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Bioinformatics Advance Access published online on May 21, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn237
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

The Sleipnir library for computational functional genomics

Curtis Huttenhower 1,2, Mark Schroeder 1, Maria D. Chikina 1,3 and Olga G. Troyanskaya 1,2,*

1Lewis-Sigler Institute for Integrative Genomics, Carl Icahn Laboratory, Princeton University, Princeton, NJ 08540
2Department of Computer Science or 3Molecular Biology, Princeton University, Princeton, NJ 08540

*To whom correspondence should be addressed. Olga G. Troyanskaya, E-mail: ogt{at}princeton.edu


  Abstract

Motivation: Biological data generation has accelerated to the point where hundreds or thousands of whole-genome datasets of various types are available for many model organisms. This wealth of data can lead to valuable biological insights when analyzed in an integrated manner, but the computational challenge of managing such large data collections is substantial. In order to mine these data efficiently, it is necessary to develop methods that use storage, memory, and processing resources carefully.

Results: The Sleipnir C++ library implements a variety of machine learning and data manipulation algorithms with a focus on heterogeneous data integration and efficiency for very large biological data collections. Sleipnir allows microarray processing, functional ontology mining, clustering, Bayesian learning and inference, and Support Vector Machine tasks to be performed for heterogeneous data on scales not previously practical. In addition to the library, which can easily be integrated into new computational systems, prebuilt tools are provided to perform a variety of common tasks. Many tools are multithreaded for parallelization in desktop or high-throughput computing environments, and most tasks can be performed in minutes for hundreds of datasets using a standard personal computer.

Availability: Source code (C++) and documentation are available at http://function.princeton.edu/sleipnir and compiled binaries are available from the authors on request.

Contact: ogt{at}princeton.edu

Associate Editor: Dr. Jonathan Wren

Received on April 16, 2008; revised on May 14, 2008; accepted on May 14, 2008

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