metagenomeSeq

Learning classifiers with ternary weights

Natural variation in SAR11 marine bacterioplankton genomes

Exome capture from saliva produces high quality

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In biological experiments, researchers often have information in the form of a graph that supplements observed numerical data. Incorporating the knowledge contained in these graphs into an analysis of the numerical data is an important and non trivial task. We look at the example of metagenomic

assemblies are typic-ally fragmented and also leave a large fraction of reads unassembled. Here, we present a method for reconstructing complete protein sequences directly from NGS metagenomic data. Our frame-work is based on a novel short peptide assembler (SPA) that assembles protein sequences from

Early marker-based metagenomic studies were performed without properly accounting for the effects of noise (sequencing errors, PCR single-base errors, and PCR chimeras). Denoising algorithms have been developed, but they were validated using data derived from mock communities, in which the true

assemblers for assembling reads from a single genome, there are no assemblers for assembling reads in metagenomic data without reference genome sequences. Moreover, the performances of these assemblers on metagenomic data are far from satisfactory, because of the existence of common regions in the genomes

This manuscript presents the most rigorous benchmark-ing of gene annotation algorithms for metagenomic datasets to date. We compare three different programs: GeneMark, MetaGeneAnnotator (MGA) and Orphelia. The compar-isons are based on their performances over simulated frag-ments from one hundred