8759270
Organelle DB: a cross-species database of protein localization and function
To efficiently utilize the growing body of available protein localization data, we have developed Organelle DB, a web-accessible database cataloging more than 25 000 proteins from nearly 60 organelles, subcellular structures and protein complexes in 154 organisms spanning the eukaryotic kingdom. Organelle DB is the first on-line resource devoted to the identification and presentation of eukaryotic proteins localized to organelles and subcellular structures. As such, Organelle DB is a strong resource of data from the human proteome as well as from the major model organisms Saccharomyces cerevisiae, Arabidopsis thaliana, Drosophila melanogaster, Caenorhabditis elegans and Mus musculus. In particular, Organelle DB is a central repository of yeast data, incorporating results—and actual fluorescent images—from ongoing large-scale studies of protein localization in S.cerevisiae. Each protein in Organelle DB is presented with its sequence and, as available, a detailed description of its function; functions were extracted from relevant model organism databases, and links to these databases are provided within Organelle DB. To facilitate data interoperability, we have annotated all protein localizations using vocabulary from the Gene Ontology consortium. We also welcome new data for inclusion in Organelle DB, which may be freely accessed at http://organelledb.lsi.umich.edu.
21095960
The TRIPLES database: a community resource for yeast molecular biology
TRIPLES is a web-accessible database of TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces cerevisiae-a relational database housing nearly half a million data points generated from an ongoing study using large-scale transposon mutagenesis to characterize gene function in yeast. At present, TRIPLES contains three principal data sets (i.e. phenotypic data, protein localization data and expression data) for over 3500 annotated yeast genes as well as several hundred non-annotated open reading frames. In addition, the TRIPLES web site provides online order forms linked to each data set so that users may request any strain or reagent generated from this project free of charge. In response to user requests, the TRIPLES web site has undergone several recent modifications. Our localization data have been supplemented with approximately 500 fluorescent micrographs depicting actual staining patterns observed upon indirect immunofluorescence analysis of indicated epitope-tagged proteins. These localization data, as well as all other data sets within TRIPLES, are now available in full as tab-delimited text. To accommodate increased reagent requests, all orders are now cataloged in a separate database, and users are notified immediately of order receipt and shipment. Also, TRIPLES is one of five sites incorporated into the new functional analysis tool Function Junction provided by the Saccharomyces Genome Database. TRIPLES may be accessed from the Yale Genome Analysis Center (YGAC) homepage at http://ygac.med.yale.edu.
23929618
Organelle DB: an updated resource of eukaryotic protein localization and function
Organelle DB () is a web-accessible relational database presenting a supplemented catalog of organelle-localized proteins and major protein complexes. Since its release in 2004, Organelle DB has grown by 20% to encompass over 30 000 proteins from 138 eukaryotic organisms. Each protein in Organelle DB is presented with its subcellular localization, primary sequence and a detailed description of its function, as available. All records in Organelle DB have been annotated using controlled vocabulary from the Gene Ontology consortium. Protein localization data are inherently visual, and Organelle DB is a significant repository of biological images, housing 1500 micrographs of yeast cells carrying stained proteins. Furthermore, we report here the development of Organelle View, an extension of Organelle DB for the interactive visualization of organelles and subcellular structures in the budding yeast Saccharomyces cerevisiae. Organelle View offers a dimensional representation of a yeast cell; users can search Organelle View for proteins of interest, and the organelles housing these proteins will be highlighted in the cell image. Among other applications, Organelle View may serve as an educational aid engaging introductory biology students through a visually ‘fun’ interface. Organelle View can be accessed from the Organelle DB home page or directly at .
14831931
TRIPLES: a database of gene function in Saccharomyces cerevisiae
Using a novel multipurpose mini-transposon, we have generated a collection of defined mutant alleles for the analysis of disruption phenotypes, protein localization, and gene expression in Saccharomyces cerevisiae. To catalog this unique data set, we have developed TRIPLES, a Web-accessible database of TRansposon-Insertion Phenotypes, Localization and Expression in Saccharomyces. Encompassing over 250 000 data points, TRIPLES provides convenient access to information from nearly 7800 transposon-mutagenized yeast strains; within TRIPLES, complete data reports of each strain may be viewed in table format, or if desired, downloaded as tab-delimited text files. Each report contains external links to corresponding entries within the Saccharomyces Genome Database and International Nucleic Acid Sequence Data Library (GenBank). Unlike other yeast databases, TRIPLES also provides on-line order forms linked to each clone report; users may immediately request any desired strain free-of-charge by submitting a completed form. In addition to presenting a wealth of information for over 2300 open reading frames, TRIPLES constitutes an important medium for the distribution of useful reagents throughout the yeast scientific community. Maintained by the Yale Genome Analysis Center, TRIPLES may be accessed at http://ycmi.med.yale.edu/ygac/triples.htm
31048080
Advertising data analysis using rough sets model
This study explores the use of rough-set methods for marketing decision support systems in the retail business. A tutorial presentation of Rough Set Data Analysis (RSDA) in the context of knowledge discovery from time series databases is given. We show how an RSDA model can be used to develop a marketing decision support system which can capture the complex relationships between marketing factors, such as advertising and promotion, and the total impact on sales levels in order to find influential advertising strategies. This information is used by the business manager to make faster and better strategy decisions for the business to survive in the rapidly changing and competitive environments. The data set used for RSDA application example contains weekly investments in different media categories: TV, radio, cinema, morning press, evening press, popular press, special interest press, and outdoor posters; for seven makes of cars in the Swedish market.
8388405
Distributed Sampling for Dense Sensor Networks: A "Bit-Conservation Principle"
We address the problem of deterministic oversampling of bandlimited sensor fields in a distributed communication-constrained processing environment, where it is desired for a central intelligent unit to reconstruct the sensor field to maximum pointwise accuracy.We show, using a dither-based sampling scheme, that is is possible to accomplish this using minimal inter-sensor communication with the aid of a multitude of low-precision sensors. Furthermore, we show the feasibility of having a flexible tradeoff between the average oversampling rate and the Analog to Digital (A/D) quantization precision per sensor sample with respect to achieving exponential accuracy in the number of bits per Nyquist-period, thereby exposing a key underpinning "conservation of bits" principle. That is, we can distribute the bit budget per Nyquist-period along the amplitude-axis (precision of A/D converter) and space (or time or space-time) using oversampling in an almost arbitrary discrete-valued manner, while retaining the same reconstruction error decay profile. Interestingly this oversampling is possible in a highly localized communication setting, with only nearest-neighbor communication, making it very attractive for dense sensor networks operating under stringent inter-node communication constraints. Finally we show how our scheme incorporates security as a by-product due to the presence of an underlying dither signal which can be used as a natural encryption device for security. The choice of the dither function enhances the security of the network.
8488383
Sorting and selection with structured costs
The study of the effect of priced information on basic algorithmic problems was initiated by M. Charikar et al. (2000). The authors continue the study of sorting and selection in the priced comparison model, i.e., when each comparison has an associated cost, and answer some of the open problems suggested by Charikar et al. If the comparison costs are allowed to be arbitrary, we show that one cannot get good approximation ratios. A different way to assign costs is based on the idea that one can distill out an intrinsic value for each item being compared, such that the cost of comparing two elements is some "well-behaved" or "structured" function of their values. We feel that most practical applications will have some structured cost property. The authors study the problems of sorting and selection (which includes finding the maximum and the median) in the structured cost model. We get a variety of approximation results for these problems, depending on the restrictions we put on the structured costs. We show that it is possible to get much improved results with the structured cost model than the case when we do not have any assumptions on comparison costs.
1630993
Join-distinct aggregate estimation over update streams
There is growing interest in algorithms for processing andquerying continuous data streams (i.e., data that is seenonly once in a fixed order) with limited memory resources.Providing (perhaps approximate) answers to queries over suchstreams is a crucial requirement for many application environments;examples include large IP network installations where performancedata from different parts of the network needs to be continuouslycollected and analyzed.
The ability to estimate the number of distinct (sub)tuples inthe result of a join operation correlating two data streams (i.e.,the cardinality of a projection with duplicate elimination over ajoin) is an important requirement for several data-analysisscenarios. For instance, to enable real-time traffic analysis andload balancing, a network-monitoring application may need toestimate the number of distinct (<i>source</i>,destination) IP-address pairs occurring in the stream of IP packetsobserved by router <i>R</i><inf>1</inf>,where the source address is also seen in packets routed through adifferent router <i>R</i><inf>2</inf>.Earlier work has presented solutions to the individual problems ofdistinct counting and join-size estimation (without duplicateelimination) over streams. These solutions, however, arefundamentally different and extending or combining them to handleour more complex "Join-Distinct" estimation problem is far fromobvious. In this paper, we propose the <i>first</i>space-efficient algorithmic solution to the general Join-Distinctestimation problem over continuous data streams (our techniques canactually handle general <i>update streams</i>comprising tuple deletions as well as insertions). Our estimatorsare probabilistic in nature and rely on novel algorithms forbuilding and combining a new class of hash-based synopses (termed"JD <i>sketches</i>") for individual update streams. Wedemonstrate that our algorithms can provide low error,high-confidence Join-Distinct estimates using only small space andsmall processing time per update. In fact, we present lower boundsshowing that the space usage of our estimators is within smallfactors of the best possible for the Join-Distinct problem.Preliminary experimental results verify the effectiveness of ourapproach.
29316025
Optimizing the costs of hierarchical quorum consensus
We study the problem of how to minimize the cost of maintaining consistency among at least N copies of an object in an enviroment where the mix of read and write operations can vary. Quorum consensus requires that read and write operations must assemble appropriate quorums before an operation can succeed. The cost of an operation is proportional to the size of a quorum, and the objective is obviously to minimize the cost while still maintaining consistency. It is known that the quorum size can be reduced by organizing a number of copies into logical structures such as grids and hierarchies. In this paper, we show (a) how methods based on grids and hierarchies can be treated in a common framework, and (b) how these hierarchies can be optimized so as to minimize the cost of consensus. Of course, the optimal solution depends upon the mix of read and write operations that is present. Consequently, given N copies of an object and a ratio of write operations F, our algorithms determine the optimal values for the number of levels in the hierarchy and the read/write quorum sizes at each level. The algorithms, which run in O(N1.63) and O(N2) time, were tested, and the results are reported and discussed.