• Monzoorul Haque Mohammed, Tarini Shankar Ghosh, Nitin Kumar Singh, and Sharmila S Mande.
• Bio-sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, Madhapur, Hyderabad, Andhra Pradesh, India.
• Bioinformatics. 2011 Jan 1; 27 (1): 22-30.

MotivationCompared with composition-based binning algorithms, the binning accuracy and specificity of alignment-based binning algorithms is significantly higher. However, being alignment-based, the latter class of algorithms require enormous amount of time and computing resources for binning huge metagenomic datasets. The motivation was to develop a binning approach that can analyze metagenomic datasets as rapidly as composition-based approaches, but nevertheless has the accuracy and specificity of alignment-based algorithms. This article describes a hybrid binning approach (SPHINX) that achieves high binning efficiency by utilizing the principles of both 'composition'- and 'alignment'-based binning algorithms.ResultsValidation results with simulated sequence datasets indicate that SPHINX is able to analyze metagenomic sequences as rapidly as composition-based algorithms. Furthermore, the binning efficiency (in terms of accuracy and specificity of assignments) of SPHINX is observed to be comparable with results obtained using alignment-based algorithms.AvailabilityA web server for the SPHINX algorithm is available at http://metagenomics.atc.tcs.com/SPHINX/.

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