Current efforts to understand and counteract them have been aided by the use of cultured mosquito cells. Mosquito-borne diseases present a worldwide public health burden. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR's user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein-DNA binding data, and protein-protein interaction networks. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. Furthermore, methods integrating ordered RNA-seq data with protein-DNA binding data to distinguish direct from indirect interactions are urgently needed. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. Miss the old tools overview page? View it here. Table of all public DRSC cell-based RNAi screen data sets at Screen SummaryįlyBi Drosophila Y2H binary interaction data DRSC/CCSB/BDGPĬonnect human gene variants to ortholog info (multi-source)Ĭonnect disease genes to ortholog info or vice versa (OMIM & GWAS)įly insulin network data (PPI, RNAi, PTM +/- insulin) Nucleolar fly cell RNAi image-based screen data setĭRSC RNA Binding RNAi library fly cell screen data set Pooled CRISPR fly cell screen raw data sets MitoMax fly mitochondrial proteomics data set Single-cell RNAseq data sets for specific tissues Predict cell-cell communication from scRNAseq data (make a TRiP fly stock list from a gene list) Nominate or track TRiP-KO & -OE fly stock productionĭesign allele-specific sgRNA for major model organisms ( PAthway, Network and Gene-set Enrichment Analysis)
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