scholarly journals A Fast and Memory‐Efficient Spectral Library Search Algorithm Using Locality‐Sensitive Hashing

PROTEOMICS ◽  
2020 ◽  
Vol 20 (21-22) ◽  
pp. 2000002 ◽  
Author(s):  
Lei Wang ◽  
Kaiyuan Liu ◽  
Sujun Li ◽  
Haixu Tang
Keyword(s):  
Search Algorithm ◽  
Locality Sensitive Hashing ◽  
Spectral Library ◽  
Library Search ◽  
Memory Efficient

scholarly journals N-Linked Glycopeptide Identification Based on Open Mass Spectral Library Search

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhiwu An ◽  
Qingbo Shu ◽  
Hao Lv ◽  
Lian Shu ◽  
Jifeng Wang ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Human Cancer ◽  
Cancer Cell Line ◽  
Peptide Identification ◽  
High Energy ◽  
Mass Spectral Library ◽  
Spectral Library ◽  
Data Set ◽  
Library Search ◽  
Mass Spectral

Confident characterization of intact glycopeptides is a challenging task in mass spectrometry-based glycoproteomics due to microheterogeneity of glycosylation, complexity of glycans, and insufficient fragmentation of peptide bones. Open mass spectral library search is a promising computational approach to peptide identification, but its potential in the identification of glycopeptides has not been fully explored. Here we present pMatchGlyco, a new spectral library search tool for intact N-linked glycopeptide identification using high-energy collisional dissociation (HCD) tandem mass spectrometry (MS/MS) data. In pMatchGlyco, (1) MS/MS spectra of deglycopeptides are used to create spectral library, (2) MS/MS spectra of glycopeptides are matched to the spectra in library in an open (precursor tolerant) manner and the glycans are inferred, and (3) a false discovery rate is estimated for top-scored matches above a threshold. The efficiency and reliability of pMatchGlyco were demonstrated on a data set of mixture sample of six standard glycoproteins and a complex glycoprotein data set generated from human cancer cell line OVCAR3.

scholarly journals MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Yang-Ming Lin ◽  
Ching-Tai Chen ◽  
Jia-Ming Chang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Tandem Mass ◽  
Spectral Library ◽  
Deep Convolutional Neural Networks ◽  
Library Search

Abstract Background Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database search but is limited to peptides that have been previously identified. An accurate tandem mass spectrum prediction tool is thus crucial in expanding the peptide space and increasing the coverage of spectral library search. Results We propose MS2CNN, a non-linear regression model based on deep convolutional neural networks, a deep learning algorithm. The features for our model are amino acid composition, predicted secondary structure, and physical-chemical features such as isoelectric point, aromaticity, helicity, hydrophobicity, and basicity. MS2CNN was trained with five-fold cross validation on a three-way data split on the large-scale human HCD MS2 dataset of Orbitrap LC-MS/MS downloaded from the National Institute of Standards and Technology. It was then evaluated on a publicly available independent test dataset of human HeLa cell lysate from LC-MS experiments. On average, our model shows better cosine similarity and Pearson correlation coefficient (0.690 and 0.632) than MS2PIP (0.647 and 0.601) and is comparable with pDeep (0.692 and 0.642). Notably, for the more complex MS2 spectra of 3+ peptides, MS2PIP is significantly better than both MS2PIP and pDeep. Conclusions We showed that MS2CNN outperforms MS2PIP for 2+ and 3+ peptides and pDeep for 3+ peptides. This implies that MS2CNN, the proposed convolutional neural network model, generates highly accurate MS2 spectra for LC-MS/MS experiments using Orbitrap machines, which can be of great help in protein and peptide identifications. The results suggest that incorporating more data for deep learning model may improve performance.

Closure Effects on Infrared Spectral Library Search Performance

1987 ◽  
Vol 41 (8) ◽  
pp. 1298-1302 ◽  
Author(s):  
P. B. Harrington ◽  
T. L. Isenhour
Keyword(s):  
Infrared Spectra ◽  
Analytical Method ◽  
Unit Vector ◽  
Search Performance ◽  
Spectral Library ◽  
Data Set ◽  
Library Search ◽  
Vector Length ◽  
Infrared Spectral ◽  
Noise Frequency

Closure is caused by normalization of a data set, and it affects any multivariate analytical method applied to that data set. Two common methods of normalizing infrared spectra (IR), to unit maximum absorbance and to unit vector length, are evaluated by measurement of library search performance. Search performance is evaluated, by the use of the Quantitative Reliability Metric (QRM), as a function of noise frequency and noise level.

A simple workflow to increase MS2 identification rate by subsequent spectral library search

PROTEOMICS ◽  
2009 ◽  
Vol 9 (6) ◽  
pp. 1731-1736 ◽  
Author(s):  
Erik Ahrné ◽  
Alexandre Masselot ◽  
Pierre-Alain Binz ◽  
Markus Müller ◽  
Frederique Lisacek
Keyword(s):  
Spectral Library ◽  
Library Search ◽  
Identification Rate

Evaluation of mass spectral library search algorithms implemented in commercial software

2015 ◽  
Vol 50 (6) ◽  
pp. 820-825 ◽  
Author(s):  
Andrey Samokhin ◽  
Ksenia Sotnezova ◽  
Vitaly Lashin ◽  
Igor Revelsky
Keyword(s):  
Search Algorithms ◽  
Mass Spectral Library ◽  
Spectral Library ◽  
Commercial Software ◽  
Library Search ◽  
Mass Spectral

scholarly journals Pepitome: Evaluating Improved Spectral Library Search for Identification Complementarity and Quality Assessment

2012 ◽  
Vol 11 (3) ◽  
pp. 1686-1695 ◽  
Author(s):  
Surendra Dasari ◽  
Matthew C. Chambers ◽  
Misti A. Martinez ◽  
Kristin L. Carpenter ◽  
Amy-Joan L. Ham ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Spectral Library ◽  
Library Search

scholarly journals A New Library-Search Algorithm for Mixture Analysis Using DART-MS

2021 ◽  
Author(s):  
Arun Moorthy ◽  
Edward Sisco
Keyword(s):  
Mass Spectrum ◽  
Mass Spectra ◽  
Search Algorithm ◽  
Collision Induced Dissociation ◽  
Two Stage ◽  
Library Search ◽  
Prototype Implementation ◽  
Search Results ◽  
Protonated Molecules ◽  
Acetyl Fentanyl

This manuscript introduces a new library-search algorithm for identifying components of a mixture using in-source collision-induced dissociation (is-CID) mass spectra. The two-stage search, titled the Inverted Library-Search Algorithm (ILSA), identifies potential components in a mixture by first searching its low fragmentation mass spectrum for target peaks, assuming these peaks are protonated molecules, and then scoring each target peak with possible library matches using one of two schemes. Utility of the ILSA is demonstrated through several example searches of model mixtures of acetyl fentanyl, benzyl fentanyl, amphetamine and methamphetamine searched against a small library of select compounds and the NIST DART-MS Forensics library. Discussion of the search results and several open areas of research to further extend the method are provided. A prototype implementation of the ILSA is available at <a href="https://github.com/asm3-nist/DART-MS-DST">https://github.com/asm3-nist/DART-MS-DST</a>.

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