Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms

Abstract

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/), a scientific database of the molecular biology and genetics of the yeast Saccharomyces cerevisiae, has recently developed several new resources that allow the comparison and integration of information on a genome-wide scale, enabling the user not only to find detailed information about individual genes, but also to make connections across groups of genes with common features and across different species. The Fungal Alignment Viewer displays alignments of sequences from multiple fungal genomes, while the Sequence Similarity Query tool displays PSI-BLAST alignments of each S.cerevisiae protein with similar proteins from any species whose sequences are contained in the non-redundant (nr) protein data set at NCBI. The Yeast Biochemical Pathways tool integrates groups of genes by their common roles in metabolism and displays the metabolic pathways in a graphical form. Finally, the Find Chromosomal Features search interface provides a versatile tool for querying multiple types of information in SGD.

Npa1p is an essential trans-acting factor required for an early step in the assembly of 60S ribosomal subunits in Saccharomyces cerevisiae

“…A database search revealed no obvious protein motifs for Npa1p, but its first 693 amino acids seem to have a predicted structure related to proteins of the HEAT-repeat family such as the PR65A subunit of protein phosphatase 2A (Yeast Resource Center Informatics Platform, http:// www.yeastrc.org; Hazbun et al 2003) and a low homology with Tpd3p, the yeast PR65A protein. We could also identify potential homologs of yeast Npa1p in human, Drosophila, Arabidopsis, and other fungi (Sequence Similarity Query Tool, Saccharomyces Genome Database; Christie et al 2004). The strong sequence conservation suggests that Npa1p carries out an important, evolutionarily conserved function in all eukaryotes.…”

PubMiner: Machine Learning-Based Text Mining System for Biomedical Information Mining

“…A set of features of each component of the interaction are collected from public databases such as Saccharomyces Genome Database (SGD) [17] and database of Munich Information Center for Protein Sequences (MIPS) [18] and represented as a binary feature vector. An association rule discovery algorithm, Apriori [19] was used to extract the appropriate common feature set of interacting biological entities.…”

“…It includes MIPS, YPD and Y2H by Ito et al and Uetz et al, respectively [18]. Additionally, we use SGD [17] to collect more abundant feature set. Table 3 shows the statistics of interaction data for each data source and filtering with FDRF of Figure 4.…”

Adaptive Neural Network-Based Clustering of Yeast Protein–Protein Interactions

“…We constructed massive feature sets for each protein and interacting protein pairs from public protein description databases [11,12,15,16,17]. However, there exist also many features which have no information of their association with other proteins.…”

“…Here, we assume protein-protein interaction of yeast as feature-tofeature association of each interacting proteins. To analyze protein-protein interactions with respect to their interaction class with their feature association, we use as many features as possible from several major databases such as MIPS and SGD [11,12] to construct a rich feature vector for each protein interaction which is provided to the proposed clustering model. Here, we use the same approach of Rangarajan et al [13] for the design of clustering model and employ the feature selection filter of Yu et al [14] to reduce computational complexity and improve the overall clustering performance by eliminating non-informative features.…”