We validate our results using data from genome-wide TF knockout experiments.

Thus, we developed a general-purpose computational method using logic-circuit models from electronics and applied it to a human leukemia dataset, identifying the genome-Wide cooperatiVity of transcription factors and microRNAs.

To our knowledge, the present study describes for the first time the use of 16 logic operations to perform a comprehensive genome-wide analysis of regulatory triplets.

The rapidly increasing amount of high throughput sequencing data offers novel and diverse resources to probe molecular functions on a genome-wide scale.

On a genome-wide scale ChIP-Seq provides regulatory information about wiring between RFs and targets, while RNA-Seq provides gene eXpression data; by combining these two data types we are able to go beyond the regulatory activities of individual RFs and investigate the relationships between higher order RF groups.

While this model is not able to capture the very complex regulatory patterns that may be characterized by continuous models [12,13], it is computationally efficient, and it is comprehensive enough to meaningfully describe a large variety of regulatory networks on a genome-wide scale in multiple organisms.

We apply Loregic to find the logic operations that characterize the FFLs from a genome-Wide perspective in both the yeast cell cycle and human leukemia cancer datasets.

We used yeast genome-wide TF knockout experiments to validate the TF logic from gate-consistent triplets.

Robust methods for identifying patterns of expression in genome-wide data are important for generating hypotheses regarding gene function.

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 compare methods for detecting rhythmic time series in genome-wide expression data.

These approaches are general and can be applied to detecting periodic behavior in a wide range of contexts, but we focus on time series representative of genome-wide expression data.

[28] recently reviewed a number of earlier studies of rhythm detection methods and selected four algorithms for comparison (de Lichtenberg, Lomb-Scargle, ITK_CYCLE, and persistent homology) based on their mathematical properties and applicability to genome-wide expression data.

By contrast, here we focus on discovering rhythmic time series that represent only a fraction of a genome-wide dataset.

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.

We use it to further assess the importance of considering asymmetric waveforms, and we eXplore how multiple hypothesis correction impacts the results when the true positives represent a relatively small fraction of the simulated time series, as we eXpect to be the case in genome-wide studies.

Furthermore, we focus on genome-wide experiments where the experimental design is such that there is no meaningful difference between data collected over multiple periods and data collected at the same sampling rate in replicate over a single period.

This composition was chosen to be reflective of a genome-wide dataset.

We characterise IEGs in a genome-wide sequencing dataset that captures their transcriptional response over time.

Genome-wide analysis of enhancer activity was then performed.

Such similarities and differences between the epigenetic regulation of lncRNA and mRNA have been reported previously in genome-wide data [17].

Gene sets assigned to the best fitting model can be tested for over-representa-tion of established gene and pathway annotations, and can be integrated with genome-wide data sets to test additional hypotheses.

The transcription of primary miRNA transcripts (pri-miRNAs), and the subsequent role of the mature transcripts in the immediate-early response is unexplored in genome-wide data.

Genome-wide characterisation of histone modifications H3K4me3 and H3K27me3 at lncRNA has demonstrated common features with mRNA, whereas patterns of DNA methylation differ [17].

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.

Immediate early genes are typically shorter in length than the genome-wide average [6].

The genome-Wide CAGE data considered here necessarily included transcripts Whose functions are unknown thus we began by hypothesising the possible kinetics they may display, rather than by constructing a detailed, interconnected systems model.

We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences.

To overcome this limitation and design cost-efficient studies, we developed a two step method: sequencing of relatively few members of a well-characterized founder population followed by pedigree-based whole genome imputation of many other individuals with genome-wide genotype data.

Framework Genome-Wide Genotypes

To address the limitations of LD- and pedigree-based imputation methods, we developed PRIMAL (Bediggee Mputation &gorithm), a fast phasing and imputation algorithm, to assign genotypes at 7 million bi-allelic variants that were discovered in the whole genome sequences of 98 Hutterites to an additional set of 1,317 Hutterites who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs).

The gene-disease associations were retrieved from OMIM (Online Mendelian Inheritance in Man; http://WWW.ncbi.nlm.nih.g0V/ omim) [51] and GWAS ( Genome-Wide Association Studies.

We use a genome-wide significance cutoff of p-value g 5 - 10—8.

With recent advances in genome-wide disease gene association [9] and high-throughput Interactome mapping [10] we can already pinpoint the approximate location for some disease modules (Fig.

Our in silico analysis carried out genome-wide via the StabFIex algorithm, shows the conserved presence of highly flexible regions in budding yeast genome as well as in genomes of other Saccharomyces sensu stricto species.

The extent of this correlation Will be determined by a comparable genome-Wide analysis on human sequence DNA flexibility.

A very favourable condition is the large availability of genome-wide data concerning the structural and functional aspects.