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Google Summer of Code 2014 Ideas

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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

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

BioPerl logo tiny.jpg

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 
Mark Jensen, 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, Jason Stajich

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
A followup to the 2010 project "Alignment Subsystem Refactoring": Continued refinement of AssemblyIO.
Approach
SAM or ACE files once imported should have similar handles and/or methods.
Difficulty and needed skills
Medium to hard. Excellent command of Perl, familiarity with sequence alignment and alignment 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

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JVM support and improvements for the Systems Biology Markup Language

See Native and JVM-based support for the Systems Biology Markup Language (SMBL)


BioPython

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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

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

BioHaskell

Optimizing a novel, very sensitive alignment method