Project description:Detecting differential activation of transcription factors (TFs) in response to perturbation provides insight into global cellular processes. We present here Transcription Factor Enrichment Analysis (TFEA), a robust and reliable computational method that can detect differential activity of hundreds of TFs given any set of perturbation data. TFEA draws inspiration from GSEA and detects positional motif enrichment within an ordered list of regions of interest (ROI). As ROI are typically directly inferred from the data, we also introduce muMerge, a statistically principled method that generates a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data types that inform on transcriptional regulation, including CAGE data, ChIP-Seq, and accessibility data (e.g. ATAC-Seq). We demonstrate that TFEA can not only identify key TFs that respond to a perturbation, but also temporally unravel complex regulatory networks with time series data. Consequently, TFEA serves as a “hypothesis-generating engine” that provides an easy, rigorous, and cost-effective means to broadly assess TF activity to yield new biological insights about basic cellular processes.
2020-12-18 | GSE142419 | GEO
Project description:A Simplified Method to Enrich Tetrahymena Micronuclear DNA
Project description:Anthropogenic activities have dramatically increased the inputs of reactive nitrogen (N) into terrestrial ecosystems, with potentially important effects on the soil microbial community and consequently soil C and N dynamics. Our analysis of microbial communities in soils subjected to 14 years of 7 g N m-2 year-1 Ca(NO3)2 amendment in a Californian grassland showed that the taxonomic composition of bacterial communities, examined by 16S rRNA gene amplicon sequencing, was significantly altered by nitrate amendment, supporting the hypothesis that N amendment- induced increased nutrient availability, yielded more fast-growing bacterial taxa while reduced slow-growing bacterial taxa. Nitrate amendment significantly increased genes associated with labile C degradation (e.g. amyA and xylA) but had no effect or decreased the relative abundances of genes associated with degradation of more recalcitrant C (e.g. mannanase and chitinase), as shown by data from GeoChip targeting a wide variety of functional genes. The abundances of most N cycling genes remained unchanged or decreased except for increases in both the nifH gene (associated with N fixation), and the amoA gene (associated with nitrification) concurrent with increases of ammonia-oxidizing bacteria. Based on those observations, we propose a conceptual model to illustrate how changes of functional microbial communities may correspond to soil C and N accumulation.
Project description:A sigB null (∆sigB) mutant was constructed and analyzed for its phenotypes and transcriptome along with those of the parental RF122 strain. Method: Duplicate samples of rRNA depleted RNA from wild type and mutants were used to study transcriptomes by ion torrent platform.
Project description:Protein synthesis is dysregulated in many diseases, but we lack a systems-level picture of how signaling molecules and RNA binding proteins interact with the translational machinery, largely due to technological limitations. Here we present riboPLATE-seq, a scalable method for generating paired libraries of ribosome-associated and total mRNA. As an extension of the PLATE-seq protocol, riboPLATE-seq utilizes barcoded primers for pooled library preparation, but additionally leverages rRNA immunoprecipitation on whole polysomes to measure ribosome association (RA). We demonstrate the performance of riboPLATE-seq and its utility in detecting translational alterations induced by inhibition of protein kinases.
Project description:The most widely-used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms and analysis algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and "spike-ins" comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. All commercial tiling array platforms performed well, although each platform and analysis algorithm had distinct performance and cost characteristics. Simple sequence repeats and genome redundancy tend to result in false positives on oligonucleotide platforms. The spike-in DNA samples and the resulting array data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: chip-ChIP simulation For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf