MIPS: a database for genomes and protein sequences

Mewes, Hans‐Werner; Heumann, Klaus G.; Kaps, A.; Mayer, Klaus; Pfeiffer, Friedhelm; Stöcker, Susanne; Frishman, Dmitrij

doi:10.1093/nar/27.1.44

Cited by 333 publications

(380 citation statements)

References 16 publications

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“…The first was generated by SGA, a large-scale screen 10 crossing 132 yeast gene deletion strains versus each of the ~4,700 available deletion strains 22 and resulting in 2,012 observed synthetic-lethal interactions and 2,113 synthetic-sick interactions. The second data source consisted of an additional 687 synthetic-lethal interactions culled from the literature and catalogued at the Munich Information Center for Protein Sequences (MIPS) 23 . The combined genetic network synthesizing these data consisted of 1,434 proteins (genes) linked by 4,812 synthetic-lethal interactions.…”

Section: Construction Of Genetic and Physical Networkmentioning

confidence: 99%

Systematic interpretation of genetic interactions using protein networks

2005

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Genetic interaction analysis, in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between-or within-pathway explanations encoded in the physical networks, covering ~40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than withinpathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.A major biological challenge is to interpret observed genetic interactions in a physical cellular context 1-3 . There are several major types of genetic interactions: synthetic-lethal interactions, in which mutations in two nonessential genes are lethal when combined; suppressor interactions, in which one mutation is lethal but when combined with a second, cell viability is restored; and an array of other effects such as enhancement and epistasis. Genetic interactions have been used extensively to shed light on pathway organization in model organisms [1][2][3][4] . In humans, genetic interactions are critical in linkage analysis of complex diseases 5 and in discovery of new pharmaceuticals 6 . Although genetic interactions are classically identified by mutant screens 7 , recent studies have applied systematic 'reverse' methods such as synthetic genetic arrays (SGA) 8 or synthetic lethal analysis by microarrays (SLAM) 9 to catalog ~4,000 synthetic-lethal and synthetic-sick interactions in Saccharomyces cerevisiae.Because of the high-throughput nature of SGA, discovery of new genetic interactions is largely automated. However, interpreting the functional significance of each result remains a relatively slow process. The problem is compounded by the large number of genetic interactions measured when screening one gene versus all others (~34 on average 10 ) as well as possible false positives if the interactions are not confirmed by tetrad or random spore analysis. Thus,

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Section: Construction Of Genetic and Physical Networkmentioning

confidence: 99%

Systematic interpretation of genetic interactions using protein networks

2005

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“…Furthermore, there are also a number of databases [i.e. MIPS (22), BIND (23) and DIP (24)] that have many manually curated interactions for yeast.…”

Section: Biological Applicationsmentioning

confidence: 99%

TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics

Zhu

Greenbaum

et al. 2004

Nucleic Acids Research

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Biological networks are a topic of great current interest, particularly with the publication of a number of large genome-wide interaction datasets. They are globally characterized by a variety of graph-theoretic statistics, such as the degree distribution, clustering coef®cient, characteristic path length and diameter. Moreover, real protein networks are quite complex and can often be divided into many sub-networks through systematic selection of different nodes and edges. For instance, proteins can be sub-divided by expression level, length, amino-acid composition, solubility, secondary structure and function. A challenging research question is to compare the topologies of subnetworks, looking for global differences associated with different types of proteins. TopNet is an automated web tool designed to address this question, calculating and comparing topological characteristics for different sub-networks derived from any given protein network. It provides reasonable solutions to the calculation of network statistics for sub-networks embedded within a larger network and gives simpli®ed views of a sub-network of interest, allowing one to navigate through it. After constructing TopNet, we applied it to the interaction networks and protein classes currently available for yeast. We were able to ®nd a number of potential biological correlations. In particular, we found that soluble proteins had more interactions than membrane proteins. Moreover, amongst soluble proteins, those that were highly expressed, had many polar amino acids, and had many alpha helices, tended to have the most interaction partners. Interestingly, TopNet also turned up some systematic biases in the current yeast interaction network: on average, proteins with a known functional classi®cation had many more interaction partners than those without. This phenomenon may re¯ect the incompleteness of the experimentally determined yeast interaction network.

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“…Using the MIPS database (Mewes et al, 1999), we asked whether the Yap1p-and Yap2p-dependent genes were enriched for particular functions. Cluster 31 (Yap1p dependent) was enriched for genes in the category "detoxification" (p ϭ 2.4 ϫ 10 Ϫ4 ).…”

Section: Identification Of Yap1p-and Yap2p-dependent Genesmentioning

confidence: 99%

Discrimination between Paralogs using Microarray Analysis: Application to the Yap1p and Yap2p Transcriptional Networks

Cohen

Pilpel

Mitra

et al. 2002

MBoC

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Ohno [Ohno, S. (1970) in Evolution by Gene Duplication, Springer, New York] proposed that gene duplication with subsequent divergence of paralogs could be a major force in the evolution of new gene functions. In practice the functional differences between closely related homologues produced by duplications can be subtle and difficult to separate experimentally. Here we show that DNA microarrays can distinguish the functions of two closely related homologues from the yeast Saccharomyces cerevisiae, Yap1p and Yap2p. Although Yap1p and Yap2p are both bZIP transcription factors involved in multiple stress responses and are 88% identical in their DNA binding domains, our work shows that these proteins activate nonoverlapping sets of genes. Yap1p controls a set of genes involved in detoxifying the effects of reactive oxygen species, whereas Yap2p controls a set of genes over represented for the function of stabilizing proteins. In addition we show that the binding sites in the promoters of the Yap1p-dependent genes differ from the sites in the promoters of Yap2p-dependent genes and we validate experimentally that these differences are important for regulation by Yap1p. We conclude that while Yap1p and Yap2p may have some overlapping functions they are clearly not redundant and, more generally, that DNA microarray analysis will be an important tool for distinguishing the functions of the large numbers of highly conserved genes found in all eukaryotic genomes.

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MIPS: a database for genomes and protein sequences

Cited by 333 publications

References 16 publications

Systematic interpretation of genetic interactions using protein networks

Systematic interpretation of genetic interactions using protein networks

TopNet: a tool for comparing biological sub-networks, correlating protein properties with topological statistics

Discrimination between Paralogs using Microarray Analysis: Application to the Yap1p and Yap2p Transcriptional Networks

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