Biognosys has expert know-how on the field of multiple reaction monitoring (MRM) in proteomics. Please see below for a list of Publications[1-8].

1. mQuest/mProphet automates processing of MRM data and assigns statistically sound confidences to MRM peptide identifications.
2. MRM is a critical tool to validate biomarkers in blood.
3. MRM was used to profile complete metabolic pathways across five growing conditions in yeast.
4. Synthetic unpurified peptides can be used to speed up the process of generating high quality MRM assays.
5. MRM was used to validate predicted microRNA targets in C. elegans.
6. Proteome wide absolute protein abundances were derived for L. interrogans using MRM combined with shotgun proteomics.
7. Use of a mouse cancer model to discover tissue derived prostate biomarker candidates and their validation in human blood.
8. A combination of targeted proteomics (MRM) with N-glycopeptide enrichment allows a sensitive detection of biomarker candidates in human blood specimen.

1. Reiter L, Rinner O, Picotti P, Huttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R: mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nature methods 2011, 8(5):430-435.
2. Ossola R, Schiess R, Picotti P, Rinner O, Reiter L, Aebersold R: Biomarker validation in blood specimens by selected reaction monitoring mass spectrometry of N-glycosites. Methods Mol Biol 2011, 728:179-194.
3. Costenoble R, Picotti P, Reiter L, Stallmach R, Heinemann M, Sauer U, Aebersold R: Comprehensive quantitative analysis of central carbon and amino-acid metabolism in Saccharomyces cerevisiae under multiple conditions by targeted proteomics. Mol Syst Biol 2011, 7:464.
4. Picotti P, Rinner O, Stallmach R, Dautel F, Farrah T, Domon B, Wenschuh H, Aebersold R: High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nature methods 2010, 7(1):43-46.
5. Jovanovic M, Reiter L, Picotti P, Lange V, Bogan E, Hurschler BA, Blenkiron C, Lehrbach NJ, Ding XC, Weiss M et al: A quantitative targeted proteomics approach to validate predicted microRNA targets in C. elegans. Nature methods 2010, 7(10):837-842.
6. Malmstrom J, Beck M, Schmidt A, Lange V, Deutsch EW, Aebersold R: Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 2009, 460(7256):762-765.
7. Cima I, Schiess R, Wild P, Kaelin M, Schuffler P, Lange V, Picotti P, Ossola R, Templeton A, Schubert O et al: Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proc Natl Acad Sci U S A 2011, 108(8):3342-3347.
8. Stahl-Zeng J, Lange V, Ossola R, Eckhardt K, Krek W, Aebersold R, Domon B: High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol Cell Proteomics 2007, 6(10):1809-1817.

Biognosys  has expert know-how in the field of shotgun proteomics. Please see a list of publications below[1-6].

1. Mass spectrometry based shotgun proteomics was used to map out the proteomes of C. elegans and D. melanogaster. The resulting semi quantitative protein abundance data was used to compare the two proteomes.
2. Error rates of spectrum identifications do not propagate in a trivial manner to protein identifications and do inflate when going from spectra to proteins. A model to accurately estimate protein error rates is proposed.
3. A search engine optimized to identify cross linked peptides is presented and tested in a complete proteome sample.
4.  N-glycoprotein profiling for lung cancer biomarker candidates in human pleural effusion samples.
5. Compilation of mass spectrometry standard data sets on various mass spectrometers derived from a mixture of 18 proteins for testing of data analysis algorithms and software.
6. Presentation of a human atlas of shotgun derived peptide identifications intended for public use and contribution.

1. Schrimpf SP, Weiss M, Reiter L, Ahrens CH, Jovanovic M, Malmstrom J, Brunner E, Mohanty S, Lercher MJ, Hunziker PE et al: Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS biology 2009, 7(3):e48.
2. Reiter L, Claassen M, Schrimpf SP, Jovanovic M, Schmidt A, Buhmann JM, Hengartner MO, Aebersold R: Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol Cell Proteomics 2009, 8(11):2405-2417.
3. Rinner O, Seebacher J, Walzthoeni T, Mueller LN, Beck M, Schmidt A, Mueller M, Aebersold R: Identification of cross-linked peptides from large sequence databases. Nature methods 2008, 5(4):315-318.
4. Soltermann A, Ossola R, Kilgus-Hawelski S, von Eckardstein A, Suter T, Aebersold R, Moch H: N-glycoprotein profiling of lung adenocarcinoma pleural effusions by shotgun proteomics. Cancer 2008, 114(2):124-133.
5. Klimek J, Eddes JS, Hohmann L, Jackson J, Peterson A, Letarte S, Gafken PR, Katz JE, Mallick P, Lee H et al: The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools. J Proteome Res 2008, 7(1):96-103.
6. Deutsch EW, Eng JK, Zhang H, King NL, Nesvizhskii AI, Lin B, Lee H, Yi EC, Ossola R, Aebersold R: Human Plasma PeptideAtlas. Proteomics 2005, 5(13):3497-3500.