Elizabeth Shoop

Microarrays for global expression constructed with a low redundancy set of 27,500 sequenced cDNAs representing an array of developmental stages and physiological conditions of the soybean plant

BackgroundMicroarrays are an important tool with which to examine coordinated gene expression. Soybean (Glycine max) is one of the most economically valuable crop species in the world food supply. In order to accelerate both gene discovery as well as hypothesis-driven research in soybean, global expression resources needed to be developed. The applications of microarray for determining patterns of expression in different tissues or during conditional treatments by dual labeling of the mRNAs are unlimited. In addition, discovery of the molecular basis of traits through examination of naturally occurring variation in hundreds of mutant lines could be enhanced by the construction and use of soybean cDNA microarrays.ResultsWe report the construction and analysis of a low redundancy unigene set of 27,513 clones that represent a variety of soybean cDNA libraries made from a wide array of source tissue and organ systems, developmental stages, and stress or pathogen-challenged plants.The set was assembled from the 5 sequence data of the cDNA clones using cluster analysis programs. The selected clones were then physically reracked and sequenced at the 3 end. In order to increase gene discovery from immature cotyledon libraries that contain abundant mRNAs representing storage protein gene families, we utilized a high density filter normalization approach to preferentially select more weakly expressed cDNAs. All 27,513 cDNA inserts were amplified by polymerase chain reaction. The amplified products, along with some repetitively spotted control or choice clones, were used to produce three 9,728-element microarrays that have been used to examine tissue specific gene expression and global expression in mutant isolines.ConclusionsGlobal expression studies will be greatly aided by the availability of the sequence-validated and low redundancy cDNA sets described in this report. These cDNAs and ESTs represent a wide array of developmental stages and physiological conditions of the soybean plant. We also demonstrate that the quality of the data from the soybean cDNA microarrays is sufficiently reliable to examine isogenic lines that differ with respect to a mutant phenotype and thereby to define a small list of candidate genes potentially encoding or modulated by the mutant phenotype.

Isolation of replication-competent molecular clones of visna virus

Visna virus is the prototypic member of a subfamily of retroviruses responsible for slow infections of animals and humans. As a part of our investigation of the functions of viral gene products in virus replication, we have isolated three infectious molecular clones and determined the complete nucleotide sequences of two of the clones. We have also characterized the progeny of the biologically cloned viral stocks and of the infectious clones and document considerable heterogeneity in plaque size and antigenic phenotype of the former that is reduced to near homogeneity in the progeny of the infectious clones. It thus should now be possible to trace the emergence of antigenic variants of visna virus as well as ascribe defined functions to structural and regulatory genes of the virus in determining neurovirulence and the slow tempo of infection.

Flexible information visualization of multivariate data from biological sequence similarity searches

Information visualization faces challenges presented by the need to represent abstract data and the relationships within the data. Previously, we presented a system for visualizing similarities between a single DNA sequence and a large database of other DNA sequences (E.H. Chi et al., 1995). Similarity algorithms generate similarity information in textual reports that can be hundreds or thousands of pages long. Our original system visualized the most important variables from these reports. However, the biologists we work with found this system so useful they requested visual representations of other variables. We present an enhanced system for interactive exploration of this multivariate data. We identify a larger set of useful variables in the information space. The new system involves more variables, so it focuses on exploring subsets of the data. We present an interactive system allowing mapping of different variables to different axes, incorporating animation using a time axis, and providing tools for viewing subsets of the data. Detail-on-demand is preserved by hyperlinks to the analysis reports. We present three case studies illustrating the use of these techniques. The combined technique of applying a time axis with a 3D scatter plot and query filters to visualization of biological sequence similarity data is both powerful and novel.

MetaFam: a unified classification of protein families. I. Overview and statistics

MOTIVATIONnProtein sequence classification is becoming an increasingly important means of organizing the voluminous data produced by large-scale genome sequencing projects. At present, there are several independent classification methods. To aid the general classification effort, we have created a unified protein family resource, MetaFam. MetaFam is a protein family classification built upon 10 publicly-accessible protein family databases (Blocks + DOMO, Pfam, PIR-ALN, PRINTS, PROSITE, ProDom, PROTOMAP, SBASE, and SYSTERS). MetaFams family supersets, as we call them, are created automatically using set-theory to compare families among the databases. Families of one database are matched to those in another when the intersection of their members exceeds all other possible family pairings between the two databases. Pairwise family matches are drawn together transitively to create a new list of protein family supersets.nnnRESULTSnMetaFam family supersets have several useful features: (1) each superset contains more members than the families from which it is composed, because each of the component family databases only works with a subset of our full non-redundant set of proteins; (2) conflicting assignments can be pinpointed quickly, since our analysis identifies individual members that are in conflict with the majority consensus; (3) family descriptions that are absent from automated databases can frequently be assigned; (4) statistics have been computed comparing domain boundaries, family size distributions, and overall quality of MetaFam supersets; (5) the supersets have been loaded into a relational database to allow for complex queries and visualization of the connections among families in a superset and the consensus of individual domain members; and (6) the quality of individual supersets has been assessed using numerous quantitative measures such as family consistency, connectedness, and size. We anticipate this new resource will be particularly useful to genomic database curators.

Visualization of biological sequence similarity search results

Biological sequence similarity analysis presents visualization challenges, primarily because of the massive amounts of discrete, multi dimensional data. Genomic data generated by molecular biologists is analyzed by algorithms that search for similarity to known sequences in large genomic databases. The output from these algorithms can be several thousand pages of text, and is difficult to analyze because of its length and complexity. We developed and implemented a novel graphical representation for sequence similarity search results, which visually reveals features that are difficult to find in textual reports. The method opens new possibilities in the interpretation of this discrete, multidimensional data by enabling interactive investigation of the graphical representation.

MetaFam: a unified classification of protein families. II. Schema and query capabilities

MOTIVATIONnProtein sequence and family data is accumulating at such a rapid rate that state-of-the-art databases and interface tools are required to aid curators with their classifications. We have designed such a system, MetaFam, to facilitate the comparison and integration of public protein sequence and family data. This paper presents the global schema, integration issues, and query capabilities of MetaFam.nnnRESULTSnMetaFam is an integrated data warehouse of information about protein families and their sequences. This data has been collected into a consistent global schema, and stored in an Oracle relational database. The warehouse implementation allows for quick removal of outdated data sets. In addition to the relational implementation of the primary schema, we have developed several derived tables that enable efficient access from data visualization and exploration tools. Through a series of straightforward SQL queries, we demonstrate the usefulness of this data warehouse for comparing protein family classifications and for functional assignment of new sequences.

The MetaFam Server: a comprehensive protein family resource

MetaFam is a comprehensive relational database of protein family information. This web-accessible resource integrates data from several primary sequence and secondary protein family databases. By pooling together the information from these disparate sources, MetaFam is able to provide the most complete protein family sets available. Users are able to explore the interrelationships among these primary and secondary databases using a powerful graphical visualization tool, MetaFamView. Additionally, users can identify corresponding sequence entries among the sequence databases, obtain a quick summary of corresponding families (and their sequence members) among the family databases, and even attempt to classify their own unassigned sequences. Hypertext links to the appropriate source databases are provided at every level of navigation. Global family database statistics and information are also provided. Public access to the data is available at http://metafam.ahc.umn.edu/.

TableView: portable genomic data visualization

UNLABELLEDnTableView is a generalized scientific visualization program for exploration of various biological data, including EST, SAGE, microarray and annotation data. Written in Java, TableView is portable, is easily used together with other software including DBMSs and is versatile enough to be applied to any tabular datannnAVAILABILITYnTableView is freely available at: http://ccgb.umn.edu/software/java/apps/TableView/.

Implementation and testing of an automated EST processing and similarity analysis system

Expressed sequence tag (EST) sequencing projects are being undertaken in an effort to identify the function of as many genes as possible from entire genomes. Putative function can be determined by analyzing the similarity of the ESTs to sequences in the public databases. We are involved in a long-term project to research and develop database technology to store and analyze ESTs for Arabidopsis thaliana. The massive amounts of ESTs being produced through automated sequencing technologies necessitates the automated processing and similarity analysis of the ESTs. This paper describes a complete software system that takes ESTs from a sequencing machine, analyzes them for quality, and searches in public databases of previously known sequences. Automating the processing and analysis of the several thousand ESTs produced to date by the Michigan State University, Arabidopsis cDNA Sequencing Project has improved the quality of the EST data and the speed at which ESTs can be entered in the public databases.<<ETX>>

Novel visualization method for biological sequence similarity reports

Previously, we presented a system called Alignment Viewer that uses information visualization techniques to visualize similarities between a single DNA sequence and a large database of other sequences [Chi et al., IEEE Visualization ’95, pp. 44–51, IEEE CS (1995); Chi et al., IEEE Visualization ’96, pp. 133–140 and 477, IEEE CS (1996)]. In this paper, we extend, summarize, and describe the system using several interesting case studies. We present our comb glyph technique for visualizing alignments between sequences. In this paper, we also extend the original system by incorporating computational steering, and the visualization of differences between data sets. The case studies and the new extended system present our approach of extracting significant relationships in the biological data set.