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      Tn-seq; high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms

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      Nature methods

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          Abstract

          Biological pathways are structured in complex networks of interacting genes. Solving the architecture of such networks may provide valuable information, such as how microorganisms cause disease. Here we present a method (Tn-seq) for accurately determining quantitative genetic interactions on a genome-wide scale in microorganisms. Tn-seq is based on the assembly of a saturated Mariner transposon insertion library. After library selection, changes in frequency of each insertion mutant are determined by sequencing of the flanking regions en masse. These changes are used to calculate each mutant’s fitness. Fitness was determined for each gene of the gram-positive bacterium Streptococcus pneumoniae, a causative agent of pneumonia and meningitis. A genome-wide screen for genetic interactions identified both alleviating and aggravating interactions that could be further divided into seven distinct categories. Due to the wide activity of the Mariner transposon, Tn-seq has the potential to contribute to the exploration of complex pathways across many different species.

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          Most cited references29

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Genes required for mycobacterial growth defined by high density mutagenesis.

            Despite over a century of research, tuberculosis remains a leading cause of infectious death worldwide. Faced with increasing rates of drug resistance, the identification of genes that are required for the growth of this organism should provide new targets for the design of antimycobacterial agents. Here, we describe the use of transposon site hybridization (TraSH) to comprehensively identify the genes required by the causative agent, Mycobacterium tuberculosis, for optimal growth. These genes include those that can be assigned to essential pathways as well as many of unknown function. The genes important for the growth of M. tuberculosis are largely conserved in the degenerate genome of the leprosy bacillus, Mycobacterium leprae, indicating that non-essential functions have been selectively lost since this bacterium diverged from other mycobacteria. In contrast, a surprisingly high proportion of these genes lack identifiable orthologues in other bacteria, suggesting that the minimal gene set required for survival varies greatly between organisms with different evolutionary histories.
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              Is Open Access

              STRING 7—recent developments in the integration and prediction of protein interactions

              Information on protein–protein interactions is still mostly limited to a small number of model organisms, and originates from a wide variety of experimental and computational techniques. The database and online resource STRING generalizes access to protein interaction data, by integrating known and predicted interactions from a variety of sources. The underlying infrastructure includes a consistent body of completely sequenced genomes and exhaustive orthology classifications, based on which interaction evidence is transferred between organisms. Although primarily developed for protein interaction analysis, the resource has also been successfully applied to comparative genomics, phylogenetics and network studies, which are all facilitated by programmatic access to the database backend and the availability of compact download files. As of release 7, STRING has almost doubled to 373 distinct organisms, and contains more than 1.5 million proteins for which associations have been pre-computed. Novel features include AJAX-based web-navigation, inclusion of additional resources such as BioGRID, and detailed protein domain annotation. STRING is available at
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nature methods
                1548-7091
                1548-7105
                24 September 2009
                20 September 2009
                October 2009
                20 October 2010
                : 6
                : 10
                : 767-772
                Affiliations
                Howard Hughes Medical Institute and Dept. of Molecular Biology & Microbiology, Tufts University School of Medicine, 136 Harrison Avenue, South Cove 510, Boston, MA 02111-1817, Ph: 617-636-2144, Fax: 617-636-2175, Andrew. Camilli@ 123456Tufts.edu
                Article
                hhmipa143284
                10.1038/nmeth.1377
                2957483
                19767758
                96fe6e69-111b-4874-ac06-9ab4acd186cf

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: Howard Hughes Medical Institute
                Award ID: ||HHMI_
                Categories
                Article

                Life sciences
                Life sciences

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