scholarly journals A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides

2015 ◽  
Vol 33 (7) ◽  
pp. 743-749 ◽  
Author(s):  
Joel M Chick ◽  
Deepak Kolippakkam ◽  
David P Nusinow ◽  
Bo Zhai ◽  
Ramin Rad ◽  
...  
Keyword(s):  
Shotgun Proteomics ◽  
Database Search ◽  
Modified Peptides

scholarly journals Erratum: A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides

2015 ◽  
Vol 33 (8) ◽  
pp. 882-882 ◽  
Author(s):  
Joel M Chick ◽  
Deepak Kolippakkam ◽  
David P Nusinow ◽  
Bo Zhai ◽  
Ramin Rad ◽  
...  
Keyword(s):  
Shotgun Proteomics ◽  
Database Search ◽  
Modified Peptides

scholarly journals Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

2020 ◽  
Author(s):  
John T. Halloran ◽  
Gregor Urban ◽  
David Rocke ◽  
Pierre Baldi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Peptide Identification ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Supervised Machine Learning ◽  
Superior Performance ◽  
Support Vector ◽  
Proteomics Data ◽  
Learning Classifier

AbstractSemi-supervised machine learning post-processors critically improve peptide identification of shot-gun proteomics data. Such post-processors accept the peptide-spectrum matches (PSMs) and feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSMs using the trained parameters, often yielding significantly more identified peptides across q-value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as support vector machines. In contrast, the powerful training capabilities of deep learning models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep models significantly improve the recalibration of PSMs compared to the most accurate and widely-used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that deep learning is able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed database-search functions.

Survey of the camel urinary proteome by shotgun proteomics using a multiple database search strategy

PROTEOMICS ◽  
2012 ◽  
Vol 12 (22) ◽  
pp. 3403-3406 ◽  
Author(s):  
Abdulqader A. Alhaider ◽  
Nervana Bayoumy ◽  
Evelyn Argo ◽  
Abdel G. M. A. Gader ◽  
David A. Stead
Keyword(s):  
Search Strategy ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Urinary Proteome

scholarly journals Combining high resolution and exact calibration to boost statistical power: A well-calibrated score function for high-resolution MS2 data

2018 ◽  
Author(s):  
Andy Lin ◽  
J. Jeffry Howbert ◽  
William Stafford Noble
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Statistical Power ◽  
State Of The Art ◽  
Score Function ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Mass Spectrometry Data ◽  
P Value ◽  
Score Functions

AbstractTo achieve accurate assignment of peptide sequences to observed fragmentation spectra, a shotgun proteomics database search tool must make good use of the very high resolution information produced by state-of-the-art mass spectrometers. However, making use of this information while also ensuring that the search engine’s scores are well calibrated—i.e., that the score assigned to one spectrum can be meaningfully compared to the score assigned to a different spectrum—has proven to be challenging. Here, we describe a database search score function, the “residue evidence” (res-ev) score, that achieves both of these goals simultaneously. We also demonstrate how to combine calibrated res-ev scores with calibrated XCorr scores to produce a “combined p-value” score function. We provide a benchmark consisting of four mass spectrometry data sets, which we use to compare the combined p-value to the score functions used by several existing search engines. Our results suggest that the combined p-value achieves state-of-the-art performance, generally outperforming MS Amanda and Morpheus and performing comparably to MS-GF+. The res-ev and combined p-value score functions are freely available as part of the Tide search engine in the Crux mass spectrometry toolkit (http://crux.ms).

scholarly journals An averaging strategy to reduce variability in target-decoy estimates of false discovery rate

2018 ◽  
Author(s):  
Uri Keich ◽  
Kaipo Tamura ◽  
William Stafford Noble
Keyword(s):  
False Discovery Rate ◽  
Statistical Power ◽  
Shotgun Proteomics ◽  
Database Search ◽  
Proteomics Data ◽  
Decoy Database ◽  
Software Toolkit ◽  
True Proportion ◽  
False Discovery ◽  
False Discoveries

AbstractDecoy database search with target-decoy competition (TDC) provides an intuitive, easy-to-implement method for estimating the false discovery rate (FDR) associated with spectrum identifications from shotgun proteomics data. However, the procedure can yield different results for a fixed dataset analyzed with different decoy databases, and this decoy-induced variability is particularly problematic for smaller FDR thresholds, datasets or databases. In such cases, the nominal FDR might be 1% but the true proportion of false discoveries might be 10%. The averaged TDC protocol combats this problem by exploiting multiple independently shuffled decoy databases to provide an FDR estimate with reduced variability. We provide a tutorial introduction to aTDC, describe an improved variant of the protocol that offers increased statistical power, and discuss how to deploy aTDC in practice using the Crux software toolkit.

Limitation of predictive 2-D liquid chromatography in reducing the database search space in shotgun proteomics: In silico studies

2012 ◽  
Vol 35 (14) ◽  
pp. 1771-1778 ◽  
Author(s):  
Eugene Moskovets ◽  
Anton A. Goloborodko ◽  
Alexander V. Gorshkov ◽  
Mikhail V. Gorshkov
Keyword(s):  
Liquid Chromatography ◽  
In Silico ◽  
Shotgun Proteomics ◽  
Search Space ◽  
Database Search ◽  
In Silico Studies
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