Biomedical subjects

John H Morris

Publications and source records attributed to John H Morris.

4 recordsLinked to original sources

PubMed2024

Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks.

Mass spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualization and network analysis of bottom-up MS-based proteomics data sets.

Proteomics↗

PubMed2006

Leveraging enzyme structure-function relationships for functional inference and experimental design: the structure-function linkage database.

The study of mechanistically diverse enzyme superfamilies-collections of enzymes that perform different overall reactions but share both a common fold and a distinct mechanistic step performed by key conserved residues-helps elucidate the structure-function relationships of enzymes. We have developed a resource, the structure-function linkage database (SFLD), to analyze these structure-function relationships. Unique to the SFLD is its hierarchical classification scheme based on linking the specific partial reactions (or other chemical capabilities) that are conserved at the superfamily, subgroup, and family levels with the conserved structural elements that mediate them. We present the results of analyses using the SFLD in correcting misannotations, guiding protein engineering experiments, and elucidating the function of recently solved enzyme structures from the structural genomics initiative. The SFLD is freely accessible at http://sfld.rbvi.ucsf.edu.

Animals↗

PubMed2006

The International Gene Trap Consortium Website: a portal to all publicly available gene trap cell lines in mouse.

Gene trapping is a method of generating murine embryonic stem (ES) cell lines containing insertional mutations in known and novel genes. A number of international groups have used this approach to create sizeable public cell line repositories available to the scientific community for the generation of mutant mouse strains. The major gene trapping groups worldwide have recently joined together to centralize access to all publicly available gene trap lines by developing a user-oriented Website for the International Gene Trap Consortium (IGTC). This collaboration provides an impressive public informatics resource comprising approximately 45 000 well-characterized ES cell lines which currently represent approximately 40% of known mouse genes, all freely available for the creation of knockout mice on a non-collaborative basis. To standardize annotation and provide high confidence data for gene trap lines, a rigorous identification and annotation pipeline has been developed combining genomic localization and transcript alignment of gene trap sequence tags to identify trapped loci. This information is stored in a new bioinformatics database accessible through the IGTC Website interface. The IGTC Website (www.genetrap.org) allows users to browse and search the database for trapped genes, BLAST sequences against gene trap sequence tags, and view trapped genes within biological pathways. In addition, IGTC data have been integrated into major genome browsers and bioinformatics sites to provide users with outside portals for viewing this data. The development of the IGTC Website marks a major advance by providing the research community with the data and tools necessary to effectively use public gene trap resources for the large-scale characterization of mammalian gene function.

Animals↗

PubMed2005

Enhancing data sharing in collaborative research projects with DASH.

We describe a software framework, called DASH, that enables the facile access, maintenance, curation and sharing of computational biology data among collaborating research scientists. The DASH event-based framework enables members of team-based research projects to describe the multistep computational processing pipelines frequently required to generate data for sharing, monitors multiple distributed data stores for changes, and will then automatically invoke the appropriate processing pipeline(s). These pipelines can be used to communicate the results of data analyses to collaborators using mechanisms such as Web Services. We describe the overall design of the DASH system and the application of a simple DASH prototype to a collaborative pharmacogenomics research project involving several dozen researchers located at several different sites--the UCSF Pharmacogenetics of Membrane Transporters project.

Automation↗