Abstract | Using a large collection of densely-sampled time series expression data we have examined the induction of the immediate-early response in unparalleled detail, across cell types and stimuli. |
Abstract | We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities overtime. |
Abstract | Using a novel analysis method for time series data we identify transcripts with expression patterns that closely resemble the dynamics of known immediate-early genes (IEGs) and this enables a comprehensive comparative study of these genes and their chromatin state. |
Core regulatory components in the immediate-early response | The percentage of CAGE time series that could be reliably annotated with a kinetic signature varied from 21%—39% across the cell types and protein coding or RNA biotypes. |
Introduction | In particular, the CAGE time series datasets obtained for MCF-7 cells and human primary aortic smooth muscle cells are a unique resource for the study of the temporal response of stimulated human cells [19]. |
Introduction | Using these unique datasets, and a novel approach to time series analysis, we identify a comprehensive set of transcripts whose expression patterns are altered in response to a stimulus genome-wide, including all ncRNA transcripts present. |
Results | We focused on four particular time series datasets: human aortic smooth muscle cells (AoSMC) treated with FGF2 and with IL-lfi (9 time points from 0 to 360 min; 3 replicates per treatment; IL-lfi will be referred to as Ile hereafter), as well as human MCF7 breast cancer cells treated with EGF and HRG (16 time points from 0 to 480 min; 3 replicates per treatment). |
Results | This led to exclusion of five libraries from the AoSMC-FGF2 time series , three from AoSMC-IL1b and one from MCF7-EGF. |
Results | For the analysis of time series in this context, we distinguished early peaks from late peaks by bounding the prior range for the tS parameter by 1-240 minutes (the first half of the time series ), and by 240 minutes-end time (the second half) of the experiment respectively. |
A ale—9i. B._e_-—*a—« n—¥QL« ._ei' .—"§=4I;u ._e-_-fi:. ale—9i. ._eJ—' 'fi‘ n—ei' W n—Q'Zha deb. .—e—'_%‘ I481—c I—eic .—'fi. .—e—'_b n—W I—e—HE' | For climate considerations, we envisage that the precision of natural variability, To, would be large relative to each 2,- if bias is assessed by comparing model simulations to long time series of climate data. |
Introduction | Yet, at courser resolution climate researchers have access to long time series of climate variables to assess model bias. |
Introduction | However, for emerging diseases, long time series would rarely be available, making the lack of data an even bigger issue for epidemiology. |
Coupled Stuart-Landau/Kuramoto model parameters | Model time series for the measurement |
Coupled Stuart-Landau/Kuramoto model parameters | With each model, we produce a times series of length 10,000 for each run of the simulation, and then take the latter half of the time series for the measurement. |
Coupled Stuart-Landau/Kuramoto model parameters | The sampling rate of the time series is 1,000HZ, making the length of the produced time series 10s containing approximately 100 cycles of oscillation. |