Benchmarking quantitative label-free LC–MS data processing workflows using a complex spiked proteomic standard dataset

Claire Ramus (1) , Agnès Hovasse (2) , Marlène Marcellin (3) , Anne-Marie Hesse (4, 5) , Emmanuelle Mouton-Barbosa (3) , David Bouyssié (3) , Sebastian Vaca , Christine Carapito (6) , Karima Chaoui (3) , Christophe Bruley (1) , Jérôme Garin (5) , Sarah Cianférani (7) , Myriam Ferro (1, 5) , Alain van Dorssaeler (7) , Odile Burlet-Schiltz (3) , Christine Schaeffer , Yohann Couté (1, 5) , Anne Gonzalez de Peredo (3)

1 EDyP - Etude de la dynamique des protéomes
2 Laboratoire de Spectrométrie de Masse BioOrgan [Université de Strasbourg]
3 IPBS - Institut de pharmacologie et de biologie structurale
4 ESPCI Paris - Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris
5 BGE - UMR S1038 - Laboratoire de Biologie à Grande Échelle
6 DSA-IPHC - Département Sciences Analytiques et Interactions Ioniques et Biomoléculaires
7 LSMBO - Laboratoire de Spectrométrie de Masse BioOrganique [Strasbourg]

Proteomic workflows based on nanoLC-MS/MS data-dependent-acquisition analysis have progressed tremendously in recent years. High-resolution and fast sequencing instruments have enabled the use of label-free quantitative methods, based either on spectral counting or on MS signal analysis, which appear as an attractive way to analyze differential protein

Mots clés: proteomic standard; nanoLC-MS/MS; label-free quantification; computational proteomics; spectral counting; MS signal analysis

Domaines: Génomique, Transcriptomique et Protéomique [q-bio.GN]

Claire Ramus, Agnès Hovasse, Marlène Marcellin, Anne-Marie Hesse, Emmanuelle Mouton-Barbosa, et al.. Benchmarking quantitative label-free LC–MS data processing workflows using a complex spiked proteomic standard dataset. Journal of Proteomics, 2016, 132, pp.51-62. ⟨10.1016/j.jprot.2015.11.011⟩. ⟨hal-02083890⟩