Phylogenetic Binning of Metagenomic Sequence Data

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DESCRIPTION (provided by applicant): Culture-independent metagenomic studies are essential for understanding our relationship with the organisms comprising the human microbiome, defining optimal microbial composition to maintain health, and devising selective treatment strategies to eliminate pathogens without harming beneficial species. To use metagenomic data effectively, raw DNA sequence data (reads) must be processed computationally (assembled) to obtain longer sequences (contigs). Existing software packages for this purpose are quite inefficient when presented with large, taxonomically diverse samples, resulting in considerable wastage of reads that cannot be assembled. Efforts to maximize assembly efficiency by relaxing stringency can lead to inappropriate joining of sequences from unrelated organisms (chimeric artifacts), compromising data accuracy and usefulness. Taxonomic binning of raw reads as a pre-filtering step is expected to improve metagenomic sequence assembly efficiency, reducing statistical noise due to sample complexity and allowing incorporation of raw reads into longer, more informative contigs without incurring chimeric artifacts. Benefits should be especially significant for less abundant species in complex mixtures. We have developed methods to quantify taxonomic binning program performance and assembly improvements in real metagenomic data sets, including reproducible calibration standards, to enable efficient parameter optimization for existing software and provide reliable benchmarks for future software development. Our specific aims are to 1) develop new computational methods for large-scale taxonomic classification of metagenomic sequence data, applicable to raw reads as well as assembled contigs; 2) develop software and protocols to use taxonomic data binning as a pre-treatment to increase efficiency of existing sequence assembly software; 3) benchmark performance enhancement for different assembly software programs using quantitative, statistical tests with both artificially created models and real-life metagenomic data sets of varying size and complexity; 4) make new computational methods and performance evaluation tools available to the general scientific community.