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updated bib file for vignette

Nils Kurzawa authored on 18/03/2020 10:23:10
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 @article{Molina2013,
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 author = {Molina, Daniel Martinez and Jafari, Rozbeh and Ignatushchenko, Marina and Seki, Takahiro and Larsson, E. Andreas and Dan, Chen and Sreekumar, Lekshmy and Cao, Yihai and Nordlund, P{\"{a}}r},
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-file = {:Users/kurzawa/Documents/Literature/2013/Molina et al/84.full.pdf:pdf},
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 journal = {Science},
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-mendeley-groups = {TPP,TppLm},
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 number = {July},
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 pages = {84--88},
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 title = {{Monitoring Drug Target Engagement in Cells and Tissues Using the Cellular Thermal Shift Assay}},
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@@ -17,11 +15,9 @@ arxivId = {arXiv:1011.1669v3},
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 author = {Savitski, M. M. and Reinhard, F. B. M. and Franken, H. and Werner, Thilo and Savitski, Maria F{\"{a}}lth and Eberhard, Dirk and {Martinez Molina}, Daniel and Jafari, Rozbeh and Dovega, Rebecca Bakszt and Klaeger, Susan and Kuster, Bernhard and Nordlund, P{\"{a}}r and Bantscheff, Marcus and Drewes, Gerard},
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 doi = {10.1126/science.1255784},
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 eprint = {arXiv:1011.1669v3},
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-file = {:Users/kurzawa/Documents/Literature/2014/Savitski et al/1255784.full.pdf:pdf},
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 isbn = {1095-9203 (Electronic)$\backslash$r0036-8075 (Linking)},
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 issn = {1095-9203},
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 journal = {Science},
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-mendeley-groups = {TPP,short project description,project{\_}description,TppLm},
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 number = {6205},
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 pages = {1255784},
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 pmid = {25278616},
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@@ -34,10 +30,8 @@ year = {2014}
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 abstract = {We describe a two-dimensional thermal proteome profiling strategy that can be combined with an orthogonal chemoproteomics approach to enable comprehensive target profiling of the marketed histone deacetylase inhibitor panobinostat. The N-hydroxycinnamide moiety is identified as critical for potent and tetrahydrobiopterin-competitive inhibition of phenylalanine hydroxylase leading to increases in phenylalanine and decreases in tyrosine levels. These findings provide a rationale for adverse clinical observations and suggest repurposing of the drug for treatment of tyrosinemia.},
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 author = {Becher, Isabelle and Werner, Thilo and Doce, Carola and Zaal, Esther A and T{\"{o}}gel, Ina and Khan, Crystal A and Rueger, Anne and Muelbaier, Marcel and Salzer, Elsa and Berkers, Celia R and Fitzpatrick, Paul F and Bantscheff, Marcus and Savitski, Mikhail M},
36 32
 doi = {10.1038/nchembio.2185},
37
-file = {:Users/kurzawa/Documents/Literature/2016/Becher et al/nchembio.2185.pdf:pdf},
38 33
 issn = {1552-4450},
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 journal = {Nature Chemical Biology},
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-mendeley-groups = {TPP,short project description,project{\_}description,TppLm},
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 number = {11},
42 36
 pages = {908--910},
43 37
 pmid = {27669419},
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@@ -50,11 +44,9 @@ year = {2016}
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 abstract = {The direct detection of drug-protein interactions in living cells is a major challenge in drug discovery research. Recently, we introduced an approach termed thermal proteome profiling (TPP), which enables the monitoring of changes in protein thermal stability across the proteome using quantitative mass spectrometry. We determined the intracellular thermal profiles for up to 7,000 proteins, and by comparing profiles derived from cultured mammalian cells in the presence or absence of a drug we showed that it was possible to identify direct and indirect targets of drugs in living cells in an unbiased manner. Here we demonstrate the complete workflow using the histone deacetylase inhibitor panobinostat. The key to this approach is the use of isobaric tandem mass tag 10-plex (TMT10) reagents to label digested protein samples corresponding to each temperature point in the melting curve so that the samples can be analyzed by multiplexed quantitative mass spectrometry. Important steps in the bioinformatic analysis include data normalization, melting curve fitting and statistical significance determination of compound concentration-dependent changes in protein stability. All analysis tools are made freely available as R and Python packages. The workflow can be completed in 2 weeks.},
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 author = {Franken, Holger and Mathieson, Toby and Childs, Dorothee and Sweetman, Gavain M A and Werner, Thilo and T{\"{o}}gel, Ina and Doce, Carola and Gade, Stephan and Bantscheff, Marcus and Drewes, Gerard and Reinhard, Friedrich B M and Huber, Wolfgang and Savitski, Mikhail M},
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 doi = {10.1038/nprot.2015.101},
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-file = {:Users/kurzawa/Documents/Literature/2015/Franken et al/nprot.2015.101.pdf:pdf},
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 isbn = {1754-2189},
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 issn = {1754-2189},
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 journal = {Nature Protocols},
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-mendeley-groups = {TPP,statistical methods,TppLm},
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 number = {10},
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 pages = {1567--1593},
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 pmid = {26379230},
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@@ -67,11 +59,9 @@ year = {2015}
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 abstract = {},
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 author = {Perrin, Jessica and Werner, Thilo and Kurzawa, Nils and Childs, Dorothee and Kalxdorf, Mathias and Rutkowska, Anna and Poeckel, Daniel and Sevin, Daniel C. and Stonehouse, Eugenia and Strohmer, Katrin and Heller, Bianca and Thomson, Douglas W. and Vappiani, Johanna and Krause, Jana and Eberl, H. Christian and Rau, Christina and Franken, Holger and Huber, Wolfgang and Faelth-Savitski, Maria and Savitski, Mikhail M. and Bantscheff, Marcus and Bergamini, Giovanna},
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 doi = {},
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-file = {},
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 isbn = {},
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 issn = {},
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 journal = {submitted},
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-mendeley-groups = {TPP,statistical methods,TppLm},
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 number = {},
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 pages = {},
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 pmid = {},
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@@ -84,11 +74,9 @@ year = {2018}
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 abstract = {},
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 author = {Sridharan, Sindhuja and Kurzawa, Nils and Werner, Thilo and Günther, Ina and Helm, Dominic and Huber, Wolfgang and Bantscheff, Marcus and Savitski, Mikhail M.},
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 doi = {},
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-file = {},
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 isbn = {},
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 issn = {},
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 journal = {submitted},
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-mendeley-groups = {TPP,statistical methods,TppLm},
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 number = {},
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 pages = {},
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 pmid = {},
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@@ -101,7 +89,6 @@ year = {2018}
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 abstract = {Determining the interaction partners of small molecules in the intact cellular context remains a key challenge in drug research. Thermal proteome profiling (TPP) addresses this need by inferring target engagement from changes in temperature-dependent protein denaturation at a proteome-wide scale. Existing approaches to TPP data analysis have been centered around a single parameter, the melting point. Target engagement is then identified from a compound induced change in this parameter. However, for a substantial number of proteins the melting point shift does not reflect the treatment effect well, or cannot be confidently estimated at all. To overcome these limitations, we present a non-parametric analysis of response curves (NPARC), a functional approach that compares entire curves instead of summary parameters. NPARC projects the data to a space of smooth functions and infers treatment effects by an F-statistic with degrees of freedom estimated from the data. We show that our method outperforms the currently used melting point-centric approach with regard to specificity and sensitivity on five independent datasets. NPARC reliably detected known cancer drug targets for which ligand binding was not reflected by melting point shifts. The proposed method works with arbitrary numbers of replicates, and comparisons can be defined in a flexible manner. We hope that the proposed approach will aid in the detection of novel targets and off-targets for drugs with to date unexplained mechanisms of action or side effects.},
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 author = {Childs, Dorothee and Bach, Karsten and Franken, Holger and Anders, Simon and Kurzawa, Nils and Bantscheff, Marcus and Savitski, Mikhail and Huber, Wolfgang},
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 doi = {10.1101/373845},
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-file = {:Users/kurzawa/Documents/Literature/2018/Childs et al/373845.full.pdf:pdf},
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 journal = {bioRxiv},
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 mendeley-groups = {TppLm},
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 pages = {1--20},
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@@ -113,12 +100,10 @@ year = {2018}
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 abstract = {Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. By using our method, 7,409 genes are called significant at a 1{\%} false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10{\%} false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47{\%} of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source edge software package.},
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 author = {Storey, John D and Xiao, Wenzhong and Leek, Jeffrey T and Tompkins, Ronald G and Davis, Ronald W},
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 doi = {10.1073/pnas.0504609102},
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-file = {:Users/kurzawa/Documents/Literature/2005/Storey et al/PNAS-2005-Storey-12837-42.pdf:pdf},
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 isbn = {0027-8424 (Print)$\backslash$r0027-8424 (Linking)},
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 issn = {0027-8424},
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 journal = {Proceedings of the National Academy of Sciences of the United States of America},
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 keywords = {Adult,Aged,Aged, 80 and over,Aging,Aging: genetics,Gene Expression Profiling,Gene Expression Profiling: methods,Gene Expression Regulation,Gene Expression Regulation: drug effects,Humans,Inflammation,Inflammation: chemically induced,Inflammation: genetics,Kidney Cortex,Kidney Cortex: metabolism,Lipopolysaccharides,Lipopolysaccharides: pharmacology,Middle Aged,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Software,Time Factors},
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-mendeley-groups = {statistical methods,references{\_}methods{\_}ATP,TppLm},
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 number = {36},
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 pages = {12837--42},
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 pmid = {16141318},
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@@ -129,12 +114,10 @@ year = {2005}
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 }
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 @article{Becher2018,
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 abstract = {{\textless}h2{\textgreater}Summary{\textless}/h2{\textgreater}{\textless}p{\textgreater}Quantitative mass spectrometry has established proteome-wide regulation of protein abundance and post-translational modifications in various biological processes. Here, we used quantitative mass spectrometry to systematically analyze the thermal stability and solubility of proteins on a proteome-wide scale during the eukaryotic cell cycle. We demonstrate pervasive variation of these biophysical parameters with most changes occurring in mitosis and G1. Various cellular pathways and components vary in thermal stability, such as cell-cycle factors, polymerases, and chromatin remodelers. We demonstrate that protein thermal stability serves as a proxy for enzyme activity, DNA binding, and complex formation {\textless}i{\textgreater}in situ{\textless}/i{\textgreater}. Strikingly, a large cohort of intrinsically disordered and mitotically phosphorylated proteins is stabilized and solubilized in mitosis, suggesting a fundamental remodeling of the biophysical environment of the mitotic cell. Our data represent a rich resource for cell, structural, and systems biologists interested in proteome regulation during biological transitions.{\textless}/p{\textgreater}},
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-author = {Becher, Isabelle and Andre, Amparo and Romanov, Natalie and Stein, Frank and Schramm, Maike and Baudin, Florence and Helm, Dominic and Kurzawa, Nils and Mateus, André and Mackmull, Marie-Therese and Typas, Athanasios and Mu, Christoph W and Bork, Peer and Beck, Martin and Savitski, Mikhail M},
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+author = {Becher, Isabelle and Andr{\'{e}}s-Pons, Amparo and Romanov, Natalie and Stein, Frank and Schramm, Maike and Baudin, Florence and Helm, Dominic and Kurzawa, Nils and Mateus, Andr{\'{e}} and Mackmull, Marie-Therese and Typas, Athanasios and M{\"{u}}ller, Christoph W and Bork, Peer and Beck, Martin and Savitski, Mikhail M},
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 doi = {10.1016/j.cell.2018.03.053},
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-file = {:Users/kurzawa/Documents/Literature/2018/Becher et al/PIIS0092867418303854.pdf:pdf},
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 issn = {00928674},
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 journal = {Cell},
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-mendeley-groups = {cell cycle,own papers,project{\_}description,TppLm},
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 pages = {1--13},
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 title = {{Pervasive Protein Thermal Stability Variation during the Cell Cycle}},
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 url = {https://www.cell.com/cell/fulltext/S0092-8674(18)30385-4},
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@@ -143,10 +126,8 @@ year = {2018}
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 @article{Feng2014,
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 author = {Feng, Yuehan and Franceschi, Giorgia De and Kahraman, Abdullah and Soste, Martin and Melnik, Andre and Boersema, Paul J and Laureto, Patrizia Polverino De and Nikolaev, Yaroslav and Oliveira, Ana Paula and Picotti, Paola},
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 doi = {10.1038/nbt.2999},
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-file = {:Users/kurzawa/Documents/Literature/2014/Feng et al/nbt.2999.pdf:pdf},
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 issn = {1087-0156},
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 journal = {Nature Biotechnology},
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-mendeley-groups = {stability proteomics,LiP-MS,TppLm},
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 number = {10},
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 publisher = {Nature Publishing Group},
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 title = {{Global analysis of protein structural changes in complex proteomes}},
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@@ -157,7 +138,6 @@ year = {2014}
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 @article{Ritz2015,
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 author = {Ritz, Christian and Baty, Florent and Streibig, Jens C and Gerhard, Daniel},
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 doi = {10.1371/journal.pone.0146021},
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-file = {:Users/kurzawa/Documents/Literature/2015/Ritz et al/journal.pone.0146021.PDF:PDF},
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 mendeley-groups = {statistical methods},
162 142
 pages = {1--13},
163 143
 title = {{Dose-Response Analysis Using R}},