DNA-binding transcriptional regulators interpret the genome's regulatory code by binding to specific sequences to induce or repress gene expression 1 . Comparative genomics has recently been used to identify potential cis-regulatory sequences within the yeast genome on the basis of phylogenetic conservation 2-6 , but this information alone does not reveal if or when transcriptional regulators occupy these binding sites. We have constructed an initial map of yeast's transcriptional regulatory code by identifying the sequence elements that are bound by regulators under various conditions and that are conserved among Saccharomyces species. The organization of regulatory elements in promoters and the environment-dependent use of these elements by regulators are discussed. We find that environment-specific use of regulatory elements predicts mechanistic models for the function of a large population of yeast's transcriptional regulators.We used genome-wide location analysis 7-10 to determine the genomic occupancy of 203 DNA-binding transcriptional regulators in rich media conditions and, for 84 of these regulators, in at least 1 of 12 other environmental conditions (Supplementary Table 1, Supplementary Fig. 1; http://web.wi.mit.edu/young/regulatory_code). These 203 proteins are likely to include nearly all of the DNA-binding transcriptional regulators encoded in the yeast genome. Regulators were selected for profiling in an additional environment if they were essential for growth in that environment or if there was other evidence implicating them in the regulation of gene expression in that environment. The genome-wide location data identified 11,000 unique interactions between regulators and promoter regions at high confidence (P ≤ 0.001).

We demonstrate that the binding sites for highly conserved transcription factors vary extensively between human and mouse. We mapped the binding of four tissue-specific transcription factors (FOXA2, HNF1A, HNF4A, HNF6) to 4,000 orthologous gene pairs in hepatocytes purified from human and mouse livers. Despite the conserved function of these factors, from 41% to 89% of their binding events appear to be species-specific. When the same protein binds the promoters of orthologous genes, approximately two-thirds of the binding sites do not align.Elements of transcriptional regulation have central roles in evolution [1][2][3] . In many cases, conserved biological processes are controlled by evolutionarily conserved regulatory programs while evolving phenotypes are associated with cross-species variation in transcription regulation 4 . However, in the absence of suitable genome-wide data, it is unclear what fraction of all protein-DNA interactions are under either positive or negative selective pressure 1 . A preliminary effort to compare genome-wide binding sites for two stem cell-specific transcription factors in human and mouse has suggested that large differences exist between mouse and human 5, 6 yet because the data were obtained using different To compare systematically the binding of transcriptional regulators to promoter regions across species, we designed carefully matched ChIP-chip experiments 7 in human and mouse. We created custom DNA microarrays that array ten kilobases of sequence surrounding the known transcription start sites of over 4,000 orthologous pairs of mouse and human genes. These genes were selected because their orthology could be unambiguously assigned and oligonucleotides could be designed to represent the putative regulatory regions at high density ( Figure 1A, Supplementary Methods). Forty-seven hand-curated, tissuespecific genes were included in the array design as controls.Chromatin immunoprecipitations were performed independently in primary hepatocytes directly isolated from mouse and human liver using antibodies against four tissue-specific transcription factors (FOXA2, HNF1A, HNF4A, HNF6) involved in liver development and regulation ( Figure 1B, Table S1) 7 . Hepatocytes were chosen as a representative tissue for these experiments because (1) they are functionally and structurally conserved among mammals 8 ; (2) their gene expression programs are similar across species (Table S1); (3) their gene expression patterns are largely unperturbed by isolation procedures 9 ; and (4) the transcription factors responsible for hepatocyte development and function are highly conserved 8 . We amplified and fluorescently labeled the DNA from these binding experiments, hybridized it to the microarrays, and then scored binding events 10 .Several possible outcomes can be distinguished when comparing a binding event in one species with the data from the second species (Figure 1). First, one can determine if a particular transcription factor binds anywhere within the arrayed region of the human ...

In yeast, the impact of gene knockouts depends on genetic background.

We mapped the transcriptional regulatory circuitry for six master regulators in human hepatocytes using chromatin immunoprecipitation and high-resolution promoter microarrays. The results show that these regulators form a highly interconnected core circuitry, and reveal the local regulatory network motifs created by regulator-gene interactions. Autoregulation was a prominent theme among these regulators. We found that hepatocyte master regulators tend to bind promoter regions combinatorially and that the number of transcription factors bound to a promoter corresponds with observed gene expression. Our studies reveal portions of the core circuitry of human hepatocytes.

Direct physical information that describes where transcription factors, nucleosomes, modified histones, RNA polymerase II and other key proteins interact with the genome provides an invaluable mechanistic foundation for understanding complex programs of gene regulation. We present a method, joint binding deconvolution (JBD), which uses additional easily obtainable experimental data about chromatin immunoprecipitation (ChIP) to improve the spatial resolution of the transcription factor binding locations inferred from ChIP followed by DNA microarray hybridization (ChIP-Chip) data. Based on this probabilistic model of binding data, we further pursue improved spatial resolution by using sequence information. We produce positional priors that link ChIP-Chip data to sequence data by guiding motif discovery to inferred protein-DNA binding sites. We present results on the yeast transcription factors Gcn4 and Mig2 to demonstrate JBD's spatial resolution capabilities and show that positional priors allow computational discovery of the Mig2 motif when a standard approach fails.

Background: Genome-wide approaches have begun to reveal the transcriptional networks responsible for pluripotency in embryonic stem (ES) cells. Chromatin Immunoprecipitation (ChIP) followed either by hybridization to a microarray platform (ChIP-chip) or by DNA sequencing (ChIP-PET), has identified binding targets of the ES cell transcription factors OCT4 and NANOG in humans and mice, respectively. These studies have provided an outline of the transcriptional framework involved in maintaining pluripotency. Recent evidence with comparing multiple technologies suggests that expanding these datasets using different platforms would be a useful resource for examining the mechanisms underlying pluripotency regulation.

"Next generation" data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the problem caused by exponential data growth by applying horizontally scalable techniques from current analytics systems to accelerate scientific processing pipelines. In this paper, we describe ADAM, an example genomics pipeline that leverages the open-source Apache Spark and Parquet systems to achieve a 28× speedup over current genomics pipelines, while reducing cost by 63%. From building this system, we were able to distill a set of techniques for implementing scientific analyses efficiently using commodity "big data" systems. To demonstrate the generality of our architecture, we then implement a scalable astronomy image processing system which achieves a 2.8-8.9× improvement over the state-of-the-art MPI-based system.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Zebrafish promoter microarray

The development and verification of a genomic microarray for ChIP-chip analysis of zebrafish genes is described.