Difference between revisions of "Google Summer of Code"

Revision as of 10:10, 17 February 2014

GSoC 2014

Introduction

Google Summer of Code (GSoC) is a student internship program for open-source projects. The program offers eligible student developers stipends to write code for open source projects over a period of 3 summer months ("flip bits, not burgers"). See the Google Summer of Code Main Site for general information about the Google Summer of Code program, how to apply, frequently asked general questions, and more.

The Google Summer of Code 2014 is ON! OBF is once again applying as a mentoring organization this year. Interested mentors and students should subscribe to the OBF/GSoC mailing list. Please announce yourself, so we know who you are! The details of each of our project ideas are listed below, including potential mentors.

Facts & Links

• GSoC 2014 time line
• For questions of eligibility, see the GSoC 2014 FAQ.
• There is also a Google group for posting GSoC questions (and receiving answers; note that you will need to sign up for the group) that relate to the program itself (and are not specific to our organization).
• Students receive a stipend from Google if accepted. See the GSoC 2014 FAQ for full documentation.
• Development is done entirely remotely and on-line; there is no requirement or expectation for either students or mentors to travel.

Why apply?

One of the most important features of the program is that students are paired with mentors, who are typically experienced developers from the project to which the student is contributing. The mentor guides the student to work productively within the community, and helps the student avoid obstacles and pitfalls. The program is global - students and mentors may be located anywhere where they have an internet connection (except for countries affected by US trade restrictions), and no travel is required. Thus, aside from the stipend and mentorship aspects, the student's experience in the internship closely mirrors normal work on distributed development projects. Effective work habits for distributed development are typically not taught as part of computer science curricula, yet are highly desired in the increasingly global and distributed software, IT, and biotechnology industries.

From the viewpoint of each open-source project, the program not only offers to pay students for contributing, but more importantly, offers an opportunity to recruit new developers who will hopefully go on to become regular, sustaining contributors.

Projects 2014

Cross-project ideas

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 requireed.

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

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

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

1. Integrate Galaxy's tool configuration file format in a pluggable way for developing a generic wrapper application.
2. 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.
3. 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:

1. Parsers and converters from and to RDF, including IO modules for GenBank, EMBL, several XML specifications, et cetera.
2. 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

• Mailing lists
• 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.

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

• Mailing lists
• Information for contributors
• Source Code

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

BioRuby

• Developers mailing list
• Source code
• IRC: #bioruby on Freenode

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

• Project website
• Bioinformatics section on HackageDB

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:

1. Optimize the Haskell program, primarily to reduce memory footprint, and secondarily to make use of multi-CPU systems.
2. 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)

OBF Projects Accepting Applicants

BioPerl 

• BioPerl GSoC Page - project ideas and mentors
• Project website
• 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
• Mailing lists
• IRC: #bioperl on Freenode

BioPython 

• BioPython GSoC Page - project ideas and mentors
• Project website
• Information for contributors
• Mailing lists
• Source Code
• No IRC channel at present

BioJava 

• BioJava GSoC Page - project ideas and mentors
• 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
• Mailing lists
• No IRC channel at present

BioRuby 

• BioRuby GSoC Page - project ideas and mentors
• Project website
• developers mailing list
• source code
• IRC: #bioruby on Freenode

BioSQL 

• Project website
• Current enhancement requests as another source for student-conceived project ideas
• developers mailing list
• source code
• No IRC channel at present

BioHaskell 

• Project website]
• Bioinformatics section on HackageDB

Guide for prospective GSoC students

Before you apply

• Proposals should extend one of affiliated toolkits, not start a new project.
• If you want to apply with your own idea, it's best to contact the OBF subproject you're interested in well before the application deadline, so we can work with you to find a mentor and solidify your project idea and application.
• Ask us questions on the subproject mailing lists about the project idea you have in mind.
• Write a project proposal draft, include a project plan (see below), and send it to a project mailing list for comments before submitting it.

Again, students are strongly encouraged to contact us as early as possible. Frequent and early communication is extremely valuable for putting together successful projects.

When you apply

When applying, (aside from the information requested by Google) please provide the following in your application material.

1. Your complete contact information, including full name, physical address, preferred email address, and telephone number, plus other pertinent contact information such as IRC handles, etc.
2. Why you are interested in the project you are proposing and are well-suited to undertake it.
3. A summary of your programming experience and skills.
4. Programs or projects you have previously authored or contributed to, in particular those available as open-source, including, if applicable, any past Summer of Code involvement.
5. A project plan for the project you are proposing, even if your proposed project is directly based on one of the proposed project ideas for member projects.
   • A project plan in principle divides up the whole project into a series of manageable milestones and time-lines that, when all accomplished, logically lead to the end goal(s) of the project. Put in another way, a project plan explains what you expect you will need to be doing, and what you expect you need to have accomplished, at which time, so that at the end you reach the goals of the project.
   • Do not take this part lightly. A compelling plan takes a significant amount of work. Empirically, applications with no or a hastily composed project plan have not been competitive, and a more thorough project plan can easily make an applicant out compete another with more advanced skills.
   • A good plan will require you to thoroughly think about the project itself and how one might want to go about the work.
   • We don't expect you to have all the experience, background, and knowledge to come up with the final, real work plan on your own at the time you apply. We do expect your plan to demonstrate, however, that you have made the effort and thoroughly dissected the goals into tasks and successive accomplishments that make sense.
   • We strongly recommend that you bounce your proposed project and your project plan draft off of us, using either the pertinent developers mailing list or the IRC channel(s). Through the project plan exercise you will inevitably discover that you are missing a lot of the pieces - we are there to help you fill those in as best as we can.
6. Any obligations, vacations, or plans for the summer that may require scheduling during the GSoC work period.
   • We expect the your GSoC project to be your primary focus over the summer. It should not be regarded as a part-time occupation.
   • If you feel that you can manage other work obligations concurrently with your Summer of Code project, make your case and support it with evidence.
   • Be honest and open. If it turns out later that you weren't clear about other obligations, at best (i.e., if your accomplishment record at that point is spotless) our trust in you will be severely degraded. Also, if you are accepted, discuss with your GSoC mentor before taking on additional obligations.
   • One of the most common reasons for students to struggle or fail is being overcommitted. Do not set yourself up for failure! GSoC summers should be fun and rewarding!

Student Progress Reports

To encourage community bonding and awareness of what the GSoC students are doing, from 2012 the OBF is being much clearer about our progress report expectations.

We would like every student to setup a blog for the GSoC project (or a category/tag on your existing blog) which you will use to summarize your progress every week, as well as longer posts at the half way evaluation, and at the end of the summer.

In addition, after publishing each blog post, we expect you to email the URL and the text of the blog (or if important images or formatting would be lost, at least a short summary) to the host project's mailing list(s) (check with your mentors if the project has more than one) AND the gsoc@open-bio.org mailing list.

You will be writing under your own name, but with a clear association with your mentors, the OBF and its projects, so please take this seriously and be professional. Remember this will become part of your online presence, and potentially looked at by future employers and colleagues.

Contact

Before applying, please read our documentation on information that students should know and guidelines we expect you to follow. We also require that you include certain information, listed below, under "When you apply."

Staff and org Admins

Organization administrator

Eric Talevich (eric.talevich@gmail.com)

Backup administrator

Raoul Bonnal (email) (IRC: helius | channels: #obf-soc, #bioruby, #gsoc ) (Skype: ilpuccio)

Google Plus

OBF Summer of Code on G+

Email

For prospective students, the first point of contact should be the mailing list of the OBF project you are interested in working with:

BioPerl

bioperl-l@lists.open-bio.org

BioPython

biopython@lists.open-bio.org

BioJava

biojava-l@lists.open-bio.org

BioRuby

bioruby@lists.open-bio.org

BioSQL

biosql-l@lists.open-bio.org

BioLib

biolib-dev@lists.open-bio.org

Also, it would be a good idea to CC the organization administrator (Eric Talevich, eric.talevich@gmail.com), so he can make sure that you are properly taken care of!

If you are not quite sure which project you would like to contribute to, you can email to the organization administrator for help. However, do not worry overly much about picking the right OBF project at the outset. If you are unsure, simply make your best guess, and other members of the email list will help you to find the best organization to suit your idea.

IRC - Internet Relay Chat

OBF IRC channels are maintained on freenode, connect your IRC client to chat.freenode.net.

Main OBF GSoC Channel

#obf-soc

BioPerl

#bioperl

BioRuby

#bioruby

Some mentors and developers can regularly be found on IRC, see the list of OBF projects below for information on which projects have a channel and the name of the channel. And/or join #obf-soc on Freenode. (If you do not have an IRC client installed, you might find the comparison on Wikipedia, the Google directory, or the IRC Reviews helpful. For Macs, X-Chat Aqua works pretty well. If you have never used IRC, try the IRC Primer at IRC Help, which also has links to lots of other material.)

Mentor Resources

• GSoC Mentoring Guide
• OBF Application Evaluation Guidelines

Previous Years

This section contains links to content related to OBF's participation in GSoC in previous years.

• 2012 - 5 student projects
• 2011 - 6 student projects
• 2010 - 6 student projects