visualisation module currently produces static images (bitmap format), or images with relatively limited interactive capability such as click-throughs (.pdf, .svg). This is fine for its original intent of producing publication-quality graphics, but interactivity such as dynamic formatting, data selection, and box-outs would greatly enhance the value of the existing visualisations, and enable new uses.
+;  Approach & Goals
+: The [http://bokeh.pydata.org/ Bokeh] interactive visualisation library uses the standalone [http://bokeh.pydata.org/dev_guide.html BokehJS] backend for in-browser visualisation. Targeting BokehJS with GenomeDiagram ought to be possible.
+;  Difficulty and needed skills
+:  Looks tricky to me. Introduction of BokehJS brings dependencies that may not be desirable for Biopython. BokehJS is in Coffeescript - the Python interface is not well documented. Familiarity with Javascript would be very useful.
+;  Possible Mentors
+:  [https://github.com/widdowquinn Leighton Pritchard] though I'd be learning as much as the student, so others very welcome.
+===[http://bioruby.open-bio.org/wiki/Google_Summer_of_Code BioRuby]===
+[[Image:BioRuby_logo_tiny.png|right|link=http://bioruby.org]]
+* [http://lists.open-bio.org/mailman/listinfo/bioruby Developers mailing list]
+* [http://github.com/bioruby/bioruby/tree/master Source code]
+* IRC: <code>#bioruby</code> on [http://freenode.net Freenode]
+==== [http://bioruby.open-bio.org/wiki/Google_Summer_of_Code#An_ultra-fast_scalable_RESTful_API_to_query_large_numbers_of_genomic_variations An ultra-fast scalable RESTful API to query large numbers of genomic variations] ====
+;  Rationale
+:VCF files are the typical output of genome resequencing projects (http://www.1000genomes.org/node/101). They store the information on all the mutations and variations ([http://en.wikipedia.org/wiki/Single-nucleotide_polymorphism SNPs] and [http://en.wikipedia.org/wiki/Indel InDels]) that are found by comparing the outputs of a [http://en.wikipedia.org/wiki/DNA_sequencing#Next-generation_methods NGS] platform with a reference genome. These files are not incredibly large (a typical uncompressed VCF file is few gigabytes) but they are full with information on millions of positions in the genome where mutations are found. Large resequencing projects can produce hundreds or thousands of these files, one for each sample sequenced.
+:Existing tools (such as [http://vcftools.sourceforge.net VCFTools] or [http://samtools.sourceforge.net/samtools.shtml#4 BCFTools]) offer a convenient way to access these files and extract or convert the information present, but are limited in functionalities and speed when more complex queries need to be performed on these data. With existing tools it is very complicated, if not impossibile, to retrive information when working on many VCF files and samples together to compare, for instance, the variations found in 100 samples and extract all the mutations that are present in 50 samples but are not present in the other 50 and so on.
+;  Approach
+:The project should develop a RESTful API to address the issues described in the rationale and to allow users to manipulate and compare genomics variation information for hundreds of samples. A database engine will be required to store the information and to support the data mining. Unstructured database engines such as noSQL databases or key-values stores can all be valid alternatives to combine high-speed with data flexibility. The decision on the best database engine to be used will be discussed between the student and the mentors and within the OpenBio community. Given the high amount of information that will need to be processed by such an application, scalable and fast languages such as JVM-based languages like Scala or JRuby will be a good choice. The project should also take care of the deploy of such an API, by creating a Ruby gem or a JAR that users can install and use right away with their datasets.
+;  Difficulty and needed skills
+:The project has an average difficulty and it is aimed at talented students who wants to develop a fast API to address these problems.
+;  The project requires
+:Knowledge of advanced programming languages. Some experience and knowledge of databases and data mining will help managing the information of VCF files.
+;  Mentors
+:  Francesco Strozzi, Raoul J.P. Bonnal
+===[http://biohaskell.org/Google_Summer_of_Code BioHaskell]===
+:* [http://biohaskell.org/ Project website]
+:* [http://hackage.haskell.org/packages/#cat:Bioinformatics Bioinformatics section on HackageDB]
+==== [http://biohaskell.org/Google_Summer_of_Code#Optimizing_transalign Optimizing transalign, a novel, very sensitive alignment method] ====
+; Rationale
+: A method and implementation for more sensitive pairwise alignments was recently developed and published (paper is [http://dx.plos.org/10.1371/journal.pone.0054422 here], and a copy [http://malde.org/~ketil/publications/Malde2013a.pdf here]).  The method appears to be the best of its type -- if nothing else, check the SCOP benchmark -- although it’s difficult to construct a good test case using more complex methods (training sets for HMMs and whatnot). The current implementation is in Haskell, and although it works correctly, it is a bit slow, and more problematic, it consumes too much memory (so going multi-threaded, although pretty easy, won’t be of any help).
+; Approach
+:The goal is to make this into a more practical tool by reducing resource requirements. A prospective student would either:
+# Optimize the Haskell program, primarily to reduce memory footprint, and secondarily to make use of multi-CPU systems.
+# Reimplement the algorithm (or parts of it) in a different language, and achieving the same as above.
+:Advantages of 1:
+:* Already have a working program, and the type system makes it easy to refactor without introducing errors.
+:* Haskell supports lots of good multi-threading programming models (like STM)
+:* The author of the method, Ketil, knows Haskell pretty well and will mentor.
+:Disadvantages:
+:* Haskell has some good debugging tools, but they tend to work really poorly for large memory (i.e. it takes a long time to generate profiles)
+:* Needs somebody with a bit (or a lot) of experience optimizing Haskell, and good knowledge of high-perf libraries (like vector)
+:Advantages of 2:
+:* Easier to get a student with adequate skills.
+:* More predictable performance models in other languages.
+:* Easier to compile and install for many users.
+:Disadvantages:
+:* Ideally, should know enough Haskell to read and understand the code.
+:* Likely needs a co-mentor with knowledge of the language in question.
+; Difficulty and skills needed
+: Medium to hard; Haskell proficiency and some knowledge of algorithm development
+; Mentor:
+: Ketil Malde (ketil@malde.org)
+===[http://biocaml.org Biocaml]===
+:* [https://groups.google.com/d/forum/biocaml Mailing List]
+==== Bioinformatics Js_of_ocaml Visualization Toolkit ====
+; Rationale
+: [http://ocaml.org OCaml] is a strong statically typed functional programming language. Usually one does not consider such languages for front-end development, but the Js_of_ocaml compiler is causing OCaml to be more widely used for building websites. [http://ocsigen.org/js_of_ocaml/ Js_of_ocaml] compiles OCaml code to pure Javascript and the generated Javascript has [http://ocsigen.org/js_of_ocaml/manual/performances very good performance]. On the other hand, bioinformatics data analysis needs to be conducted by a broader range of users, which requires more elegant user interfaces with high quality data visualization.
+; Approach
+: Write an OCaml library that can be used to visualize large data sets efficiently and interactively in the browser. The library should be smart enough to work on the client side when possible, but make server side calls when necessary. You may want to use [http://ocsigen.org/eliom/ Eliom] for this purpose. You can connect to parsers and data structures available in [http://biocaml.org Biocaml] as needed. As demonstration of success, it should be possible to create genome visualizations like that of the [http://genome.ucsc.edu/cgi-bin/hgGateway UCSC genome browser] and protein interaction networks like that of [http://www.cytoscape.org/ Cytoscape].
-:[http://kappaloris.github.io/GSoC-2014-OBF/ An ultra-fast scalable RESTful API to query large numbers of VCF datapoints]
+; Difficulty and needed skills
-:mentored by Francesco Strozzi and Raoul Bonnal (BioRuby)
+This project is for intermediate to advanced programmers. You will need to be already familiar with OCaml (or closely related languages like F# and Haskell) and have a basic understanding of Javascript and client/server programming.
-=== Victor Kofia ===
+; Mentor
+: [http://ashishagarwal.org Ashish Agarwal] <agarwal1975@gmail.com>
-:Redesign the implementation of mathematical formulas in JSBML
-:mentored by Alex Thomas and Sarah Keating (JSBML)
-=== Ibrahim Vazirabad ===
+<!--
+==BioSQL==
-:Improving the Plug-in interface for CellDesigner
+[[Image:BioSQL_logo.png|160px|right|link=biosql:Main Page]]
-:mentored by Andreas Dräger (JSBML)
+; [[biosql:Main Page|BioSQL]] :
+:* [[biosql:Main Page|Project website]]
+:* Current [http://biosql.org/wiki/Enhancement_Requests enhancement requests] as another source for student-conceived project ideas
+:* [http://biosql.org/mailman/listinfo/biosql-l developers mailing list]
+:* [http://code.open-bio.org/svnweb/index.cgi/biosql/browse/biosql-schema/trunk source code]
+:* No IRC channel at present
+-->

Difference between revisions of "Google Summer of Code 2014"

Latest revision as of 11:56, 16 February 2015

Contents

GSoC 2014

2014 Student Projects

Sarah Berkemer

Loris Cro

Victor Kofia

Evan Parker

Ibrahim Vazirabad

Leandro Watanabe

2014 Projects Idea

Cross-project ideas

Language APIs for the Systems Biology Markup Language (SBML) through the JVM

Improve SegAnnDB interactive genomic segmentation web app

Integrate basic biological data analysis capabilities in fastR (Java/R)

BioInterchange: Convert and Exchange Biological File Formants using RESTful web service

bionode - A Node.js JavaScript library for client and server side bioinformatics

BioJava and JSBML

Add support for Schema-based validation of SBML

Redesign the implementation of mathematical formulas in JSBML

Implement support for the SBML Multistate/Multicomponent Species package

Improve the plugin interface for CellDesigner

Bioinformatics Js_of_ocaml Visualization Toolkit

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