The genome-wide protein architecture of chromatin that maintains chromosome integrity and gene regulation is ill-defined. Here we use ChIP-exo/seq 1 , 2 to define this structure in Saccharomyces . We identified 21 ensembles consisting of ~400 different proteins related to DNA replication, centromeres, subtelomeres, transposons, and RNA polymerase (Pol) I, II, and III transcription. Replication proteins engulfed a nucleosome, centromeres lacked a nucleosome, and repressive proteins encompassed three nucleosomes at subtelomeric X-elements. We find that most Pol II promoters evolved to lack a regulatory region, having only a core promoter. These constitutive promoters comprised a short nucleosome-free region (NFR) adjacent to a +1 nucleosome, which together bound TFIID to form a preinitiation complex (PIC). Positioned insulators protected core promoters from upstream events. A small fraction of promoters were architected for inducibility, wherein sequence-specific transcription factors (TFs) create a nucleosome-depleted region (NDR) that is distinct from NFRs. We describe TF structural interactions with the genome and cognate cofactors, including nucleosomal and transcriptional regulators RPD3-L, SAGA, NuA4, Tup1, Mediator, and SWI-SNF. Surprisingly, we do not detect TF-TFIID interactions, suggesting that they do not stably occur. Our model for gene induction involves TFs, cofactors, and general factors like TBP and TFIIB, but not TFIID. However, constitutive transcription involves TFIID but not TFs and cofactors. From this we define a highly integrated network of TF-regulated transcription.

There has been a rapid development in genome sequencing, including high-throughput next generation sequencing (NGS) technologies, automation in biological experiments, new bioinformatics tools and utilization of high-performance computing and cloud computing.ChIP-based NGS technologies, e.g. ChIP-seq and ChIP-exo, are widely used to detect the binding sites of DNA-interacting proteins in the genome and help us to have a deeper mechanistic understanding of genomic regulation. As sequencing data is generated at an unprecedented pace from the ChIP-based NGS pipelines, there is an urgent need for a metadata management system. To meet this need, we developed the Platform for Eukaryotic Genomic Regulation (PEGR), a web service platform that logs metadata for samples and sequencing experiments, manages the data processing workflows, and provides reporting and visualization. PEGR links together people, samples, protocols, DNA sequencers and bioinformatics computation. With the help of PEGR, scientists can have a more integrated understanding of the sequencing data and better understand the scientific mechanisms of genomic regulation. In this paper, we present the architecture and the major functionalities of PEGR. We also share our experience in developing this application and discuss the future directions.

Reproducibility is a significant challenge in (epi)genomic research due to the complexity of experiments composed of traditional biochemistry and informatics. Recent advances have exacerbated this challenge as high-throughput sequencing data is generated at an unprecedented pace. Here we report on our development of a Platform for Epi-Genomic Research (PEGR), a web-based project management platform that tracks and quality controls experiments from conception to publication-ready figures, compatible with multiple assays and bioinformatic pipelines. It supports rigor and reproducibility for biochemists working at the wet bench, while continuing to fully support reproducibility and reliability for bioinformaticians through integration with the Galaxy platform.

The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil

The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil .

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Reproducibility is a significant challenge in (epi)genomic research due to the complexity of experiments composed of traditional biochemistry and informatics. Recent advances have exacerbated this as high-throughput sequencing data is generated at an unprecedented pace. Here, we report the development of a Platform for Epi-Genomic Research (PEGR), a web-based project management platform that tracks and quality controls experiments from conception to publication-ready figures, compatible with multiple assays and bioinformatic pipelines. It supports rigor and reproducibility for biochemists working at the bench, while fully supporting reproducibility and reliability for bioinformaticians through integration with the Galaxy platform.