2 results for chip-seq drosophila
Transcriptomics hit the target: Monitoring of ligand-activated and stress response pathways for chemical testing
ChIP-seq...ChIP-seq datasets reporting genes specifically bound by a TF become publicly...ChIP-seq and gene expression gene lists was used to generate TF signatures...ChIP-seq analysis (activator, concentration and duration of exposure), ... High content omic methods provide a deep insight into cellular events occurring upon chemical exposure of a cell population or tissue. However, this improvement in analytic precision is not yet matched by a thorough understanding of molecular mechanisms that would allow an optimal interpretation of these biological changes. For transcriptomics (TCX), one type of molecular effects that can be assessed already is the modulation of the transcriptional activity of a transcription factor (TF). As more ChIP-seq datasets reporting genes specifically bound by a TF become publicly available for mining, the generation of target gene lists of TFs of toxicological relevance becomes possible, based on actual protein-DNA interaction and modulation of gene expression. In this study, we generated target gene signatures for Nrf2, ATF4, XBP1, p53, HIF1a, AhR and PPAR gamma and tracked TF modulation in a large collection of in vitro TCX datasets from renal and hepatic cell models exposed to clinical nephro- and hepato-toxins. The result is a global monitoring of TF modulation with great promise as a mechanistically based tool for chemical hazard identification.
Contributors: Percha, Bethany, Altman, Russ B.
... This repository contains labeled, weighted networks of chemical-gene, gene-gene, gene-disease, and chemical-disease relationships based on single sentences in PubMed abstracts. All raw dependency paths are provided in addition to the labeled relationships. PART I: Connects dependency paths to labels, or "themes". Each record contains a dependency path followed by its score for each theme, and indicators of whether or not the path is part of the flagship path set for each theme (meaning that it was manually reviewed and determined to reflect that theme). The themes themselves are listed below and are in our paper (reference below). PART II: Connects sentences to dependency paths. It consists of sentences and associated metadata, entity pairs found in the sentences, and dependency paths connecting those entity pairs. Each record contains the following information: PubMed ID Sentence number (0 = title) First entity name, formatted First entity name, location (characters from start of abstract) Second entity name, formatted Second entity name, location First entity name, raw string Second entity name, raw string First entity name, database ID(s) Second entity name, database ID(s) First entity type (Chemical, Gene, Disease) Second entity type (Chemical, Gene, Disease) Dependency path Sentence, tokenized The "with-themes.txt" files only contain dependency paths with corresponding theme assignments from Part I. The plain ".txt" files contain all dependency paths. This release contains the annotated network for the April 30, 2016 version of PubTator, which is described in our paper (below). We will also be releasing an updated version of the network periodically, as the PubTator community continues to release new versions each month or so. ------------------------------------------------------------------------------------ REFERENCES Percha B, Altman RBA (2017) A global network of biomedical relationships derived from text. (Submitted to Bioinformatics; currently in revision.) Percha B, Altman RBA (2015) Learning the structure of biomedical relationships from unstructured text. PLoS Computational Biology, 11(7): e1004216. This project depends on named entity annotations from the PubTator project: https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/ Reference: Wei CH et. al., PubTator: a Web-based text mining tool for assisting Biocuration, Nucleic acids research, 2013, 41 (W1): W518-W522. doi: 10.1093/nar/gkt44 Dependency parsing was provided by the Stanford CoreNLP toolkit: https://stanfordnlp.github.io/CoreNLP/index.html Reference: Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60. ------------------------------------------------------------------------------------ THEMES chemical-gene (A+) agonism, activation (A-) antagonism, blocking (B) binding, ligand (esp. receptors) (E+) increases expression/production (E-) decreases expression/production (E) affects expression/production (neutral) (N) inhibits gene-chemical (O) transport, channels (K) metabolism, pharmacokinetics (Z) enzyme activity chemical-disease (T) treatment/therapy (including investigatory) (C) inhibits cell growth (esp. cancers) (Sa) side effect/adverse event (Pr) prevents, suppresses (Pa) alleviates, reduces (J) role in disease pathogenesis disease-chemical (Mp) biomarkers (of disease progression) gene-disease (U) causal mutations (Ud) mutations affecting disease course (D) drug targets (J) role in pathogenesis (Te) possible therapeutic effect (Y) polymorphisms alter risk (G) promotes progression disease-gene (Md) biomarkers (diagnostic) (X) overexpression in disease (L) improper regulation linked to disease gene-gene (B) binding, ligand (esp. receptors) (W) enhances response (V+) activates, stimulates (E+) increases expression/production (E) affects expression/production (neutral) (I) signaling pathway (H) same protein or complex (Rg) regulation (Q) production by cell population