Updates to maintain a state-of-the art reconstruction of the yeast metabolic network are essential to reflect our understanding of yeast metabolism and functional organization, to eliminate any inaccuracies identified in earlier iterations, to improve predictive accuracy and to continue to expand into novel subsystems to extend the comprehensiveness of the model. Here, we present version 6 of the consensus yeast metabolic network (Yeast 6) as an update to the community effort to computationally reconstruct the genome-scale metabolic network of Saccharomyces cerevisiae S288c. Yeast 6 comprises 1458 metabolites participating in 1888 reactions, which are annotated with 900 yeast genes encoding the catalyzing enzymes. Compared with Yeast 5, Yeast 6 demonstrates improved sensitivity, specificity and positive and negative predictive values for predicting gene essentiality in glucose-limited aerobic conditions when analyzed with flux balance analysis. Additionally, Yeast 6 improves the accuracy of predicting the likelihood that a mutation will cause auxotrophy. The network reconstruction is available as a Systems Biology Markup Language (SBML) file enriched with Minimium Information Requested in the Annotation of Biochemical Models (MIRIAM)-compliant annotations. Small- and macromolecules in the network are referenced to authoritative databases such as Uniprot or ChEBI. Molecules and reactions are also annotated with appropriate publications that contain supporting evidence. Yeast 6 is freely available at http://yeast.sf.net/ as three separate SBML files: a model using the SBML level 3 Flux Balance Constraint package, a model compatible with the MATLAB® COBRA Toolbox for backward compatibility and a reconstruction containing only reactions for which there is experimental evidence (without the non-biological reactions necessary for simulating growth). Database URL: http://yeast.sf.net/

Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight clusters that focus on their cores or wide clusters that overlap and contain all possibly relevant genes. In this paper, a new clustering paradigm is proposed. In this paradigm, all three eventualities of a gene being exclusively assigned to a single cluster, being assigned to multiple clusters, and being not assigned to any cluster are possible. These possibilities are realised through the primary novelty of the introduction of tunable binarization techniques. Results from multiple clustering experiments are aggregated to generate one fuzzy consensus partition matrix (CoPaM), which is then binarized to obtain the final binary partitions. This is referred to as Binarization of Consensus Partition Matrices (Bi-CoPaM). The method has been tested with a set of synthetic datasets and a set of five real yeast cell-cycle datasets. The results demonstrate its validity in generating relevant tight, wide, and complementary clusters that can meet requirements of different gene discovery studies.

This communication describes the consensus multi-locus typing scheme established by the Cryptococcal Working Group I (Genotyping of Cryptococcus neoformans and C. gattii) of the International Society for Human and Animal Mycology (ISHAM) using seven unlinked genetic loci for global strain genotyping. These genetic loci include the housekeeping genes CAP59,GPD1, LAC1, PLB1, SOD1, URA5 and the IGS1 region. Allele and sequence type information are accessible at http://www.mlst.net/ .

BACKGROUND: Identifying all protein complexes in an organism is a major goal of systems biology. In the past 18 months, the results of two genome-scale tandem affinity purification-mass spectrometry (TAP-MS) assays in yeast have been published, along with corresponding complex maps. For most complexes, the published data sets were surprisingly uncorrelated. It is therefore useful to consider the raw data from each study and generate an accurate complex map from a high-confidence data set that integrates the results of these and earlier assays.
RESULTS: Using an unsupervised probabilistic scoring scheme, we assigned a confidence score to each interaction in the matrix-model interpretation of the large-scale yeast mass-spectrometry data sets. The scoring metric proved more accurate than the filtering schemes used in the original data sets. We then took a high-confidence subset of these interactions and derived a set of complexes using MCL. The complexes show high correlation with existing annotations. Hierarchical organization of some protein complexes is evident from inter-complex interactions.
CONCLUSIONS: We demonstrate that our scoring method can generate an integrated high-confidence subset of observed matrix-model interactions, which we subsequently used to derive an accurate map of yeast complexes. Our results indicate that essentiality is a product of the protein complex rather than the individual protein, and that we have achieved near saturation of the yeast high-abundance, rich-media-expressed \"complex-ome.\"

BACKGROUND: Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods give results which are dependent on the initialization of the algorithm. Therefore, it is difficult to assess the significance of the results. We have developed a consensus clustering algorithm, where the final result is averaged over multiple clustering runs, giving a robust and reproducible clustering, capable of capturing small signal variations. The algorithm preserves valuable properties of hierarchical clustering, which is useful for visualization and interpretation of the results.
RESULTS: We show for the first time that one can take advantage of multiple clustering runs in DNA microarray analysis by collecting re-occurring clustering patterns in a co-occurrence matrix. The results show that consensus clustering obtained from clustering multiple times with Variational Bayes Mixtures of Gaussians or K-means significantly reduces the classification error rate for a simulated dataset. The method is flexible and it is possible to find consensus clusters from different clustering algorithms. Thus, the algorithm can be used as a framework to test in a quantitative manner the homogeneity of different clustering algorithms. We compare the method with a number of state-of-the-art clustering methods. It is shown that the method is robust and gives low classification error rates for a realistic, simulated dataset. The algorithm is also demonstrated for real datasets. It is shown that more biological meaningful transcriptional patterns can be found without conservative statistical or fold-change exclusion of data.
BACKGROUND: Matlab source code for the clustering algorithm ClusterLustre, and the simulated dataset for testing are available upon request from T.G. and O.W.

A panel of 86 different Candida albicans isolates was subjected to multilocus sequence typing (MLST) in two laboratories to obtain sequence data for 10 published housekeeping gene fragments. Analysis of data for all possible combinations of five, six, seven, eight, and nine of the fragments showed that a set comprising the fragments AAT1a, ACC1, ADP1, MPIb, SYA1, VPS13, and ZWF1b was the smallest that yielded 86 unique diploid sequence types for the 86 isolates. This set is recommended for future MLST with C. albicans.

Consensus pattern and matrix-based searches designed to predict cis-acting transcriptional regulatory sequences have historically been subject to large numbers of false positives. We sought to decrease false positives by incorporating expression profile data into a consensus pattern-based search method. We have systematically analyzed the expression phenotypes of over 6000 yeast genes, across 121 expression profile experiments, and correlated them with the distribution of 14 known regulatory elements over sequences upstream of the genes. Our method is based on a metric we term probabilistic element assessment (PEA), which is a ranking of potential sites based on sequence similarity in the upstream regions of genes with similar expression phenotypes. For eight of the 14 known elements that we examined, our method had a much higher selectivity than a naïve consensus pattern search. Based on our analysis, we have developed a web-based tool called PROSPECT, which allows consensus pattern-based searching of gene clusters obtained from microarray data.

Mitochondrial transport proteins (MTP) typically are homodimeric with a 30-kDa subunit with six transmembrane helices. The subunit possesses a sequence motif highly similar to Pro X Asp/Glu X X Lys/Arg X Arg within each of its three similar 10-kDa segments. Four (YNL083W, YFR045W, YPR021C, YDR470C) of the 35 yeast (S. cerevisiae) MTP genes were resequenced since the masses of their proteins deviate significantly from the typical 30 kDa. We now find these four proteins to have 545, 285, 902, and 502 residues, respectively. Together with only four other MTPs, the sequences of YPR021C and YDR470C show substitutions of some of the five residues that are absolutely conserved among the 12 MTPs with identified transport function and 17 other MTPs. We do now find these five consensus residues also in the new sequences of YNL083W and YFR045W. Additional analyses of the 35 yeast MTPs show that the location of transmembrane helix sequences do not correlate with the general consensus residues of the MTP family; protein segments connecting the six transmembrane helices and facing the intermembrane space are not uniformly short (about 20 residues) or long (about 40 residues) when facing the matrix; most MTPs have at least one transmembrane helix for which the sum of the negative hydropathy values of all residues yields a very small negative value, suggesting a membrane location bordering polar faces of other transmembrane helices or a non-transmembrane location. The extra residues of the three large MTPs are hydrophilic and at the N-terminal. The 200-residue N-terminal segment of YNL083W has four putative Ca2+-binding sites. The 500-residue N-terminal segment of YPR021C shows sequence similarity to enzymes of nucleic acid metabolism. cDNA microarray data show that YNL083W is expressed solely during sporulation, while the expressions of YFR045W, YPR021C, and YDR470C are induced by various stress situations. These results also show that the 35 MTP genes are expressed under a rather diverse set of metabolic conditions that may help identify the function of the proteins. Interestingly, yeast two-hybrid screens, that will also be useful in identifying the function of MTPs, indicate that MIR1, AAC3, YOR100C, and YPR011C do interact with non-MTPs.

The majority of strains of Candida albicans can switch frequently and reversibly between two or more general phenotypes, a process now considered a putative virulence factor in this species. Candida albicans WO-1 switches frequently and reversibly between a white and an opaque phase, and this phenotypic transition is accompanied by the differential expression of white-phase-specific and opaque-phase-specific genes. In the opaque phase, cells differentially express the gene OP4, which encodes a putative protein 402 amino acids in length that contains a highly hydrophobic amino-terminal sequence and a carboxy-terminal sequence with a pI of 10.73. A series of deletion constructs fused to the Renilla reniformis luciferase was used to functionally characterize the OP4 promoter in order to investigate how this gene is differentially expressed in the white-opaque transition. An extremely strong 17-bp transcription activation sequence was identified between -422 and -404 bp. This sequence contained a MADS box consensus binding site, most closely related to the Mcm1 binding site of Saccharomyces cerevisiae. A number of point mutations generated in the MADS box consensus binding site as well as a complete deletion of the consensus site further demonstrated that it was essential for the activation of OP4 transcription in the opaque phase. Gel mobility shift assays with the 17-bp activation sequence identified three specific complexes which formed with both white- and opaque-phase cell extracts. Competition with a putative MADS box consensus binding site from the promoter of the coordinately regulated opaque-phase-specific gene PEP1 (SAP1) and the human MADS box consensus binding site for serum response factor demonstrated that one of the three complexes formed was specific to the OP4 sequence.

The two zinc-fingers of the Aspergillus nidulans repressor CreA recognize the consensus hexanucleotide 5\'-SYGGRG-3\'. We have determined all the CreA binding sites in a approximately 2 kb region upstream the ipnA gene. Our analysis shows that (i) CreA binds to certain consensus sites in a context-dependent manner; (ii) five non-consensus 6-bp sequences are also recognized by CreA; this non-canonical binding correlates with the presence of a second, neighbouring CreA binding site, suggesting that recognition of two linked sites stabilizes CreA binding. Our results suggest that the binding possibilities of CreA might be more complex than originally envisaged.