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Learning the cis sequence elements that determine AP-1 monomer specificity (ChIP-seq data sets)

ABSTRACT: Learning the cis sequence elements that determine AP-1 monomer specificity (ChIP-seq data sets)

PROVIDER: PRJNA438357 | ENA |

REPOSITORIES: ENA

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Learning the cis sequence elements that determine AP-1 monomer specificity (RNA-seq data sets)

Project description:Learning the cis sequence elements that determine AP-1 monomer specificity (RNA-seq data sets)

| PRJNA438358 | ENA

Learning the cis sequence elements that determine AP-1 monomer specificity

Project description:Learning the cis sequence elements that determine AP-1 monomer specificity

| PRJNA438354 | ENA

Learning the cis sequence elements that determine AP-1 monomer specificity (RNA-seq data sets)

Project description:Regulation of gene expression is mediated by combinations of DNA binding transcription factors that work in concert to recruit transcriptional machinery. Each cell type expresses hundreds of sequence-specific transcription factors, many of which recognize identical or similar DNA sequences. Such factors can play both redundant and non-redundant roles, but mechanisms determining overlapping or distinct biological outcomes are largely unknown. Here, we implement a machine learning approach to investigate how local combinations of sequence motifs influence the genome wide binding patterns of different members of the AP-1 transcription factor family in macrophages. Significant motifs associated with family member specific binding patterns were validated by assessing effects of motif mutations in different strains of mice. We further confirmed the prediction of PPARg to be preferentially associated with the specific binding pattern of cJun using PPARg knockout macrophages. Together, our results provide evidence that unique binding patterns of AP-1 family members result in part from the corresponding unique ensembles of nearby regulatory elements embedded within enhancers and promoters, and that these elements can be identified by machine learning models trained using genomic sequence.

2019-01-31 | GSE111855 | GEO

Learning the cis sequence elements that determine AP-1 monomer specificity (ChIP-seq data sets)

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Learning the cis sequence elements that determine AP-1 monomer specificity

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3-AP and Gemcitabine in Treating Patients With Advanced Solid Tumors or Lymphoma

Project description:This phase I trial is studying the best dose of 3-AP and the side effects of giving 3-AP together with gemcitabine in treating patients with advanced solid tumors or lymphoma. Drugs used in chemotherapy, such as 3-AP and gemcitabine (GEM), work in different ways to stop the growth of cancer cells, either by killing the cells or by stopping them from dividing. 3-AP may help gemcitabine kill more cancer cells by making the cells more sensitive to the drug. 3-AP may also stop the growth of tumor cells by blocking some of the enzymes needed for cell growth.

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Modulation of apple leaf trancriptome by tomato cutin monomer extract

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An unbiased machine learning exploration reveals gene sets predictive of allograft tolerance after kidney transplantation

Project description:Efforts at finding potential biomarkers of tolerance after kidney transplantation have been hindered by limited sample size, as well as the complicated mechanisms underlying tolerance and the potential risk of rejection after immunosuppressant withdrawal. In this work, three different publicly available genome-wide expression data sets of peripheral blood lymphocyte (PBL) from 63 tolerant patients were used to compare 14 different machine learning models for their ability to predict spontaneous kidney graft tolerance. We found that the Best Subset Selection (BSS) regression approach was the most powerful with a sensitivity of 91.7% and a specificity of 93.8% in the test group, and a specificity of 86.1% and a sensitivity of 80% in the validation group. A feature set with five genes (HLA-DOA, TCL1A, EBF1, CD79B, and PNOC) was identified using the BSS model. EBF1 downregulation was also an independent factor predictive of graft rejection and graft loss. An AUC value of 84.4% was achieved using the two-gene signature (EBF1 and HLA-DOA) as an input to our classifier. Overall, our systematic machine learning exploration suggests novel biological targets that might affect tolerance to renal allografts, and provides clinical insights that can potentially guide patient selection for immunosuppressant withdrawal.

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Project description:to discover the miRNAs involved in the production of cytokines by RAW264.7 cells treated with LPS and ginsinoside Rd monomer We then performed gene expression profiling analysis using data obtained from RNA-seq of RAW264.7 cells.

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