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ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUN PROTEOMICS USING PEPTIDE DETECTABILITY

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ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUN PROTEOMICS USING PEPTIDE DETECTABILITY

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    1. ADVANCEMENT IN PROTEIN INFERENCE FROM SHOTGUN PROTEOMICS USING PEPTIDE DETECTABILITY PEDRO ALVES Advisor: Predrag Radivojac

    2. Overview Shotgun Proteomics Protein Inference Problem Protein Identification Using Peptide Detectability Results Limitations and Improvements

    6. Protein Inference Problem

    8. Resolving Ambiguity

    9. Factors affecting Peptide Detection

    12. RESULTS

    13. GMPSA vs LDFA in a R. norvegicus sample

    14. GMPSA vs LDFA

    15. Limitations and Improvements Include missed-cleavage peptides Include lower scoring peptides to aid in the differentiation of tied proteins Include peptides identified with charges +1 and +3 Train on other analytical platforms Study the effects of detectability prediction on algorithm results

    16. Publications PSB 2007 Alves, P. , Arnold, R. , Novotny, M. , Radivojac, P. , Reilly, J. , Tang, H. (2007). Advancement in Protein Inference from Shotgun Proteomics Using Peptide Detectability. Pac. Symp. Biocomput., (2007) 12: 409-420 ISMB 2006 Tang, H., Arnold, R. J., Alves, P., Xun, Z., Clemmer, D. E., Novotny, M. V., Reilly, J. P. & Radivojac, P. (2006). A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics, (2006) 22 (14): e481-e488.

    17. Acknowledgements Predrag Radivojac Haixu Tang Randy Arnold IU School of Informatics IU Chemistry Dept.

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