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The exponentially increasing volume of scientific information is threatening to overwhelm the budgets and storage capacities of the world's academic libraries. At the same time, new information technologies are poised to revolutionize the publication infrastructure of science. In evaluating these new technologies we must pay attention to their scalability properties: how well will they perform as the amount of information they are handling increases by orders of magnitude? This article identifies some scalability criteria and evaluates some of the current information technologies with respect to them. It ends by suggesting future directions for these technologies.

The contents of the E. coli Genetic Stock Center database and the availability in electronic form of the subset of information most relevant to sequence databases are described. The database uses the long-standing Stock Center records (developed and curated by Dr B.J.Bachmann) in describing genotypes of mutant derivatives of E.coli K-12 in terms of alleles, structural mutations, mating type, and plasmids as well as the derivation, names and originators of the strain, and references. The database includes descriptions of mutations, mutation properties, genes, gene properties, and gene products, with EC number identifiers for enzymes. Sequence information is not included, but entries refer to sequence database accession numbers for sequenced regions. A gene is described as a subtype of a more general category of chromosome interval called Site. Since sites are used to describe any chromosomal interval, mapping information is associated with sites. Alleles are described as mutations of those sites and they are not primary map objects, but inherit map position information from the corresponding site description. The database design is intended to preserve richness of detail where it is known and uncertainty of measurements or information as it occurs in order to represent the stock center records as accurately as possible.

We present a computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other. Such sets are referred to as protein communities and are likely to represent functional modules. Preferential interaction between module members is quantified via an analytical framework based on a network null model known as the random graph with given expected degrees. Based on this framework, the concept of local protein community is generalized to that of community of communities. Protein communities and higher-level structures are extracted from two yeast protein interaction data sets and a network of published interactions between human proteins. The high level structures obtained with the human network correspond to broad biological concepts such as signal transduction, regulation of gene expression, and intercellular communication. Many of the obtained human communities are enriched, in a statistically significant way, for proteins having no clear orthologs in lower organisms. This indicates that the extracted modules are quite coherent in terms of function.

Motivation:The development of experimental methods for genome scale analysis of molecular interaction networks has made possible new approaches to inferring protein function. This paper describes a method of assigning functions based on a probabilistic analysis of graph neighborhoods in a protein-protein interaction network. The method exploits the fact that graph neighbors are more likely to share functions than nodes which are not neighbors. A binomial model of local neighbor function labeling probability is combined with a Markov random field propagation algorithm to assign function probabilities for proteins in the network. Results: We applied the method to a protein-protein interaction dataset for the yeast Saccharomyces cerevisiae using the Gene Ontology (GO) terms as function labels. The method reconstructed known GO term assignments with high precision, and produced putative GO assignments to 320 proteins that currently lack GO annotation, which represents about 10% of the unlabeled proteins in S. cerevisiae. Availability: Source code available upon request. Results available athttp://genomics10.bu.edu/netmark Contact: sletovsky@aol.com Keywords: protein–protein interaction, protein function prediction, gene ontology, Markov Random fields *To whom correspondence should be addressed.

A description is given of CPU, a program analysis tool that converts programs into formal specifications. CPU takes as input a program plus a knowledge base of programming plans and finds instances of plans in the code. A technique called transformational analysis is used in which plans that are recognized in the code are replaced by descriptions of their goals. Both procedural plans and data-structuring plans can be recognized. The result of a transformational analysis is a hierarchical derivation of the program, where the topmost layer constitutes a formal specification for the input program, the bottommost layer is the original code, and the intermediate layers denote plans that were recognized in the program. This derivation can be used to generate summaries of the code and to answer questions about it. >

The Genome Database (GDB, http://www.gdb.org ) is a public repository of data on human genes, clones, STSs, polymorphisms and maps. GDB entries are highly cross-linked to each other, to literature citations and to entries in other databases, including the sequence databases, OMIM, and the Mouse Genome Database. Mapping data from large genome centers and smaller mapping efforts are added to GDB on an ongoing basis. The database can be searched by a variety of methods, ranging from keyword searches to complex queries. Major functionality extensions in the last year include the ongoing computation of integrated human genome maps, called Comprehensive Maps, and the use of those maps to support positional queries and graphic displays. The capabilities of the GDB map viewer (Mapview) have been extended to include map printing and the graphical display of ad hoc query results. The HUGO Nomenclature Committee continues to curate the proposed and official gene symbols and related data in collaboration with GDB. As genome research shifts its emphasis from mapping to sequencing and functional analysis, the scope of the GDB schema is being extended. We are in the process of adding representations of gene function and expression, and improving our representation of human polymorphism and mutation.

Scientific databases are used to accession objects representing the results of scientific inquiry, such as genes and DNA sequences. These objects must have stable identifiers that can be used as references in scientific papers and other databases. The requirement for stable object identifiers, however, conflicts with the tendency of scientific data to evolve over time. We present in this paper version management facilities that allow scientific databases to achieve a balance between stable object identifiers and evolving data.

Gene mapping assigns chromosomal coordinates to genetic loci based on analysis of fragmentary ordering and metric data. In assembling genetic maps, geneticists use rules of inference to derive new facts about order and distance between loci from experimentally derived conclusions about order and distance. They construct comprehensive maps by merging related sets of data and resolving conflicts between them. In this article we describe software that formalizes and automates some of these rules of inference to yield a useful map construction utility called CPROP.

This paper reports on an empirical study of the cognitive processes involved in program comprehension. Verbal protocols were gathered from professional programmers as they were engaged in a program-understanding task. Based on analysis of these protocols, several types of interesting cognitive events were identified. These include asking questions and conjecturing facts about the code. We describe these event types and use them to derive a computational model of the programmers' mental processes.

A maintainer's understanding can go awry when it is based on purely local clues. How can we spell out the intentions behind a piece of code?