Expanded protein information at SGD: new pages and proteome browser

The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; ) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82 000 manually-curated interactions

Rapidly evolving protointrons in Saccharomyces genomes revealed by a hungry spliceosome.

Introns are a prevalent feature of eukaryotic genomes, yet their origins and contributions to genome function and evolution remain mysterious. In budding yeast, repression of the highly transcribed intron-containing ribosomal protein genes (RPGs) globally increases splicing of non-RPG transcripts through reduced competition for the spliceosome. We show that under these "hungry spliceosome" conditions, splicing occurs at more than 150 previously unannotated locations we call protointrons that do not overlap known introns. Protointrons use a less constrained set of splice sites and branchpoints than standard introns, including in one case AT-AC in place of GT-AG. Protointrons are not conserved in all closely related species, suggesting that most are not under positive selection and are fated to disappear. Some are found in non-coding RNAs (e. g. CUTs and SUTs), where they may contribute to the creation of new genes. Others are found across boundaries between noncoding and coding sequences, or within coding sequences, where they offer pathways to the creation of new protein variants, or new regulatory controls for existing genes. We define protointrons as (1) nonconserved intron-like sequences that are (2) infrequently spliced, and importantly (3) are not currently understood to contribute to gene expression or regulation in the way that standard introns function. A very few protointrons in S. cerevisiae challenge this classification by their increased splicing frequency and potential function, consistent with the proposed evolutionary process of "intronization", whereby new standard introns are created. This snapshot of intron evolution highlights the important role of the spliceosome in the expansion of transcribed genomic sequence space, providing a pathway for the rare events that may lead to the birth of new eukaryotic genes and the refinement of existing gene function

A comprehensive molecular interaction map of the budding yeast cell cycle

With the accumulation of data on complex molecular machineries coordinating cell-cycle dynamics, coupled with its central function in disease patho-physiologies, it is becoming increasingly important to collate the disparate knowledge sources into a comprehensive molecular network amenable to systems-level analyses. In this work, we present a comprehensive map of the budding yeast cell-cycle, curating reactions from ∼600 original papers. Toward leveraging the map as a framework to explore the underlying network architecture, we abstract the molecular components into three planes—signaling, cell-cycle core and structural planes. The planar view together with topological analyses facilitates network-centric identification of functions and control mechanisms. Further, we perform a comparative motif analysis to identify around 194 motifs including feed-forward, mutual inhibitory and feedback mechanisms contributing to cell-cycle robustness. We envisage the open access, comprehensive cell-cycle map to open roads toward community-based deeper understanding of cell-cycle dynamics

The functional network in predictive biology : predicting phenotype from genotype and predicting human disease from fungal phenotype

textThe ability to predict is one of the hallmarks of successful theories. Historically, the predictive power of biology has lagged behind disciplines like physics because the biological world is complex, challenging to quantify, and full of exceptions. However, in recent years the amount of available data has expanded exponentially and biological predictions based on this data become a possibility. The functional gene network is a quantitative way to integrate this data and a useful framework for making biological predictions. This study demonstrates that functional networks capture real biological insight and uses the network to predict both subcellular protein localization and the phenotypic outcome of gene knockouts. Furthermore, I use the functional network to evaluate genetic modules shared between diverse organisms that lead to orthologous phenotypes, many that are non-obvious. I show that the successful predictions of the functional network have broad applicability and implications that range from the design of large-scale biological experiments to the discovery of genes with potential roles in human disease.Institute for Cellular and Molecular Biolog

YeastWeb: a workset-centric web resource for gene family analysis in yeast

<p>Abstract</p> <p>Background</p> <p>Currently, a number of yeast genomes with different physiological features have been sequenced and annotated, which provides invaluable information to investigate yeast genetics, evolutionary mechanism, structure and function of gene families.</p> <p>Description</p> <p>YeastWeb is a novel database created to provide access to gene families derived from the available yeast genomes by assigning the genes into putative families. It has many useful features that complement existing databases, such as SGD, CYGD and Génolevures: 1) Detailed computational annotation was conducted with each entry with InterProScan, EMBOSS and functional/pathway databases, such as GO, COG and KEGG; 2) A well established user-friendly environment was created to allow users to retrieve the annotated genes and gene families using functional classification browser, keyword search or similarity-based search; 3) Workset offers users many powerful functions to manage the retrieved data efficiently, associate the individual items easily and save the intermediate results conveniently; 4) A series of comparative genomics and molecular evolution analysis tools are neatly implemented to allow users to view multiple sequence alignments and phylogenetic tree of gene families. At present, YeastWeb holds the gene families clustered from various MCL inflation values from a total of 13 available yeast genomes.</p> <p>Conclusions</p> <p>Given the great interest in yeast research, YeastWeb has the potential to become a useful resource for the scientific community of yeast biologists and related researchers investigating the evolutionary relationship of yeast gene families. YeastWeb is available at <url>http://centre.bioinformatics.zj.cn/Yeast/</url>.</p

Connecting Mutations of the RNA Polymerase II C-Terminal Domain to Complex Phenotypic Changes Using Combined Gene Expression and Network Analyses

The C-terminal domain (CTD) of the largest subunit in DNA-dependent RNA polymerase II (RNAP II) is essential for mRNA synthesis and processing, through coordination of an astounding array of protein-protein interactions. Not surprisingly, CTD mutations can have complex, pleiotropic impacts on phenotype. For example, insertions of five alanine residues between CTD diheptads in yeast, which alter the CTD's overall tandem structure and physically separate core functional units, dramatically reduce growth rate and result in abnormally large cells that accumulate increased DNA content over time. Patterns by which specific CTD-protein interactions are disrupted by changes in CTD structure, as well as how downstream metabolic pathways are impacted, are difficult to target for direct experimental analyses. In an effort to connect an altered CTD to complex but quantifiable phenotypic changes, we applied network analyses of genes that are differentially expressed in our five alanine CTD mutant, combined with established genetic interactions from the Saccharomyces cerevisiae Genome Database (SGD). We were able to identify candidate genetic pathways, and several key genes, that could explain how this change in CTD structure leads to the specific phenotypic changes observed. These hypothetical networks identify links between CTD-associated proteins and mitotic function, control of cell cycle checkpoint mechanisms, and expression of cell wall and membrane components. Such results can help to direct future genetic and biochemical investigations that tie together the complex impacts of the CTD on global cellular metabolism

yApoptosis: yeast apoptosis database

In the past few years, programmed cell death (PCD) has become a popular research area due to its fundamental aspects and its links to human diseases. Yeast has been used as a model for studying PCD, since the discovery of morphological markers of apoptotic cell death in yeast in 1997. Increasing knowledge in identification of components and molecular pathways created a need for organization of information. To meet the demands from the research community, we have developed a curated yeast apoptosis database, yApoptosis. The database structurally collects an extensively curated set of apoptosis, PCD and related genes, their genomic information, supporting literature and relevant external links. A web interface including necessary functions is provided to access and download the data. In addition, we included several networks where the apoptosis genes or proteins are involved, and present them graphically and interactively to facilitate rapid visualization. We also promote continuous inputs and curation by experts. yApoptosis is a highly specific resource for sharing information online, which supports researches and studies in the field of yeast apoptosis and cell death