Dataset Information

SLING: a tool to search for linked genes in bacterial datasets.

ABSTRACT: Gene arrays and operons that encode functionally linked proteins form the most basic unit of transcriptional regulation in bacteria. Rules that govern the order and orientation of genes in these systems have been defined; however, these were based on a small set of genomes that may not be representative. The growing availability of large genomic datasets presents an opportunity to test these rules, to define the full range and diversity of these systems, and to understand their evolution. Here we present SLING, a tool to Search for LINked Genes by searching for a single functionally essential gene, along with its neighbours in a rule-defined proximity (https://github.com/ghoresh11/sling/wiki). Examining this subset of genes enables us to understand the basic diversity of these genetic systems in large datasets. We demonstrate the utility of SLING on a clinical collection of enteropathogenic Escherichia coli for two relevant operons: toxin antitoxin (TA) systems and RND efflux pumps. By examining the diversity of these systems, we gain insight on distinct classes of operons which present variable levels of prevalence and ability to be lost or gained. The importance of this analysis is not limited to TA systems and RND pumps, and can be expanded to understand the diversity of many other relevant gene arrays.

SUBMITTER: Horesh G

PROVIDER: S-EPMC6265476 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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SLING: a tool to search for linked genes in bacterial datasets.

Horesh Gal G Harms Alexander A Fino Cinzia C Parts Leopold L Gerdes Kenn K Heinz Eva E Thomson Nicholas Robert NR

Nucleic acids research 20181101 21

Gene arrays and operons that encode functionally linked proteins form the most basic unit of transcriptional regulation in bacteria. Rules that govern the order and orientation of genes in these systems have been defined; however, these were based on a small set of genomes that may not be representative. The growing availability of large genomic datasets presents an opportunity to test these rules, to define the full range and diversity of these systems, and to understand their evolution. Here w ...[more]

PMID: 30124998

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Project description:BackgroundChemical cross-linking combined with mass spectrometry (CX-MS) is a high-throughput approach to studying protein-protein interactions. The number of peptide-peptide combinations grows quadratically with respect to the number of proteins, resulting in a high computational complexity. Widely used methods including xQuest (Rinner et al., Nat Methods 5(4):315-8, 2008; Walzthoeni et al., Nat Methods 9(9):901-3, 2012), pLink (Yang et al., Nat Methods 9(9):904-6, 2012), ProteinProspector (Chu et al., Mol Cell Proteomics 9:25-31, 2010; Trnka et al., 13(2):420-34, 2014) and Kojak (Hoopmann et al., J Proteome Res 14(5):2190-198, 2015) avoid searching all peptide-peptide combinations by pre-selecting peptides with heuristic approaches. However, pre-selection procedures may cause missing findings. The most intuitive approach is searching all possible candidates. A tool that can exhaustively search a whole database without any heuristic pre-selection procedure is therefore desirable.ResultsWe have developed a cross-linked peptides identification tool named ECL. It can exhaustively search a whole database in a reasonable period of time without any heuristic pre-selection procedure. Tests showed that searching a database containing 5200 proteins took 7 h. ECL identified more non-redundant cross-linked peptides than xQuest, pLink, and ProteinProspector. Experiments showed that about 30 % of these additional identified peptides were not pre-selected by Kojak. We used protein crystal structures from the protein data bank to check the intra-protein cross-linked peptides. Most of the distances between cross-linking sites were smaller than 30 Å.ConclusionsTo the best of our knowledge, ECL is the first tool that can exhaustively search all candidates in cross-linked peptides identification. The experiments showed that ECL could identify more peptides than xQuest, pLink, and ProteinProspector. A further analysis indicated that some of the additional identified results were thanks to the exhaustive search.

Project description:BackgroundThe length of the huntingtin (HTT) CAG repeat is strongly correlated with both age at onset of Huntington's disease (HD) symptoms and age at death of HD patients. Dichotomous analysis comparing HD to controls is widely used to study the effects of HTT CAG repeat expansion. However, a potentially more powerful approach is a continuous analysis strategy that takes advantage of all of the different CAG lengths, to capture effects that are expected to be critical to HD pathogenesis.Methodology/principal findingsWe used continuous and dichotomous approaches to analyze microarray gene expression data from 107 human control and HD lymphoblastoid cell lines. Of all probes found to be significant in a continuous analysis by CAG length, only 21.4% were so identified by a dichotomous comparison of HD versus controls. Moreover, of probes significant by dichotomous analysis, only 33.2% were also significant in the continuous analysis. Simulations revealed that the dichotomous approach would require substantially more than 107 samples to either detect 80% of the CAG-length correlated changes revealed by continuous analysis or to reduce the rate of significant differences that are not CAG length-correlated to 20% (n = 133 or n = 206, respectively). Given the superior power of the continuous approach, we calculated the correlation structure between HTT CAG repeat lengths and gene expression levels and created a freely available searchable website, "HD CAGnome," that allows users to examine continuous relationships between HTT CAG and expression levels of ∼20,000 human genes.Conclusions/significanceOur results reveal limitations of dichotomous approaches compared to the power of continuous analysis to study a disease where human genotype-phenotype relationships strongly support a role for a continuum of CAG length-dependent changes. The compendium of HTT CAG length-gene expression level relationships found at the HD CAGnome now provides convenient routes for discovery of candidates influenced by the HD mutation.

Project description:BackgroundIn the current era of high throughput genomics a major challenge is the genome-wide identification of target genes for specific transcription factors. Chromatin immunoprecipitation (ChIP) allows the isolation of in vivo binding sites of transcription factors and provides a powerful tool for examining gene regulation. Crosslinked chromatin is immunoprecipitated with antibodies against specific transcription factors, thus enriching for sequences bound in vivo by these factors in the immunoprecipitated DNA. Cloning and sequencing the immunoprecipitated sequences allows identification of transcription factor target genes. Routinely, thousands of such sequenced clones are used in BLAST searches to map their exact location in the genome and the genes located in the vicinity. These genes represent potential targets of the transcription factor of interest. Such bioinformatics analysis is very laborious if performed manually and for this reason there is a need for developing bioinformatic tools to automate and facilitate it.ResultsIn order to facilitate this analysis we generated TF Target Mapper (Transcription Factor Target Mapper). TF Target Mapper is a BLAST search tool allowing rapid extraction of annotated information on genes around each hit. It combines sequence cleaning/filtering, pattern searching and BLAST searches with extraction of information on genes located around each BLAST hit and comparisons of the output list of genes or gene ontology IDs with user-implemented lists. We successfully applied and tested TF Target Mapper to analyse sequences bound in vivo by the transcription factor GATA-1. We show that TF Target Mapper efficiently extracted information on genes around ChIPed sequences, thus identifying known (e.g. alpha-globin and zeta-globin) and potentially novel GATA-1 gene targets.ConclusionTF Target Mapper is a very efficient BLAST search tool that allows the rapid extraction of annotated information on the genes around each hit. It can contribute to the comprehensive bioinformatic transcriptome/regulome analysis, by providing insight into the mechanisms of action of specific transcription factors, thus helping to elucidate the pathways these factors regulate.

Dataset Information

SLING: a tool to search for linked genes in bacterial datasets.

Publications

SLING: a tool to search for linked genes in bacterial datasets.

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