Abstract
With the large variety of Proteomics workflows, as well as the large variety of instruments and data-analysis software available, researchers today face major challenges validating and comparing their Proteomics data. It is the expectation that Human Proteome Organisation (HUPO) related standardization initiatives with its standardized data formats but also with its efforts in standardized processing and validation will lead to field-generated data of greater accuracy, reproducibility and comparability.
Highlights
State of the art techniques for the analysis of complex proteomes use multidimensional approaches: pre-separation on protein level (1D/2D PAGE); different separation principles on peptide level (IEF, 1D LC and 2D LC workflows), and a combination of different MS techniques (ESI and MALDI Ionisation, Ion Trap or TOF- AnalysersFull Paper Accepted from the Joint 2nd Pacific Rim International Conference on Protein Science and 4th Asian-Oceania Human Proteome Organization, Cairns- Australia, 22-26 June 2008to generate MS and MS-MS data)
Nowadays a new bioinformatics focus is necessary for automatic methods of spectrum data processing and validation of peptide/protein identification, especially in multiworkflow studies
Bioinformatics tools for the automated protein identification and result processing with quality control, the elaboration of standards and a well defined processing pipeline are all mandatory to produce reliable and comparable data when analyzing heterogeneous data generated in multi-workflow studies
Summary
State of the art techniques for the analysis of complex proteomes use multidimensional approaches: pre-separation on protein level (1D/2D PAGE); different separation principles on peptide level (IEF, 1D LC and 2D LC workflows), and a combination of different MS techniques Of the ProteinScape bioinformatics platform, enabling automatic data processing and result validation by generating Protein_IDs extracted from redundant information, merging peptide lists from different workflow studies into one compiled protein list and automatically cutting off identification results for which a pre-selected false positive rate has been reached. Sequest (Eng et al, 1994) scores each peptide using a so Proteomic analyses typically produce massive amounts of mass spectrometric data, which are analysed in an automated way by database search engines for retrieval of peptide sequences and subsequent inference on the corresponding protein sequences The above generated protein list calculated for the selected peptide score threshold is validated by the decoy strategy for a specific false positive rate (FPR).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
