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- W2896512386 abstract "The standard approaches to understand gene expression and regulation in the brain include identification of differentially and co-expressed genes. To this end, the Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) consortium has produced multiple, large RNA-seq datasets from several postmortem brain regions. Separately, the ENCODE project has produced DNAse Hypersensitivity (DHS) samples for various brain regions. We have integrated these large datasets into transcriptional regulatory networks (TRN), providing a directional and mechanistic list of putative transcription factors for nearly all expressed genes in the brain. We reprocessed all ENCODE brain DHS samples at scale, generating footprints—signatures of occupancy by DNA binding proteins—using the Wellington and HINT algorithms. We assembled motifs from JASPAR2016, HOCOMOCO, UniPROBE, and SwissRegulon, removing redundant motifs with Tomtom and intersecting our footprints with all possible overlapping motifs. This resulted in a total of 1,530 motifs mapping to 1,515 different transcription factors. We developed and utilized Transcriptional Regulatory Network Analysis (TReNA), available as an R Bioconductor package. Gene regulatory regions considered in our model were obtained through Genehancer, thus enabling the inclusion of all known enhancer regions. TReNA utilizes an ensemble of machine learning techniques, including lassopv, square root lasso (flare) and randomForest to prioritize transcription factors based on the expression levels in RNA-seq for each target gene. The scores from the aforementioned techniques are scaled and normalized into a composite score, thereby ranking all associated transcription factors for each target gene. We have identified transcriptional regulators for AD genes identified through GWAS. We have also identified putative targets for the AD-associated transcription factor MEF2C. We have identified multiple microglia-enriched transcription factors that regulate many differentially and co-expressed genes in AD, in particular, the AD-associated transcription factor SPI1. These resulting models can be applied to other datasets that generate lists of differentially or co-expressed genes as well as provide testable hypotheses for non-coding variants of interest. We are actively engaged in testing several hypotheses through experimental means and have made these TRNs publically available." @default.
- W2896512386 created "2018-10-26" @default.
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- W2896512386 date "2018-07-01" @default.
- W2896512386 modified "2023-10-16" @default.
- W2896512386 title "O3‐03‐01: MECHANISTIC AND DIRECTIONAL TRANSCRIPTIONAL REGULATORY NETWORKS IN ALZHEIMER'S DISEASE" @default.
- W2896512386 doi "https://doi.org/10.1016/j.jalz.2018.06.2782" @default.
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