A Computational Framework for Genome-wide Characterization of the Human Disease Landscape.

A key challenge for the diagnosis and treatment of complex human diseases is identifying their molecular basis. Here, we developed a unified computational framework, URSAHD (Unveiling RNA Sample Annotation for Human Diseases), that leverages machine learning and the hierarchy of anatomical relationships present among diseases to integrate thousands of clinical gene expression profiles and identify molecular characteristics specific to each of the hundreds of complex diseases. URSAHD can distinguish between closely related diseases more accurately than literature-validated genes or traditional differential-expression-based computational approaches and is applicable to any disease, including rare and understudied ones. We demonstrate the utility of URSAHD in classifying related nervous system cancers and experimentally verifying novel neuroblastoma-associated genes identified by URSAHD. We highlight the applications for potential targeted drug-repurposing and for quantitatively assessing the molecular response to clinical therapies. URSAHD is freely available for public use, including the use of underlying models, at ursahd.princeton.edu.

The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions.

A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein-protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report.Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html.

BioCreative V BioC track overview: collaborative biocurator assistant task for BioGRID.

BioC is a simple XML format for text, annotations and relations, and was developed to achieve interoperability for biomedical text processing. Following the success of BioC in BioCreative IV, the BioCreative V BioC track addressed a collaborative task to build an assistant system for BioGRID curation. In this paper, we describe the framework of the collaborative BioC task and discuss our findings based on the user survey. This track consisted of eight subtasks including gene/protein/organism named entity recognition, protein-protein/genetic interaction passage identification and annotation visualization. Using BioC as their data-sharing and communication medium, nine teams, world-wide, participated and contributed either new methods or improvements of existing tools to address different subtasks of the BioC track. Results from different teams were shared in BioC and made available to other teams as they addressed different subtasks of the track. In the end, all submitted runs were merged using a machine learning classifier to produce an optimized output. The biocurator assistant system was evaluated by four BioGRID curators in terms of practical usability. The curators' feedback was overall positive and highlighted the user-friendly design and the convenient gene/protein curation tool based on text mining.Database URL: http://www.biocreative.org/tasks/biocreative-v/track-1-bioc/.

Use of the BioGRID Database for Analysis of Yeast Protein and Genetic Interactions.

The BioGRID database is an extensive repository of curated genetic and protein interactions for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe, and the yeast Candida albicans SC5314, as well as for several other model organisms and humans. This protocol describes how to use the BioGRID website to query genetic or protein interactions for any gene of interest, how to visualize the associated interactions using an embedded interactive network viewer, and how to download data files for either selected interactions or the entire BioGRID interaction data set.

BioGRID: A Resource for Studying Biological Interactions in Yeast.

The Biological General Repository for Interaction Datasets (BioGRID) is a freely available public database that provides the biological and biomedical research communities with curated protein and genetic interaction data. Structured experimental evidence codes, an intuitive search interface, and visualization tools enable the discovery of individual gene, protein, or biological network function. BioGRID houses interaction data for the major model organism species--including yeast, nematode, fly, zebrafish, mouse, and human--with particular emphasis on the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe as pioneer eukaryotic models for network biology. BioGRID has achieved comprehensive curation coverage of the entire literature for these two major yeast models, which is actively maintained through monthly curation updates. As of September 2015, BioGRID houses approximately 335,400 biological interactions for budding yeast and approximately 67,800 interactions for fission yeast. BioGRID also supports an integrated posttranslational modification (PTM) viewer that incorporates more than 20,100 yeast phosphorylation sites curated through its sister database, the PhosphoGRID.

The Set3 Complex Antagonizes the MYST Acetyltransferase Esa1 in the DNA Damage Response.

Acetylation is a dynamic posttranslational modification that contributes to chromatin-regulated processes, including DNA replication, repair, recombination, and gene expression. Acetylation is controlled by complexes containing opposing lysine and histone acetyltransferase (KAT and HAT) and deacetylase (KDAC and HDAC) activities. The essential MYST family Esa1 KAT acetylates core histones and many nonhistone substrates. Phenotypes of esa1 mutants include transcriptional silencing and activation defects, impaired growth at high temperatures, and sensitivity to DNA damage. The KDAC Rpd3 was previously identified as an activity opposing Esa1, as its deletion suppresses growth and silencing defects of esa1 mutants. However, loss of Rpd3 does not suppress esa1 DNA damage sensitivity. In this work, we identified Hos2 as a KDAC counteracting ESA1 in the damage response. Deletion of HOS2 resulted in changes of esa1's transcriptional response upon damage. Further, loss of HOS2 or components of the Set3 complex (Set3C) in which it acts specifically suppressed damage sensitivity and restored esa1 histone H4 acetylation. This rescue was mediated via loss of either Set3C integrity or of its binding to dimethylated histone H3K4. Our results thus add new insight into the interactions of an essential MYST acetyltransferase with diverse deacetylases to respond specifically to environmental and physiological challenges.

Suppression analysis of esa1 mutants in Saccharomyces cerevisiae links NAB3 to transcriptional silencing and nucleolar functions.

The acetyltransferase Esa1 is essential in the yeast Saccharomyces cerevisiae and plays a critical role in multiple cellular processes. The most well-defined targets for Esa1 are lysine residues on histones. However, an increasing number of nonhistone proteins have recently been identified as substrates of Esa1. In this study, four genes (LYS20, LEU2, VAP1, and NAB3) were identified in a genetic screen as high-copy suppressors of the conditional temperature-sensitive lethality of an esa1 mutant. When expressed from a high-copy plasmid, each of these suppressors rescued the temperature-sensitivity of an esa1 mutant. Only NAB3 overexpression also rescued the rDNA-silencing defects of an esa1 mutant. Strengthening the connections between NAB3 and ESA1, mutants of nab3 displayed several phenotypes similar to those of esa1 mutants, including increased sensitivity to the topoisomerase I inhibitor camptothecin and defects in rDNA silencing and cell-cycle progression. In addition, nuclear localization of Nab3 was altered in the esa1 mutant. Finally, posttranslational acetylation of Nab3 was detected in vivo and found to be influenced by ESA1.

Collaboration between the essential Esa1 acetyltransferase and the Rpd3 deacetylase is mediated by H4K12 histone acetylation in Saccharomyces cerevisiae.

Histone modifications that regulate chromatin-dependent processes are catalyzed by multisubunit complexes. These can function in both targeting activities to specific genes and in regulating genomewide levels of modifications. In Saccharomyces cerevisiae, Esa1 and Rpd3 have opposing enzymatic activities and are catalytic subunits of multiple chromatin modifying complexes with key roles in processes such as transcriptional regulation and DNA repair. Esa1 is an essential histone acetyltransferase that belongs to the highly conserved MYST family. This study presents evidence that the yeast histone deacetylase gene, RPD3, when deleted, suppressed esa1 conditional mutant phenotypes. Deletion of RPD3 reversed rDNA and telomeric silencing defects and restored global H4 acetylation levels, in addition to rescuing the growth defect of a temperature-sensitive esa1 mutant. This functional genetic interaction between ESA1 and RPD3 was mediated through the Rpd3L complex. The suppression of esa1's growth defect by disruption of Rpd3L was dependent on lysine 12 of histone H4. We propose a model whereby Esa1 and Rpd3L act coordinately to control the acetylation of H4 lysine 12 to regulate transcription, thereby emphasizing the importance of dynamic acetylation and deacetylation of this particular histone residue in maintaining cell viability.