The Encyclopedia of DNA Elements (ENCODE) Data Coordinating Center has developed the ENCODE Portal database and website as the source for the data and metadata generated by the ENCODE Consortium. Two principles have motivated the design. First, experimental protocols, analytical procedures and the data themselves should be made publicly accessible through a coherent, web-based search and download interface. Second, the same interface should serve carefully curated metadata that record the provenance of the data and justify its interpretation in biological terms. Since its initial release in 2013 and in response to recommendations from consortium members and the wider community of scientists who use the Portal to access ENCODE data, the Portal has been regularly updated to better reflect these design principles. Here we report on these updates, including results from new experiments, uniformly-processed data from other projects, new visualization tools and more comprehensive metadata to describe experiments and analyses. Additionally, the Portal is now home to meta(data) from related projects including Genomics of Gene Regulation, Roadmap Epigenome Project, Model organism ENCODE (modENCODE) and modERN. The Portal now makes available over 13000 datasets and their accompanying metadata and can be accessed at: https://www.encodeproject.org/.

The Saccharomyces Genome Database (SGD, http://www.yeastgenome.org) is the community resource for the budding yeast Saccharomyces cerevisiae. The SGD project provides the highest-quality manually curated information from peer-reviewed literature. The experimental results reported in the literature are extracted and integrated within a well-developed database. These data are combined with quality high-throughput results and provided through Locus Summary pages, a powerful query engine and rich genome browser. The acquisition, integration and retrieval of these data allow SGD to facilitate experimental design and analysis by providing an encyclopedia of the yeast genome, its chromosomal features, their functions and interactions. Public access to these data is provided to researchers and educators via web pages designed for optimal ease of use.

Sequence preferences of DNA-binding proteins are a primary mechanism by which cells interpret the genome. Despite these proteins' central importance in physiology, development, and evolution, comprehensive DNA-binding specificities have been determined experimentally for few proteins. Here, we used microarrays containing all 10-base-pair sequences to examine the binding specificities of 104 distinct mouse DNA-binding proteins representing 22 structural classes. Our results reveal a complex landscape of binding, with virtually every protein analyzed possessing unique preferences. Roughly half of the proteins each recognized multiple distinctly different sequence motifs, challenging our molecular understanding of how proteins interact with their DNA binding sites. This complexity in DNA recognition may be important in gene regulation and in evolution of transcriptional regulatory networks.The interactions between transcription factors (TFs) and their DNA binding sites are an integral part of the gene regulatory networks that control development, core cellular processes, and responses to environmental perturbations. However, only a handful of sequence-specific TFs have been characterized well enough to identify all the sequences that they can and, just as importantly, can not bind. Computational analysis of microarray readout of chromatin immunoprecipitation experiments (ChIP-chip) suggests extensive use of low affinity binding sites in yeast (1), and computational models of gene expression during fly embryonic development suggest that low affinity binding sites contribute as much as high affinity sites (2).The availability of TF binding data spanning the full affinity range would improve our understanding of the biophysical phenomena underlying protein-DNA recognition, and would improve accuracy in analyzing cis regulatory elements. Here we report the comprehensive determination of the DNA binding specificities of 104 known and predicted mouse TFs using the universal protein binding microarray (PBM) technology (3). These TFs represent 22 different DNA binding domain (DBD) structural classes that are the major DBD classes found in metazoan TFs.We created (4) N-terminal GST fusion constructs of the DBDs of 104 known and predicted mouse TFs (Fig. S1 and Table S1). Five of these proteins -Max, Bhlhb2, Gata3, Rfx3, and Sox7 -were also represented as full-length fusions to N-terminal GST, yielding a total set of 109 non-redundant proteins represented by 115 samples (5). Each protein was used in two PBM experiments (6,7) (Figs. S2, S3, S4 and Table S2). DNA binding site motifs initially were derived using the Seed-and-Wobble algorithm (3,8); Seed-and-Wobble first identifies the single 8-mer (ungapped or gapped) with the greatest PBM enrichment score (E-score) (3), and then systematically tests the relative preference of each nucleotide variant at each position both within and outside the seed (5). Later analyses incorporated additional motif finding algorithms, including RankMotif++ (9) and Kafal (5).Beyond simpl...

Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity and showing that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.

Summary The sequence specificity of DNA-binding proteins is the primary mechanism by which the cell recognizes genomic features. Here, we describe systematic determination of yeast transcription factor DNA-binding specificities. We obtained binding specificities for 112 DNA-binding proteins representing 19 distinct structural classes, one-third of which have not been previously reported. Several newly discovered binding sequences have striking genomic distributions relative to transcription start sites, supporting their biological relevance and suggesting a role in promoter architecture. Among these are Rsc3 binding sequences, containing the core CGCG, which are found preferentially ~100 bp upstream of transcription start sites. Mutation of RSC3 results in a dramatic increase in nucleosome occupancy in hundreds of proximal promoters containing a Rsc3 binding element, but has little impact on promoters lacking Rsc3 binding sequences, indicating that Rsc3 plays a broad role in targeting nucleosome exclusion at yeast promoters.

The Encyclopedia of DNA Elements (ENCODE) Project is in its third phase of creating a comprehensive catalog of functional elements in the human genome. This phase of the project includes an expansion of assays that measure diverse RNA populations, identify proteins that interact with RNA and DNA, probe regions of DNA hypersensitivity, and measure levels of DNA methylation in a wide range of cell and tissue types to identify putative regulatory elements. To date, results for almost 5000 experiments have been released for use by the scientific community. These data are available for searching, visualization and download at the new ENCODE Portal (www.encodeproject.org). The revamped ENCODE Portal provides new ways to browse and search the ENCODE data based on the metadata that describe the assays as well as summaries of the assays that focus on data provenance. In addition, it is a flexible platform that allows integration of genomic data from multiple projects. The portal experience was designed to improve access to ENCODE data by relying on metadata that allow reusability and reproducibility of the experiments.

Metazoan genomes encode hundreds of RNA-binding proteins (RBPs) but RNA-binding preferences for relatively few RBPs have been well defined. Current techniques for determining RNA targets, including in vitro selection and RNA co-immunoprecipitation, require significant time and labor investment. Here we introduce RNAcompete, a method for the systematic analysis of RNA binding specificities that uses a single binding reaction to determine the relative preferences of RBPs for short RNAs that contain a complete range of k-mers in structured and unstructured RNA contexts. We tested RNAcompete by analyzing nine diverse RBPs (HuR, Vts1, FUSIP1, PTB, U1A, SF2/ASF, SLM2, RBM4 and YB1). RNAcompete identified expected and previously unknown RNA binding preferences. Using in vitro and in vivo binding data, we demonstrate that preferences for individual 7-mers identified by RNAcompete are a more accurate representation of binding activity than are conventional motif models. We anticipate that RNAcompete will be a valuable tool for the study of RNA-protein interactions.

Metazoan genomes encode hundreds of RNA-binding proteins (RBPs). These proteins regulate post-transcriptional gene expression and have critical roles in numerous cellular processes including mRNA splicing, export, stability and translation. Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In vitro and in vivo studies, using affinity selection-based approaches, have successfully identified RNA sequence associated with specific RBPs; however, it is difficult to infer RBP sequence and structural preferences without specifically designed motif finding methods. In this study, we introduce a new motif-finding method, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences with greater accuracy than existing approaches. We evaluated RNAcontext on recently published in vitro and in vivo RNA affinity selected data and demonstrate that RNAcontext identifies known binding preferences for several control proteins including HuR, PTB, and Vts1p and predicts new RNA structure preferences for SF2/ASF, RBM4, FUSIP1 and SLM2. The predicted preferences for SF2/ASF are consistent with its recently reported in vivo binding sites. RNAcontext is an accurate and efficient motif finding method ideally suited for using large-scale RNA-binding affinity datasets to determine the relative binding preferences of RBPs for a wide range of RNA sequences and structures.