PeakLink: a new peptide peak linking method in LC-MS/MS using wavelet and SVM

Bari, Mehrab Ghanat; Ma, Xuepo; Zhang, Jianqiu

doi:10.1093/bioinformatics/btu299

Cited by 10 publications

(3 citation statements)

References 27 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al [66] established a risk assessment model for textile clothing safety by using support vector machine and verified the effectiveness of the model. Some other references concerning the application of SVM are listed in [54,22,11,72,73,25,40,63,10].…”

Section: Figure 1 Stages Of Semiconductor Manufacturingmentioning

confidence: 99%

A novel quality prediction method based on feature selection considering high dimensional product quality data

Hu¹,

Qian²,

Pei³

et al. 2022

JIMO

View full text Add to dashboard Cite

Product quality is the lifeline of enterprise survival and development. With the rapid development of information technology, the semiconductor manufacturing process produces multitude of quality features. Due to the increasing quality features, the requirement on the training time and classification accuracy of quality prediction methods becomes increasingly higher. Aiming at realizing the quality prediction for semiconductor manufacturing process, this paper proposes a modified support vector machine (SVM) model based on feature selection, considering the high dimensional and nonlinear characteristics of data. The model first improves the Radial Basis Function (RBF) in SVM, and then combines the Duelist algorithm (DA) and variable neighborhood search algorithm (VNS) for feature selection and parameters optimization. Compared with some other SVM models that are based on DA, genetic algorithm (GA), and Information Gain algorithm (IG), the experiment results show that our DA-VNS-SVM can obtain higher classification accuracy rate with a smaller feature subset. In addition, we compare the DA-VNS-SVM with some common machine learning algorithms such as logistic regression, naive Bayes, decision tree, random forest, and artificial neural network. The results indicate that our model outperform these machine learning algorithms for the quality prediction of semiconductor.

show abstract

Section: Figure 1 Stages Of Semiconductor Manufacturingmentioning

confidence: 99%

A novel quality prediction method based on feature selection considering high dimensional product quality data

Hu¹,

Qian²,

Pei³

et al. 2022

JIMO

View full text Add to dashboard Cite

show abstract

“…It is extended from the PeakLink algorithm. [27] While PeakLink is designed for pairwise peak linking, MZDASoft PeakLink considers multiple LC/MS samples. The flow diagram and details are provided in the Supporting Information.…”

Section: Mzdasoft Peaklinkmentioning

confidence: 99%

“…Most recently, PeakLink [27] has been developed for peak linking using peak shape information and SVM classification. Wavelet transform [28,29] is used to obtain peak shape correlation which is coupled with time information and composes the input of the SVM classifier.…”

mentioning

confidence: 99%

MZDASoft: a software architecture that enables large‐scale comparison of protein expression levels over multiple samples based on liquid chromatography/tandem mass spectrometry

Bari

Ramirez

Wang

et al. 2015

Rapid Comm Mass Spectrometry

Self Cite

View full text Add to dashboard Cite

RATIONALE Without accurate peak linking/alignment, only the expression levels of a small percentage of proteins can be compared across multiple samples in Liquid-Chromatography Mass Spectrometry/Tandem Mass Spectrometry (LC-MS/MS) due to the selective nature of tandem MS peptide identification. This greatly hampers biomedical research that aims at finding biomarkers for disease diagnosis, treatment, and the understanding of disease mechanisms. A recent algorithm, PeakLink, has allowed the accurate linking of LC-MS peaks without tandem MS identifications to their corresponding ones with identifications across multiple samples collected from different instruments, tissues and labs, which greatly enhanced the ability of comparing proteins. However, PeakLink cannot be implemented practically for large number of samples based on existing software architectures, because it requires access to peak elution profiles from multiple LC-MS/MS samples simultaneously. METHODS We propose a new architecture based on parallel processing, which extracts LC-MS peak features, and saves them in database files to enable the implementation of PeakLink for multiple samples. The software has been deployed in High Performance Computing (HPC) environments. The core part of the software, MZDASoft Parallel Peak Extractor (PPE), can be downloaded with users and developer’s guide, and it can be run on HPC centers directly. The quantification applications, MZDASoft TandemQuant and MZDASoft PeakLink are written in Matlab, which are compiled with Matlab runtime compiler. A sample script that incorporates all necessary processing steps of MZDASoft for LC-MS/MS quantification in a parallel processing environment is available. The project webpage is http://compgenomics.utsa.edu/zgroup/MZDASoft. RESULTS The proposed architecture enables the implementation of PeakLink for multiple samples. Significantly more (100%–500%) proteins can be compared over multiple samples with better quantification accuracy in test cases. CONCLUSION MZDASoft enables large scale comparison of protein expression levels over multiple samples with much larger protein comparison coverage and better quantification accuracy. It is an efficient implementation based on parallel processing which can be used to process large amount of data.

show abstract

Tutorial: Correction of shifts in single-stage LC-MS(/MS) data

Mitra

Smilde

Bischoff

et al. 2018

Analytica Chimica Acta

View full text Add to dashboard Cite

Label-free LC-MS(/MS) provides accurate quantitative profiling of proteins and metabolites in complex biological samples such as cell lines, tissues and body fluids. A label-free experiment consists of several LC-MS(/MS) chromatograms that might be acquired over several days, across multiple laboratories using different instruments. Single-stage part (MS1 map) of the LC-MS(/MS) contains quantitative information on all compounds that can be detected by LC-MS(/MS) and is the data of choice used by quantitative LC-MS(/MS) data pre-processing workflows. Differences in experimental conditions and fluctuation of analytical parameters influence the overall quality of the MS1 maps and are factors hampering comparative statistical analyses and data interpretation. The quality of the obtained MS1 maps can be assessed based on changes in the two separation dimensions (retention time, mass-to-charge ratio) and the readout (ion intensity) of MS1 maps. In this tutorial we discuss two types of changes, monotonic and non-monotonic shifts, which may occur in the two separation dimensions and the readout of MS1 map. Monotonic shifts of MS1 maps can be corrected, while non-monotonic ones can only be assessed but not corrected, since correction would require precise modelling of the underlying physicochemical effects, which would require additional parameters and analysis. We discuss reasons for monotonic and non-monotonic shifts in the two separation dimensions and readout of MS1 maps, as well as algorithms that can be used to correct monotonic or to assess the extent non-monotonic shifts. Relation of non-monotonic shift with peak elution order inversion and orthogonality as defined in analytical chemistry is discussed. We aim this tutorial for data generator and evaluators scientists who aim to known the condition and approaches to produce and pre-processed comparable MS1 maps.

show abstract

PeakLink: a new peptide peak linking method in LC-MS/MS using wavelet and SVM

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 10 publications

References 27 publications

A novel quality prediction method based on feature selection considering high dimensional product quality data

A novel quality prediction method based on feature selection considering high dimensional product quality data

MZDASoft: a software architecture that enables large‐scale comparison of protein expression levels over multiple samples based on liquid chromatography/tandem mass spectrometry

Tutorial: Correction of shifts in single-stage LC-MS(/MS) data

Contact Info

Product

Resources

About