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.

We exploit cap analysis of gene expression (CAGE) time series datasets to directly measure promoter activities overtime.

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.

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.

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].

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.

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).

This led to exclusion of five libraries from the AoSMC-FGF2 time series , three from AoSMC-IL1b and one from MCF7-EGF.

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.

Understanding how such rhythms couple to biological processes requires statistical methods that can identify cycling time series in typical genome-Wide data.

In this paper, we improve on a method used to identify cycling time series by better estimating the statistical significance of periodic patterns and, in turn, by searching for a Wider range of patterns than traditionally investigated.

Despite the decreasing cost of measuring transcript levels, profiling time series genome-wide continues to present formidable challenges: tissue-specific samples are difficult to collect, and, in contrast to imaging, measuring transcript levels is destructive in nature, requiring separate samples for each time point.

As a result, gene expression time series are typically sparsely sampled (e.g., every 2—4 hours (h) in circadian studies), often without multiple measurements per time point, which we refer to here as “replicates”.

Quantitative methods are thus needed to identify rhythmic time series from minimal data with statistical confidence.

In contrast, F24 and ITK_CYCLE compare the time series in question to a reference waveform, which is typically sinusoidal.

In this way, stable persistence tries to robustly assess overall monotonicity of a time series .

The numerator is the number of pairs that vary concordantly between the two time series minus the number that vary discordantly (Fig.

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.

Yet, at courser resolution climate researchers have access to long time series of climate variables to assess model bias.

However, for emerging diseases, long time series would rarely be available, making the lack of data an even bigger issue for epidemiology.

Model time series for the measurement

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.

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.