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Fractal MapReduce decomposition of sequence alignment

Jonas S Almeida1*, Alexander Grüneberg12, Wolfgang Maass23 and Susana Vinga45

Author Affiliations

1 Div Informatics, Dept Pathology, University of Alabama at Birmingham, USA

2 Research Center for Intelligent Media, Furtwangen University, Furtwangen, Germany

3 Information and Service Systems, Dept of Law and Economics, Saarland University, Germany

4 INESC-ID, Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento, Lisboa, Portugal

5 FCM-UNL Faculdade Ciências Médicas - Universidade Nova de Lisboa, Portugal

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Algorithms for Molecular Biology 2012, 7:12  doi:10.1186/1748-7188-7-12

Published: 2 May 2012

Abstract

Background

The dramatic fall in the cost of genomic sequencing, and the increasing convenience of distributed cloud computing resources, positions the MapReduce coding pattern as a cornerstone of scalable bioinformatics algorithm development. In some cases an algorithm will find a natural distribution via use of map functions to process vectorized components, followed by a reduce of aggregate intermediate results. However, for some data analysis procedures such as sequence analysis, a more fundamental reformulation may be required.

Results

In this report we describe a solution to sequence comparison that can be thoroughly decomposed into multiple rounds of map and reduce operations. The route taken makes use of iterated maps, a fractal analysis technique, that has been found to provide a "alignment-free" solution to sequence analysis and comparison. That is, a solution that does not require dynamic programming, relying on a numeric Chaos Game Representation (CGR) data structure. This claim is demonstrated in this report by calculating the length of the longest similar segment by inspecting only the USM coordinates of two analogous units: with no resort to dynamic programming.

Conclusions

The procedure described is an attempt at extreme decomposition and parallelization of sequence alignment in anticipation of a volume of genomic sequence data that cannot be met by current algorithmic frameworks. The solution found is delivered with a browser-based application (webApp), highlighting the browser's emergence as an environment for high performance distributed computing.

Availability

Public distribution of accompanying software library with open source and version control at http://usm.github.com webcite. Also available as a webApp through Google Chrome's WebStore http://chrome.google.com/webstore webcite: search with "usm".