Proteomics & Metabolomics
In addition to doing work on existing IGBB projects, the IGBB proteomics staff can
perform a variety of mass spectrometry and other proteomics services for MS State principal investigators and
IGBB collaborators. Such research can be performed through a Proposal Partnership,
a Research Agreement, or the Service Center.
The
IGBB's proteomics staff has considerable expertise in...
- Protein isolation/purification from all types of organisms/tissues
- 1D & 2D gel electrophoresis
- Gel- and non-gel-based mass spectrometry
- Protein identification
- Discovery and characterization of post-translational modifications;
- Quantitative proteomics
- Comparative proteomics & metabolomics
- Western blotting & protein visualization
- Integration of proteomic and nucleic acids data (e.g., proteogenomic
mapping)
- Functional annotation of proteins using Gene Ontology (GO)
standards and procedures
With regard to mass spectrometers, the IGBB's proteomics staff utilizes a ThermoFisher LTQ Orbitrap Velos, a Waters Nano ESI Q-TOF (model Xevo G2-S), and an Applied Biosystems (now ThermoFisher) MALDI TOF TOF. The LTQ Orbitrap Velos and the Nano ESI Q-TOF are fitted with upstream HPLC sample
purification systems.
To discuss the possibility of having the IGBB conduct
proteomics research in collaboration with you, please submit a ticket through the MyIGBB HelpDesk.
An IGBB proteomics consultant will respond to your query as quickly as possible
(usually within 24 hours).
A listing of IGBB Standard Services and their prices -- including information and prices for Training and Self-Service Equipment Usage -- is available in the Standard Services Catalog in MyIGBB and in PDF form via the link below.
ALSO SEE: Genomics (including Transcriptomics) | Biocomputing (Bioinformatics & Computational Biology)
NOTE: PIs are asked to consider whether the participation of an IGBB employee in a project merits that employee's inclusion as a co-author on a resulting manuscript(s). The decision ultimately lies with the PI. However, the IGBB encourages IGBB staff and faculty involved in
Proposal Partnerships and
Research Agreements to discuss/negotiate co-authorship with PIs before starting work on a project.
Ashley ByarsAdministrative Assistant I
TRAVEL
email(662) 325-3094
Portera
Prediction of peptides observable by mass spectrometry applied at the experimental set levelIGBB Authors:
William S. Sanders, Susan M. Bridges, Fiona M. McCarthy, Bindu Nanduri, Shane C. BurgessPUBLICATION YEAR:
2007IMPACT FACTOR:
4.249CITATION COUNT:
62Sanders WS, Bridges SM, McCarthy FM, Nanduri B, Burgess SC (2007) Prediction of peptides observable by mass spectrometry applied at the experimental set level.
BMC Bioinformatics 8(Suppl 7): S23.
DOI:
10.1186/1471-2105-8-S7-S23EID:
2-s2.0-38349172005PMID: 18047723
DOWNLOAD PDFABSTRACTBACKGROUND: When proteins are subjected to proteolytic digestion and analyzed by mass spectrometry using a method such as 2D LC MS/MS, only a portion of the proteotypic peptides associated with each protein will be observed. The ability to predict which peptides can and cannot potentially be observed for a particular experimental dataset has several important applications in proteomics research including calculation of peptide coverage in terms of potentially detectable peptides, systems biology analysis of data sets, and protein quantification. RESULTS: We have developed a methodology for constructing artificial neural networks that can be used to predict which peptides are potentially observable for a given set of experimental, instrumental, and analytical conditions for 2D LC MS/MS (a.k.a Multidimensional Protein Identification Technology [MudPIT]) datasets. Neural network classifiers constructed using this procedure for two MudPIT datasets exhibit 10-fold cross validation accuracy of about 80%. We show that a classifier constructed for one dataset has poor predictive performance with the other dataset, thus demonstrating the need for dataset specific classifiers. Classification results with each dataset are used to compute informative percent amino acid coverage statistics for each protein in terms of the predicted detectable peptides in addition to the percent coverage of the complete sequence. We also demonstrate the utility of predicted peptide observability for systems analysis to help determine if proteins that were expected but not observed generate sufficient peptides for detection. CONCLUSION: Classifiers that accurately predict the likelihood of detecting proteotypic peptides by mass spectrometry provide proteomics researchers with powerful new approaches for data analysis. We demonstrate that the procedure we have developed for building a classifier based on an individual experimental data set results in classifiers with accuracy comparable to those reported in the literature based on large training sets collected from multiple experiments. Our approach allows the researcher to construct a classifier that is specific for the experimental, instrument, and analytical conditions of a single experiment and amenable to local, condition-specific, implementation. The resulting classifiers have application in a number of areas such as determination of peptide coverage for protein identification, pathway analysis, and protein quantification.
The IGBB is supported, in part, by the following units:
The IGBB is an HPC² member center.