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2016

Wieczorek S, Combes F, Lazar C, Giai Gianetto Q, Gatto L, Dorffer A, Hesse A, Coute Y, Ferro M, Bruley C, and Burger T. DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics Bioinformatics 2016 doi:10.1093/bioinformatics/btw580.

Perez-Riverol Y, Gatto L, et al. Ten Simple Rules for Taking Advantage of Git and GitHub. PLoS Comput Biol. 2016 Jul 14;12(7):e1004947. doi:10.1371/journal.pcbi.1004947.

Lazar C, Gatto L, Ferro M, Bruley C, Burger T. Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies. J Proteome Res. 2016 Apr 1;15(4):1116-25. doi:10.1021/acs.jproteome.5b00981.

Christoforou A, et al A draft map of the mouse pluripotent stem cell spatial proteome Nature Communications (2016) 12;7:9992. doi: 10.1038/ncomms9992.

Marondedze C, et al ,The RNA-binding protein repertoire of Arabidopsis thaliana  Scientific Reports (2016) 6:29766. doi: 10.1038/srep29766

Breckels et al,  Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Comput Biol. (2016) May 13;12(5):e1004920. doi: 10.1371/journal.pcbi.1004920.

Fabre B, et al Analysis of Drosophila melanogaster proteome dynamics during embryonic development by a combination of label-free proteomics approaches. Proteomics. (2016) Aug;16(15-16):2068-80. doi: 10.1002/pmic.201500482. Epub 2016 May 10.

Alam MT et al and Ralser M , The metabolic background is a global player in Saccharomyces gene expression epistasis. Nature Microbiol. (2016) Feb 1;1:15030. doi: 10.1038/nmicrobiol.2015.30.

Marondedze C  et al A Quantitative Phosphoproteome Analysis of cGMP-Dependent Cellular Responses in Arabidopsis thaliana. Mol Plant. (2016) Apr 4;9(4):621-3. doi: 10.1016/j.molp.2015.11.007.