How to talk about protein‐level false discovery rates in shotgun proteomics
暂无分享,去创建一个
Matthew The | Lukas Käll | Ayesha Tasnim | L. Käll | M. The | Ayesha Tasnim
[1] William Stafford Noble,et al. On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics. , 2011, Journal of proteome research.
[2] Philip Lijnzaad,et al. The Ensembl genome database project , 2002, Nucleic Acids Res..
[3] William Stafford Noble,et al. A review of statistical methods for protein identification using tandem mass spectrometry. , 2012, Statistics and its interface.
[4] Charles Lewis,et al. The Superego, the Ego, and the Id in Statistical Reasoning , 2014 .
[5] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[6] J. Buhmann,et al. Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry* , 2009, Molecular & Cellular Proteomics.
[7] Lukas Käll,et al. Recognizing uncertainty increases robustness and reproducibility of mass spectrometry-based protein inferences. , 2012, Journal of proteome research.
[8] William Stafford Noble,et al. Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics. , 2013, Journal of proteomics.
[9] D. Tabb,et al. Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. , 2007, Journal of proteome research.
[10] Gerd Gigerenzer,et al. The superego, the ego, and the id in statistical reasoning , 1993 .
[11] Ruedi Aebersold,et al. The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools. , 2008, Journal of proteome research.
[12] William Stafford Noble,et al. Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data. , 2010, Journal of proteome research.
[13] John D. Storey. The positive false discovery rate: a Bayesian interpretation and the q-value , 2003 .
[14] B. Sorić. Statistical “Discoveries” and Effect-Size Estimation , 1989 .
[15] Roger E. Moore,et al. Qscore: An algorithm for evaluating SEQUEST database search results , 2002, Journal of the American Society for Mass Spectrometry.
[16] William Stafford Noble,et al. Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. , 2008, Journal of proteome research.
[17] R. Beavis,et al. A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. , 2003, Analytical chemistry.
[18] Quanhu Sheng,et al. A Bayesian Approach to Protein Inference Problem in Shotgun Proteomics , 2008, RECOMB.
[19] Predrag Radivojac,et al. Computational approaches to protein inference in shotgun proteomics , 2012, BMC Bioinformatics.
[20] William Stafford Noble,et al. On the Importance of Well-Calibrated Scores for Identifying Shotgun Proteomics Spectra , 2014, Journal of proteome research.
[21] Joshua E. Elias,et al. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. , 2003, Journal of proteome research.
[22] J. Berger. Could Fisher, Jeffreys and Neyman Have Agreed on Testing? , 2003 .
[23] Knut Reinert,et al. MSSimulator: Simulation of mass spectrometry data. , 2011, Journal of proteome research.
[24] P. Pevzner,et al. Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases. , 2008, Journal of proteome research.
[25] John D. Storey,et al. Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[26] Joshua E. Elias,et al. Target-Decoy Search Strategy for Mass Spectrometry-Based Proteomics , 2010, Proteome Bioinformatics.
[27] B. Kuster,et al. Mass-spectrometry-based draft of the human proteome , 2014, Nature.
[28] Mathias Wilhelm,et al. A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets , 2015, Molecular & Cellular Proteomics.
[29] Lukas Käll,et al. Solution to Statistical Challenges in Proteomics Is More Statistics, Not Less. , 2015, Journal of proteome research.
[30] R. Aebersold,et al. A statistical model for identifying proteins by tandem mass spectrometry. , 2003, Analytical chemistry.
[31] Alexey I Nesvizhskii,et al. Interpretation of Shotgun Proteomic Data , 2005, Molecular & Cellular Proteomics.