CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers.

BackgroundThe problem of supervised DNA sequence classification arises in several fields of computational molecular biology. Although this problem has been extensively studied, it is still computationally challenging due to size of the datasets that modern sequencing technologies can produce.ResultsWe introduce CLARK a novel approach to classify metagenomic reads at the species or genus level with high accuracy and high speed. Extensive experimental results on various metagenomic samples show that the classification accuracy of CLARK is better or comparable to the best state-of-the-art tools and it is significantly faster than any of its competitors. In its fastest single-threaded mode CLARK classifies, with high accuracy, about 32 million metagenomic short reads per minute. CLARK can also classify BAC clones or transcripts to chromosome arms and centromeric regions.ConclusionsCLARK is a versatile, fast and accurate sequence classification method, especially useful for metagenomics and genomics applications. It is freely available at http://clark.cs.ucr.edu/

The Universal Language of International Securitization

This article introduces the reader to international securitization, first by explaining the concepts of securitization and then by examining securitization in a cross-border context

EFICAz²: enzyme function inference by a combined approach enhanced by machine learning

©2009 Arakaki et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/107doi:10.1186/1471-2105-10-107Background: We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. Results: We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz², exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz² and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz² generates considerably more unique assignments than KEGG. Conclusion: Performance benchmarks and the comparison with KEGG demonstrate that EFICAz² is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz² web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.htm

Modeling Endogenous Mobility in Earnings Determination

We evaluate the bias from endogenous job mobility in fixed-effects estimates of worker- and firm-specific earnings heterogeneity using longitudinally linked employer-employee data from the LEHD infrastructure file system of the U.S. Census Bureau. First, we propose two new residual diagnostic tests of the assumption that mobility is exogenous to unmodeled determinants of earnings. Both tests reject exogenous mobility. We relax the exogenous mobility assumptions by modeling the evolution of the matched data as an evolving bipartite graph using a Bayesian latent class framework. Our results suggest that endogenous mobility biases estimated firm effects toward zero. To assess validity, we match our estimates of the wage components to out-of-sample estimates of revenue per worker. The corrected estimates attribute much more of the variation in revenue per worker to variation in match quality and worker quality than the uncorrected estimates