Biomedical subjects

Susan M Bridges

Publications and source records attributed to Susan M Bridges.

4 recordsLinked to original sources

PubMed2006

AgBase: a unified resource for functional analysis in agriculture.

Analysis of functional genomics (transcriptomics and proteomics) datasets is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation. To facilitate systems biology in these species we have established the curated, web-accessible, public resource 'AgBase' (www.agbase.msstate.edu). We have improved the structural annotation of agriculturally important genomes by experimentally confirming the in vivo expression of electronically predicted proteins and by proteogenomic mapping. Proteogenomic data are available from the AgBase proteogenomics link. We contribute Gene Ontology (GO) annotations and we provide a two tier system of GO annotations for users. The 'GO Consortium' gene association file contains the most rigorous GO annotations based solely on experimental data. The 'Community' gene association file contains GO annotations based on expert community knowledge (annotations based directly from author statements and submitted annotations from the community) and annotations for predicted proteins. We have developed two tools for proteomics analysis and these are freely available on request. A suite of tools for analyzing functional genomics datasets using the GO is available online at the AgBase site. We encourage and publicly acknowledge GO annotations from researchers and provide an online mechanism for agricultural researchers to submit requests for GO annotations.

Agriculture↗

PubMed2006

AgBase: a functional genomics resource for agriculture.

BACKGROUND: Many agricultural species and their pathogens have sequenced genomes and more are in progress. Agricultural species provide food, fiber, xenotransplant tissues, biopharmaceuticals and biomedical models. Moreover, many agricultural microorganisms are human zoonoses. However, systems biology from functional genomics data is hindered in agricultural species because agricultural genome sequences have relatively poor structural and functional annotation and agricultural research communities are smaller with limited funding compared to many model organism communities. DESCRIPTION: To facilitate systems biology in these traditionally agricultural species we have established "AgBase", a curated, web-accessible, public resource http://www.agbase.msstate.edu for structural and functional annotation of agricultural genomes. The AgBase database includes a suite of computational tools to use GO annotations. We use standardized nomenclature following the Human Genome Organization Gene Nomenclature guidelines and are currently functionally annotating chicken, cow and sheep gene products using the Gene Ontology (GO). The computational tools we have developed accept and batch process data derived from different public databases (with different accession codes), return all existing GO annotations, provide a list of products without GO annotation, identify potential orthologs, model functional genomics data using GO and assist proteomics analysis of ESTs and EST assemblies. Our journal database helps prevent redundant manual GO curation. We encourage and publicly acknowledge GO annotations from researchers and provide a service for researchers interested in GO and analysis of functional genomics data. CONCLUSION: The AgBase database is the first database dedicated to functional genomics and systems biology analysis for agriculturally important species and their pathogens. We use experimental data to improve structural annotation of genomes and to functionally characterize gene products. AgBase is also directly relevant for researchers in fields as diverse as agricultural production, cancer biology, biopharmaceuticals, human health and evolutionary biology. Moreover, the experimental methods and bioinformatics tools we provide are widely applicable to many other species including model organisms.

Agriculture↗

PubMed2006

Modeling a whole organ using proteomics: the avian bursa of Fabricius.

While advances in proteomics have improved proteome coverage and enhanced biological modeling, modeling function in multicellular organisms requires understanding how cells interact. Here we used the chicken bursa of Fabricius, a common experimental system for B cell function, to model organ function from proteomics data. The bursa has two major functional cell types: B cells and the supporting stromal cells. We used differential detergent fractionation-multidimensional protein identification technology (DDF-MudPIT) to identify 5198 proteins from all cellular compartments. Of these, 1753 were B cell specific, 1972 were stroma specific and 1473 were shared between the two. By modeling programmed cell death (PCD), cell differentiation and proliferation, and transcriptional activation, we have improved functional annotation of chicken proteins and placed chicken-specific death receptors into the PCD process using phylogenetics. We have identified 114 transcription factors (TFs); 42 of the bursal B cell TFs have not been reported before in any B cells. We have also improved the structural annotation of a newly sequenced genome by confirming the in vivo expression of 4006 "predicted", and 6623 ab initio, ORFs. Finally, we have developed a novel method for facilitating structural annotation, "expressed peptide sequence tags" (ePSTs) and demonstrate its utility by identifying 521 potential novel proteins from the chicken "unassigned chromosome".

Amino Acid Sequence↗

PubMed2004

Revealing gene transcription and translation initiation patterns in archaea, using an interactive clustering model.

An interactive clustering model based on positional weight matrices is described and results obtained using the model to analyze gene regulation patterns in archaea are presented. The 5' flanking sequences of ORFs identified in four archaea, Sulfolobus solfataricus, Pyrobaculum aerophilum, Halobacterium sp. NRC-1, and Pyrococcus abyssi, were clustered using the model. Three regular patterns of clusters were identified for most ORFs. One showed genes with only a ribosome-binding site; another showed genes with a transcriptional regulatory region located at a constant location with respect to the start codon. A third pattern combined the previous two. Both P. aerophilum and Halobacterium sp. NRC-1 exhibited clusters of genes that lacked any regular pattern. Halobacterium sp. NRC-1 also presented regular features not seen in the other organisms. This group of archaea seems to use a combination of eubacterial and eukaryotic regulatory features as well as some unique to individual species. Our results suggest that interactive clustering may be used to examine the divergence of the gene regulatory machinery in archaea and to identify the presence of archaea-specific gene regulation patterns.

Archaea↗