Deep learning the collisional cross sections of the peptide universe from a million experimental values

[1]  Ben C. Collins,et al.  diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition , 2020, Nature Methods.

[2]  M. Lubeck,et al.  Benefits of Collisional Cross Section Assisted Precursor Selection (caps-PASEF) for Cross-linking Mass Spectrometry , 2020, Molecular & Cellular Proteomics.

[3]  Alexey I Nesvizhskii,et al.  Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant , 2020, Molecular & Cellular Proteomics.

[4]  Fanny C. Liu,et al.  Comment on Effective Temperature and Structural Rearrangement in Trapped Ion Mobility Spectrometry: TIMS Enables Native Mass Spectrometry Applications. , 2020, Analytical chemistry.

[5]  Cameron N. Naylor,et al.  Evaluation of Trapped Ion Mobility Spectrometry (TIMS) Source Conditions Using Benzylammonium Thermometer Ions. , 2020, Journal of the American Society for Mass Spectrometry.

[6]  Henk W. P. van den Toorn,et al.  Benefits of Collisional Cross Section Assisted Precursor Selection (caps-PASEF) for Cross-linking Mass Spectrometry , 2020, bioRxiv.

[7]  Devin K. Schweppe,et al.  Benchmarking the Orbitrap Tribrid Eclipse for Next Generation Multiplexed Proteomics. , 2020, Analytical chemistry.

[8]  Guo Ci Teo,et al.  Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant , 2020, bioRxiv.

[9]  Jürgen Cox,et al.  MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics* , 2020, Molecular & Cellular Proteomics.

[10]  E. Pauw,et al.  Effective Temperature and Structural Rearrangement in Trapped Ion Mobility Spectrometry. , 2020, Analytical chemistry.

[11]  Ningombam Sanjib Meitei,et al.  Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts , 2020, Nature Communications.

[12]  Dorte B. Bekker-Jensen,et al.  A Compact Quadrupole-Orbitrap Mass Spectrometer with FAIMS Interface Improves Proteome Coverage in Short LC Gradients* , 2019, Molecular & Cellular Proteomics.

[13]  Johannes L. Schönberger,et al.  SciPy 1.0: fundamental algorithms for scientific computing in Python , 2019, Nature Methods.

[14]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[15]  Fabian J Theis,et al.  Deep learning: new computational modelling techniques for genomics , 2019, Nature Reviews Genetics.

[16]  J. Cox,et al.  High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis , 2019, Nature Methods.

[17]  Mathias Wilhelm,et al.  Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning , 2019, Nature Methods.

[18]  Roman Fischer,et al.  MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics* , 2019, Molecular & Cellular Proteomics.

[19]  Avi Ma'ayan,et al.  Faculty Opinions recommendation of Deep learning: new computational modelling techniques for genomics. , 2019, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

[20]  John A McLean,et al.  Collision cross section compendium to annotate and predict multi-omic compound identities† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c8sc04396e , 2018, Chemical science.

[21]  Martin Eisenacher,et al.  The PRIDE database and related tools and resources in 2019: improving support for quantification data , 2018, Nucleic Acids Res..

[22]  M. Huss,et al.  A primer on deep learning in genomics , 2018, Nature Genetics.

[23]  Scott M. Lundberg,et al.  Explainable machine-learning predictions for the prevention of hypoxaemia during surgery , 2018, Nature Biomedical Engineering.

[24]  Christopher J. Hogan,et al.  Recommendations for reporting ion mobility Mass Spectrometry measurements , 2018, Mass spectrometry reviews.

[25]  Huanming Yang,et al.  Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. , 2018, Analytical chemistry.

[26]  Ludovic C. Gillet,et al.  Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial , 2018, Molecular systems biology.

[27]  Derek J. Bailey,et al.  A Novel Differential Ion Mobility Device Expands the Depth of Proteome Coverage and the Sensitivity of Multiplex Proteomic Measurements* , 2018, Molecular & Cellular Proteomics.

[28]  Susan E. Abbatiello,et al.  Comprehensive Single-Shot Proteomics with FAIMS on a Hybrid Orbitrap Mass Spectrometer. , 2018, Analytical chemistry.

[29]  Melvin A. Park,et al.  Online Parallel Accumulation–Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer* , 2018, Molecular & Cellular Proteomics.

[30]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[31]  Melvin A. Park,et al.  Trapped ion mobility spectrometry: A short review , 2018 .

[32]  Chunjie Luo,et al.  pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning. , 2017, Analytical chemistry.

[33]  Stephan Hann,et al.  An Interlaboratory Evaluation of Drift Tube Ion Mobility-Mass Spectrometry Collision Cross Section Measurements. , 2017, Analytical chemistry.

[34]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[35]  Avanti Shrikumar,et al.  Learning Important Features Through Propagating Activation Differences , 2017, ICML.

[36]  Mathias Wilhelm,et al.  Building ProteomeTools based on a complete synthetic human proteome , 2017, Nature Methods.

[37]  Matthias Mann,et al.  Loss-less Nano-fractionator for High Sensitivity, High Coverage Proteomics * , 2017, Molecular & Cellular Proteomics.

[38]  Jody C. May,et al.  Ion Mobility Collision Cross Section Compendium. , 2017, Analytical chemistry.

[39]  Karl R. Weiss,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[40]  Yair Zick,et al.  Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems , 2016, 2016 IEEE Symposium on Security and Privacy (SP).

[41]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[42]  Marco Tulio Ribeiro,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, HLT-NAACL Demos.

[43]  Melvin A. Park,et al.  Fundamentals of Trapped Ion Mobility Spectrometry Part II: Fluid Dynamics , 2016, Journal of The American Society for Mass Spectrometry.

[44]  Jonathan M. Dilger,et al.  Examining the Influence of Phosphorylation on Peptide Ion Structure by Ion Mobility Spectrometry-Mass Spectrometry , 2016, Journal of The American Society for Mass Spectrometry.

[45]  Matthias Mann,et al.  Parallel Accumulation-Serial Fragmentation (PASEF): Multiplying Sequencing Speed and Sensitivity by Synchronized Scans in a Trapped Ion Mobility Device. , 2015, Journal of proteome research.

[46]  Perdita Barran,et al.  Ion Mobility Mass Spectrometry. , 2015, The Analyst.

[47]  D. Russell,et al.  Effects of charge states, charge sites and side chain interactions on conformational preferences of a series of model peptide ions. , 2015, The Analyst.

[48]  Alexander Binder,et al.  On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.

[49]  Oliver M. Bernhardt,et al.  Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues* , 2015, Molecular & Cellular Proteomics.

[50]  Erik Strumbelj,et al.  Explaining prediction models and individual predictions with feature contributions , 2014, Knowledge and Information Systems.

[51]  Eric W. Deutsch,et al.  A repository of assays to quantify 10,000 human proteins by SWATH-MS , 2014, Scientific Data.

[52]  Mathias Wilhelm,et al.  Ion Mobility Tandem Mass Spectrometry Enhances Performance of Bottom-up Proteomics , 2014, Molecular & Cellular Proteomics.

[53]  Melvin A. Park,et al.  High resolution trapped ion mobility spectrometery of peptides. , 2014, Analytical chemistry.

[54]  Lingjun Li,et al.  Large-Scale Collision Cross-Section Profiling on a Traveling Wave Ion Mobility Mass Spectrometer , 2014, Journal of The American Society for Mass Spectrometry.

[55]  Michael T Bowers,et al.  Ion mobility analysis of molecular dynamics. , 2014, Annual review of physical chemistry.

[56]  Melvin A. Park,et al.  Ion dynamics in a trapped ion mobility spectrometer. , 2014, The Analyst.

[57]  M. Mann,et al.  Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells , 2014, Nature Methods.

[58]  Cody R. Goodwin,et al.  Conformational Ordering of Biomolecules in the Gas Phase: Nitrogen Collision Cross Sections Measured on a Prototype High Resolution Drift Tube Ion Mobility-Mass Spectrometer , 2014, Analytical chemistry.

[59]  Stefan Tenzer,et al.  Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics , 2013, Nature Methods.

[60]  Peter Willett,et al.  What is a tutorial , 2013 .

[61]  Bing Wang,et al.  Prediction of peptide drift time in ion mobility mass spectrometry from sequence-based features , 2013, BMC Bioinformatics.

[62]  C. Robinson,et al.  Ion mobility mass spectrometry of peptide ions: effects of drift gas and calibration strategies. , 2012, Analytical chemistry.

[63]  F. Fernandez-Lima,et al.  Note: Integration of trapped ion mobility spectrometry with mass spectrometry. , 2011, The Review of scientific instruments.

[64]  David H Russell,et al.  Number of solution states of bradykinin from ion mobility and mass spectrometry measurements. , 2011, Journal of the American Chemical Society.

[65]  Melvin A. Park,et al.  Gas-phase separation using a trapped ion mobility spectrometer , 2011, International journal for ion mobility spectrometry : official publication of the International Society for Ion Mobility Spectrometry.

[66]  M. Mann,et al.  Andromeda: a peptide search engine integrated into the MaxQuant environment. , 2011, Journal of proteome research.

[67]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[68]  Mudita Singhal,et al.  Machine learning based prediction for peptide drift times in ion mobility spectrometry , 2010, Bioinform..

[69]  Bing Wang,et al.  Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry , 2010, BMC Bioinformatics.

[70]  Ronald J Moore,et al.  An LC-IMS-MS platform providing increased dynamic range for high-throughput proteomic studies. , 2010, Journal of proteome research.

[71]  C. Pace,et al.  Factors that influence helical preferences for singly charged gas-phase peptide ions: the effects of multiple potential charge-carrying sites. , 2010, The journal of physical chemistry. B.

[72]  Bartek Wilczynski,et al.  Biopython: freely available Python tools for computational molecular biology and bioinformatics , 2009, Bioinform..

[73]  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.

[74]  J. McLean,et al.  A collision cross-section database of singly-charged peptide ions , 2007 .

[75]  M. Jarrold,et al.  Helices and Sheets in vacuo. , 2007, Physical chemistry chemical physics : PCCP.

[76]  John A. McLean,et al.  Ion mobility–mass spectrometry: a new paradigm for proteomics , 2005 .

[77]  A. E. Counterman,et al.  Cis-trans signatures of proline-containing tryptic peptides in the gas phase. , 2002, Analytical chemistry.

[78]  S. Lipovetsky,et al.  Analysis of regression in game theory approach , 2001 .

[79]  A. Shvartsburg,et al.  Prediction of peptide ion mobilities via a priori calculations from intrinsic size parameters of amino acid residues , 2001, Journal of the American Society for Mass Spectrometry.

[80]  A. E. Counterman,et al.  Gas-phase separations of complex tryptic peptide mixtures , 2001, Fresenius' journal of analytical chemistry.

[81]  H. Hill,et al.  Separation of isomeric peptides using electrospray ionization/high-resolution ion mobility spectrometry. , 2000, Analytical chemistry.

[82]  S. Valentine,et al.  A database of 660 peptide ion cross sections: Use of intrinsic size parameters for bona fide predictions of cross sections , 1999, Journal of the American Society for Mass Spectrometry.

[83]  S. Valentine,et al.  Intrinsic Amino Acid Size Parameters from a Series of 113 Lysine-Terminated Tryptic Digest Peptide Ions , 1999 .

[84]  J P Reilly,et al.  Gas-phase separations of protease digests , 1998, Journal of the American Society for Mass Spectrometry.

[85]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[86]  E. W. McDaniel,et al.  Transport Properties of Ions in Gases , 1988 .

[87]  R. Doolittle,et al.  A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.

[88]  M. Levitt.  Conformational preferences of amino acids in globular proteins. , 1978, Biochemistry.

[89]  J. M. Zimmerman,et al.  The characterization of amino acid sequences in proteins by statistical methods. , 1968, Journal of theoretical biology.

[90]  G. Evatt,et al.  Short Review , 2019 .

[91]  Shouhong Wang,et al.  Fluid Dynamics , 2019, Phase Transition Dynamics.

[92]  Melvin A. Park,et al.  Fundamentals of Trapped Ion Mobility Spectrometry , 2014, Journal of The American Society for Mass Spectrometry.

[93]  Prabha Dwivedi,et al.  Ion mobility-mass spectrometry. , 2008, Journal of mass spectrometry : JMS.

[94]  S. Valentine,et al.  Resolving isomeric peptide mixtures: a combined HPLC/ion mobility-TOFMS analysis of a 4000-component combinatorial library. , 2002, Analytical chemistry.

[95]  M. Jarrold,et al.  Peptides and proteins in the vapor phase. , 2000, Annual review of physical chemistry.