Dataset Information

Genome-wide localization of small molecules.

ABSTRACT: A vast number of small-molecule ligands, including therapeutic drugs under development and in clinical use, elicit their effects by binding specific proteins associated with the genome. An ability to map the direct interactions of a chemical entity with chromatin genome-wide could provide important insights into chemical perturbation of cellular function. Here we describe a method that couples ligand-affinity capture and massively parallel DNA sequencing (Chem-seq) to identify the sites bound by small chemical molecules throughout the human genome. We show how Chem-seq can be combined with ChIP-seq to gain unique insights into the interaction of drugs with their target proteins throughout the genome of tumor cells. These methods will be broadly useful to enhance understanding of therapeutic action and to characterize the specificity of chemical entities that interact with DNA or genome-associated proteins.

SUBMITTER: Anders L

PROVIDER: S-EPMC4189815 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Genome-wide localization of small molecules.

Anders Lars L Guenther Matthew G MG Qi Jun J Fan Zi Peng ZP Marineau Jason J JJ Rahl Peter B PB Lovén Jakob J Sigova Alla A AA Smith William B WB Lee Tong Ihn TI Bradner James E JE Young Richard A RA

Nature biotechnology 20131215 1

A vast number of small-molecule ligands, including therapeutic drugs under development and in clinical use, elicit their effects by binding specific proteins associated with the genome. An ability to map the direct interactions of a chemical entity with chromatin genome-wide could provide important insights into chemical perturbation of cellular function. Here we describe a method that couples ligand-affinity capture and massively parallel DNA sequencing (Chem-seq) to identify the sites bound by ...[more]

PMID: 24336317

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Project description:Identifying transcription factors (TF) involved in producing a genome-wide transcriptional profile is an essential step in building mechanistic model that can explain observed gene expression data. We developed a statistical framework for constructing genome-wide signatures of TF activity, and for using such signatures in the analysis of gene expression data produced by complex transcriptional regulatory programs. Our framework integrates ChIP-seq data and appropriately matched gene expression profiles to identify True REGulatory (TREG) TF-gene interactions. It provides genome-wide quantification of the likelihood of regulatory TF-gene interaction that can be used to either identify regulated genes, or as genome-wide signature of TF activity. To effectively use ChIP-seq data, we introduce a novel statistical model that integrates information from all binding "peaks" within 2 Mb window around a gene's transcription start site (TSS), and provides gene-level binding scores and probabilities of regulatory interaction. In the second step we integrate these binding scores and regulatory probabilities with gene expression data to assess the likelihood of True REGulatory (TREG) TF-gene interactions. We demonstrate the advantages of TREG framework in identifying genes regulated by two TFs with widely different distribution of functional binding events (ER? and E2f1). We also show that TREG signatures of TF activity vastly improve our ability to detect involvement of ER? in producing complex diseases-related transcriptional profiles. Through a large study of disease-related transcriptional signatures and transcriptional signatures of drug activity, we demonstrate that increase in statistical power associated with the use of TREG signatures makes the crucial difference in identifying key targets for treatment, and drugs to use for treatment. All methods are implemented in an open-source R package treg. The package also contains all data used in the analysis including 494 TREG binding profiles based on ENCODE ChIP-seq data. The treg package can be downloaded at http://GenomicsPortals.org.

Project description:BACKGROUND: Genome-wide prediction of protein subcellular localization is an important type of evidence used for inferring protein function. While a variety of computational tools have been developed for this purpose, errors in the gene models and use of protein sorting signals that are not recognized by the more commonly accepted tools can diminish the accuracy of their output. RESULTS: As part of an effort to manually curate the annotations of 19 strains of Shewanella, numerous insights were gained regarding the use of computational tools and proteomics data to predict protein localization. Identification of the suite of secretion systems present in each strain at the start of the process made it possible to tailor-fit the subsequent localization prediction strategies to each strain for improved accuracy. Comparisons of the computational predictions among orthologous proteins revealed inconsistencies in the computational outputs, which could often be resolved by adjusting the gene models or ortholog group memberships. While proteomic data was useful for verifying start site predictions and post-translational proteolytic cleavage, care was needed to distinguish cellular versus sample processing-mediated cleavage events. Searches for lipoprotein signal peptides revealed that neither TatP nor LipoP are designed for identification of lipoprotein substrates of the twin arginine translocation system and that the +2 rule for lipoprotein sorting does not apply to this Genus. Analysis of the relationships between domain occurrence and protein localization prediction enabled identification of numerous location-informative domains which could then be used to refine or increase confidence in location predictions. This collective knowledge was used to develop a general strategy for predicting protein localization that could be adapted to other organisms. CONCLUSION: Improved localization prediction accuracy is not simply a matter of developing better computational algorithms. It also entails gathering key knowledge regarding the host architecture and translocation machinery and associated substrate recognition via experimentation and integration of diverse computational analyses from many proteins and, where possible, that are derived from different species within the same genus.

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Genome-wide localization of small molecules.

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Genome-wide localization of small molecules.

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