Recent metagenomics studies of environmental samples suggested that microbial communities are much more diverse than previously reported, and deep sequencing will significantly increase the estimate of total species diversity. Massively parallel pyrosequencing technology enables ultra-deep sequencing of complex microbial populations rapidly and inexpensively. However, computational methods for analyzing large collections of 16S ribosomal sequences are limited. We proposed a new algorithm, referred to as ESPRIT, which addresses several computational issues with prior methods. We developed two versions of ESPRIT, one for personal computers (PCs) and one for computer clusters (CCs). The PC version is used for small- and medium-scale data sets and can process several tens of thousands of sequences within a few minutes, while the CC version is for large-scale problems and is able to analyze several hundreds of thousands of reads within one day. Large-scale experiments are presented that clearly demonstrate the effectiveness of the newly proposed algorithm. The source code and user guide are freely available at http://www.biotech.ufl.edu/people/sun/esprit.html.

ObjectiveTo analyze serum immunoglobulin G (IgG) antibodies to major isoforms of myelin oligodendrocyte glycoprotein (MOG-alpha 1-3 and beta 1-3) in patients with inflammatory demyelinating diseases.MethodsRetrospective case-control study using 378 serum samples from patients with multiple sclerosis (MS), patients with non-MS demyelinating disease, and healthy controls with MOG alpha-1-IgG positive (n = 202) or negative serostatus (n = 176). Samples were analyzed for their reactivity to human, mouse, and rat MOG isoforms with and without mutations in the extracellular MOG Ig domain (MOG-ecIgD), soluble MOG-ecIgD, and myelin from multiple species using live cell-based, tissue immunofluorescence assays and ELISA.ResultsThe strongest IgG reactivities were directed against the longest MOG isoforms alpha-1 (the currently used standard test for MOG-IgG) and beta-1, whereas the other isoforms were less frequently recognized. Using principal component analysis, we identified 3 different binding patterns associated with non-MS disease: (1) isolated reactivity to MOG-alpha-1/beta-1 (n = 73), (2) binding to MOG-alpha-1/beta-1 and at least one other alpha, but no beta isoform (n = 64), and (3) reactivity to all 6 MOG isoforms (n = 65). The remaining samples were negative (n = 176) for MOG-IgG. These MOG isoform binding patterns were associated with a non-MS demyelinating disease, but there were no differences in clinical phenotypes or disease course. The 3 MOG isoform patterns had distinct immunologic characteristics such as differential binding to soluble MOG-ecIgD, sensitivity to MOG mutations, and binding to human MOG in ELISA.ConclusionsThe novel finding of differential MOG isoform binding patterns could inform future studies on the refinement of MOG-IgG assays and the pathophysiologic role of MOG-IgG.