RESUMEN
Type VII Secretion System (T7SS) is one of the factors involved in virulence of Mycobacteriun tuberculosis H37Rv. Numerous research efforts have been made in the last decade towards characterizing the components of this secretion system. An extensive genome-wide analysis through compilation of isolated information is required to obtain a global view of diverse characteristics and pathogenicity-related aspects of this machinery. The present study suggests that differences in structural components (of T7SS) between Actinobacteria and Firmicutes, observed earlier in a few organisms, is indeed a global trend. A few hitherto uncharacterized T7SS-like clusters have been identified in the pathogenic bacteria Enterococcus faecalis, Saccharomonospora viridis, Streptococcus equi, Streptococcuss gordonii and Streptococcus sanguinis. Experimental verification of these clusters can shed lights on their role in bacterial pathogenesis. Similarly, verification of the identified variants of T7SS clusters consisting additional membrane components may help in unraveling new mechanism of protein translocation through T7SS. A database of various components of T7SS has been developed to facilitate easy access and interpretation of T7SS related data.
RESUMEN
Physical partitioning techniques are routinely employed (during sample preparation stage) for segregating the prokaryotic and eukaryotic fractions of metagenomic samples. In spite of these efforts, several metagenomic studies focusing on bacterial and archaeal populations have reported the presence of contaminating eukaryotic sequences inmetagenomic data sets. Contaminating sequences originate not only from genomes of micro-eukaryotic species but also from genomes of (higher) eukaryotic host cells. The latter scenario usually occurs in the case of host-associatedmetagenomes. Identification and removal of contaminating sequences is important, since these sequences not only impact estimates of microbial diversity but also affect the accuracy of several downstream analyses. Currently, the computational techniques used for identifying contaminating eukaryotic sequences, being alignment based, are slow, inefficient, and require huge computing resources. In this article, we present Eu-Detect, an alignment-free algorithm that can rapidly identify eukaryotic sequences contaminating metagenomic data sets. Validation results indicate that on a desktop with modest hardware specifications, the Eu-Detect algorithm is able to rapidly segregate DNA sequence fragments of prokaryotic and eukaryotic origin, with high sensitivity. A Web server for the Eu-Detect algorithm is available at http://metagenomics.atc.tcs.com/Eu-Detect/.