Analysis of Proteomic Data for Toxicological Applications

[1]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[2]  Manuel C. Peitsch,et al.  Proteomics for systems toxicology , 2014, Computational and structural biotechnology journal.

[3]  Yang Xiang,et al.  Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models , 2014, BMC Bioinformatics.

[4]  Dean P. Jones,et al.  Integrated redox proteomics and metabolomics of mitochondria to identify mechanisms of cd toxicity. , 2014, Toxicological sciences : an official journal of the Society of Toxicology.

[5]  A. Chawade,et al.  Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets , 2014, Journal of proteome research.

[6]  Burkhard Morgenstern,et al.  Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets , 2014, PloS one.

[7]  Ted Slater,et al.  Recent advances in modeling languages for pathway maps and computable biological networks. , 2014, Drug discovery today.

[8]  J Craig Rowlands,et al.  FutureTox: building the road for 21st century toxicology and risk assessment practices. , 2014, Toxicological sciences : an official journal of the Society of Toxicology.

[9]  Julia Hoeng,et al.  Case study: the role of mechanistic network models in systems toxicology. , 2014, Drug discovery today.

[10]  R. Branca,et al.  Quantitative accuracy in mass spectrometry based proteomics of complex samples: the impact of labeling and precursor interference. , 2014, Journal of proteomics.

[11]  Manuel C. Peitsch,et al.  Systems Toxicology: From Basic Research to Risk Assessment , 2014, Chemical research in toxicology.

[12]  Lars Malmström,et al.  pyOpenMS: A Python‐based interface to the OpenMS mass‐spectrometry algorithm library , 2014, Proteomics.

[13]  Jaewoo Kang,et al.  Automatic Context-Specific Subnetwork Discovery from Large Interaction Networks , 2014, PloS one.

[14]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[15]  Alexey I. Nesvizhskii,et al.  Reconstructing targetable pathways in lung cancer by integrating diverse omics data , 2013, Nature Communications.

[16]  Ali Shojaie,et al.  Using random walks to identify cancer-associated modules in expression data , 2013, BioData Mining.

[17]  T. Ideker,et al.  Integrative approaches for finding modular structure in biological networks , 2013, Nature Reviews Genetics.

[18]  Peter J. van der Spek,et al.  NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data , 2013, Bioinform..

[19]  Rune Linding,et al.  PROTEINCHALLENGE: crowd sourcing in proteomics analysis and software development. , 2013, Journal of proteomics.

[20]  B. Kuster,et al.  Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. , 2013, Journal of proteome research.

[21]  Edward Y. Chen,et al.  Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool , 2013, BMC Bioinformatics.

[22]  Y. Shiloh,et al.  The ATM protein kinase: regulating the cellular response to genotoxic stress, and more , 2013, Nature Reviews Molecular Cell Biology.

[23]  I. Nookaew,et al.  Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods , 2013, Nucleic acids research.

[24]  Ingo Ruczinski,et al.  Statistical inference from multiple iTRAQ experiments without using common reference standards. , 2013, Journal of proteome research.

[25]  Manuel C. Peitsch,et al.  A Modular Cell-Type Focused Inflammatory Process Network Model for Non-Diseased Pulmonary Tissue , 2013, Bioinformatics and biology insights.

[26]  Manuel C. Peitsch,et al.  Construction of a Computable Network Model for DNA Damage, Autophagy, Cell Death, and Senescence , 2013, Bioinformatics and biology insights.

[27]  Damian Szklarczyk,et al.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..

[28]  Ni Li,et al.  Gene Ontology Annotations and Resources , 2012, Nucleic Acids Res..

[29]  M. Aluru,et al.  Reverse engineering and analysis of large genome-scale gene networks , 2012, Nucleic acids research.

[30]  Julio Saez-Rodriguez,et al.  Network based elucidation of drug response: from modulators to targets , 2013, BMC Systems Biology.

[31]  Joel N. Meyer,et al.  Effects of early life exposure to ultraviolet C radiation on mitochondrial DNA content, transcription, ATP production, and oxygen consumption in developing Caenorhabditis elegans , 2013, BMC Pharmacology and Toxicology.

[32]  Knut Reinert,et al.  Tools for Label-free Peptide Quantification , 2012, Molecular & Cellular Proteomics.

[33]  Gary D Bader,et al.  A travel guide to Cytoscape plugins , 2012, Nature Methods.

[34]  Ruedi Aebersold,et al.  New and improved proteomics technologies for understanding complex biological systems: Addressing a grand challenge in the life sciences , 2012, Proteomics.

[35]  Peter R. Baker,et al.  Current challenges in software solutions for mass spectrometry-based quantitative proteomics , 2012, Amino Acids.

[36]  Richard J. Lavallee,et al.  Optimized fast and sensitive acquisition methods for shotgun proteomics on a quadrupole orbitrap mass spectrometer. , 2012, Journal of proteome research.

[37]  D. Hardie,et al.  AMPK: a nutrient and energy sensor that maintains energy homeostasis , 2012, Nature Reviews Molecular Cell Biology.

[38]  Haiyuan Yu,et al.  Detecting overlapping protein complexes in protein-protein interaction networks , 2012, Nature Methods.

[39]  E. Marcotte,et al.  Insights into the regulation of protein abundance from proteomic and transcriptomic analyses , 2012, Nature Reviews Genetics.

[40]  J. Pype,et al.  Mainstream Smoke Chemistry and in Vitro and In Vivo Toxicity of the Reference Cigarettes 3R4F and 2R4F , 2012 .

[41]  Atul J. Butte,et al.  Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges , 2012, PLoS Comput. Biol..

[42]  Aedín C. Culhane,et al.  GeneSigDB: a manually curated database and resource for analysis of gene expression signatures , 2011, Nucleic Acids Res..

[43]  Bernhard Küster,et al.  Software Tools for MS-Based Quantitative Proteomics: A Brief Overview , 2012, Quantitative Methods in Proteomics.

[44]  Prahlad T. Ram,et al.  NetWalker: a contextual network analysis tool for functional genomics , 2012, BMC Genomics.

[45]  Jennifer Park,et al.  A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue , 2011, BMC Systems Biology.

[46]  J. Adamec,et al.  Comparison of genomic and proteomic data in recurrent airway obstruction affected horses using ingenuity pathway analysis® , 2011, BMC veterinary research.

[47]  Manuel C. Peitsch,et al.  Construction of a computable cell proliferation network focused on non-diseased lung cells , 2011, BMC Systems Biology.

[48]  H. Ichijo,et al.  Mitogen-activated protein kinases in mammalian oxidative stress responses. , 2011, Antioxidants & redox signaling.

[49]  Helga Thorvaldsdóttir,et al.  Molecular signatures database (MSigDB) 3.0 , 2011, Bioinform..

[50]  A. Barabasi,et al.  Interactome Networks and Human Disease , 2011, Cell.

[51]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[52]  Lincoln Stein,et al.  Reactome: a database of reactions, pathways and biological processes , 2010, Nucleic Acids Res..

[53]  Erwin van Vliet,et al.  Current standing and future prospects for the technologies proposed to transform toxicity testing in the 21st century. , 2011 .

[54]  Antonio Carvajal-Rodríguez,et al.  Multiple Hypothesis Testing in Proteomics: A Strategy for Experimental Work* , 2010, Molecular & Cellular Proteomics.

[55]  Gary D Bader,et al.  Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation , 2010, PloS one.

[56]  T. Graeber,et al.  The proximal signaling network of the BCR-ABL1 oncogene shows a modular organization , 2010, Oncogene.

[57]  M. Bushell,et al.  Translational regulation of gene expression during conditions of cell stress. , 2010, Molecular cell.

[58]  Richard M. Karp,et al.  DEGAS: De Novo Discovery of Dysregulated Pathways in Human Diseases , 2010, PloS one.

[59]  Michael A. White,et al.  Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data , 2010, PLoS Comput. Biol..

[60]  B. Kuster,et al.  Proteomics: a pragmatic perspective , 2010, Nature Biotechnology.

[61]  L. Stein,et al.  A human functional protein interaction network and its application to cancer data analysis , 2010, Genome Biology.

[62]  Donald Geman,et al.  Identifying Tightly Regulated and Variably Expressed Networks by Differential Rank Conservation (DIRAC) , 2010, PLoS Comput. Biol..

[63]  Tobias Müller,et al.  Bioinformatics Applications Note Systems Biology Bionet: an R-package for the Functional Analysis of Biological Networks , 2022 .

[64]  N. Karp,et al.  Addressing Accuracy and Precision Issues in iTRAQ Quantitation* , 2010, Molecular & Cellular Proteomics.

[65]  M. Huynen,et al.  Dominant processes during human dendritic cell maturation revealed by integration of proteome and transcriptome at the pathway level. , 2010, Journal of proteome research.

[66]  Natalie I. Tasman,et al.  A guided tour of the Trans‐Proteomic Pipeline , 2010, Proteomics.

[67]  Amanda Greenall,et al.  Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays , 2010, BMC Research Notes.

[68]  Lily Ting,et al.  Normalization and Statistical Analysis of Quantitative Proteomics Data Generated by Metabolic Labeling* , 2009, Molecular & Cellular Proteomics.

[69]  William Stafford Noble,et al.  Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets. , 2009, Journal of proteome research.

[70]  Frank A Witzmann,et al.  The role of toxicoproteomics in assessing organ specific toxicity. , 2009, EXS.

[71]  Pooja Mittal,et al.  A novel signaling pathway impact analysis , 2009, Bioinform..

[72]  M. Mann,et al.  MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification , 2008, Nature Biotechnology.

[73]  M. Mann,et al.  Precision proteomics: The case for high resolution and high mass accuracy , 2008, Proceedings of the National Academy of Sciences.

[74]  Tobias Müller,et al.  Identifying functional modules in protein–protein interaction networks: an integrated exact approach , 2008, ISMB.

[75]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[76]  William Stafford Noble,et al.  Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. , 2008, Journal of proteome research.

[77]  Knut Reinert,et al.  OpenMS – An open-source software framework for mass spectrometry , 2008, BMC Bioinformatics.

[78]  Steven P Gygi,et al.  Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry , 2007, Nature Methods.

[79]  Kai A. Reidegeld,et al.  Protein labeling by iTRAQ: A new tool for quantitative mass spectrometry in proteome research , 2007, Proteomics.

[80]  Thorsten Meinl,et al.  KNIME: The Konstanz Information Miner , 2007, GfKl.

[81]  L. Zieske A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. , 2006, Journal of experimental botany.

[82]  P. Bork,et al.  Proteome survey reveals modularity of the yeast cell machinery , 2006, Nature.

[83]  Adam Rauch,et al.  Computational Proteomics Analysis System (CPAS): an extensible, open-source analytic system for evaluating and publishing proteomic data and high throughput biological experiments. , 2006, Journal of proteome research.

[84]  John D. Storey,et al.  A network-based analysis of systemic inflammation in humans , 2005, Nature.

[85]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[86]  Martin Kuiper,et al.  BiNGO: a Cytoscape plugin to assess overrepresentation of Gene Ontology categories in Biological Networks , 2005, Bioinform..

[87]  R. Fisher Statistical methods for research workers , 1927, Protoplasma.

[88]  Robertson Craig,et al.  TANDEM: matching proteins with tandem mass spectra. , 2004, Bioinformatics.

[89]  S. Bryant,et al.  Open mass spectrometry search algorithm. , 2004, Journal of proteome research.

[90]  Gordon K Smyth,et al.  Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2004, Statistical applications in genetics and molecular biology.

[91]  Dermot F. Reilly,et al.  Integration of Proteomics and Genomics in Platelets , 2004, Molecular & Cellular Proteomics.

[92]  J. Yates,et al.  Probability-based validation of protein identifications using a modified SEQUEST algorithm. , 2002, Analytical chemistry.

[93]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[94]  N. Holbrook,et al.  Cellular response to oxidative stress: Signaling for suicide and survival * , 2002, Journal of cellular physiology.

[95]  Martin Vingron,et al.  Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.

[96]  Benno Schwikowski,et al.  Discovering regulatory and signalling circuits in molecular interaction networks , 2002, ISMB.

[97]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[98]  D. N. Perkins,et al.  Probability‐based protein identification by searching sequence databases using mass spectrometry data , 1999, Electrophoresis.

[99]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[100]  J. Yates,et al.  An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database , 1994, Journal of the American Society for Mass Spectrometry.

[101]  E. Suchman,et al.  The American soldier: Adjustment during army life. (Studies in social psychology in World War II), Vol. 1 , 1949 .