Literature DB >> 24611572

TracMass 2--a modular suite of tools for processing chromatography-full scan mass spectrometry data.

Erik Tengstrand1, Johan Lindberg, K Magnus Åberg.   

Abstract

In untargeted proteomics and metabolomics, raw data obtained with an LC/MS instrument are processed into a format that can be used for statistical analysis. Full scan MS data from chromatographic separation of biological samples are complex and analyte concentrations need to be extracted and aligned so that they can be compared across the samples. Several computer programs and methods have been developed for this purpose. There is still a need to improve the ease of use and feedback to the user because of the advanced multiparametric algorithms used. Here, we present and make publicly available, TracMass 2, a suite of computer programs that gives immediate graphical feedback to the data analyst on parameter settings and processing results, as well as producing state-of-the-art results. The main advantage of TracMass 2 is that the feedback and transparency of the processing steps generate confidence in the end result, which is a table of peak intensities. The data analyst can easily validate every step of the processing pipeline. Because the user receives feedback on how all parameter values affect the result before starting a lengthy computation, the user's learning curve is enhanced and the total time used for data processing can be reduced. TracMass 2 has been released as open source and is included in the Supporting Information . We anticipate that TracMass 2 will set a new standard for how chemometrical algorithms are implemented in computer programs.

Mesh:

Year:  2014        PMID: 24611572     DOI: 10.1021/ac403905h

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  8 in total

1.  Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection.

Authors:  Chiung-Ting Wu; Yizhi Wang; Yinxue Wang; Timothy Ebbels; Ibrahim Karaman; Gonçalo Graça; Rui Pinto; David M Herrington; Yue Wang; Guoqiang Yu
Journal:  Bioinformatics       Date:  2020-05-01       Impact factor: 6.937

2.  Current controlled vocabularies are insufficient to uniquely map molecular entities to mass spectrometry signal.

Authors:  Rob Smith; Ryan M Taylor; John T Prince
Journal:  BMC Bioinformatics       Date:  2015-04-23       Impact factor: 3.169

Review 3.  From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.

Authors:  Leonardo Perez de Souza; Thomas Naake; Takayuki Tohge; Alisdair R Fernie
Journal:  Gigascience       Date:  2017-07-01       Impact factor: 6.524

4.  Non-targeted analysis of unexpected food contaminants using LC-HRMS.

Authors:  Marco Kunzelmann; Martin Winter; Magnus Åberg; Karl-Erik Hellenäs; Johan Rosén
Journal:  Anal Bioanal Chem       Date:  2018-03-29       Impact factor: 4.142

5.  Elucidation of chromatographic peak shifts in complex samples using a chemometrical approach.

Authors:  Pedro F M Sousa; Angela de Waard; K Magnus Åberg
Journal:  Anal Bioanal Chem       Date:  2018-06-14       Impact factor: 4.142

6.  DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map.

Authors:  Fatema Tuz Zohora; M Ziaur Rahman; Ngoc Hieu Tran; Lei Xin; Baozhen Shan; Ming Li
Journal:  Sci Rep       Date:  2019-11-20       Impact factor: 4.379

7.  Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC-MS-Based Untargeted Metabolomics.

Authors:  Miao Tian; Zhonglong Lin; Xu Wang; Jing Yang; Wentao Zhao; Hongmei Lu; Zhimin Zhang; Yi Chen
Journal:  Molecules       Date:  2021-05-05       Impact factor: 4.411

8.  Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

Authors:  Merle M Plassmann; Erik Tengstrand; K Magnus Åberg; Jonathan P Benskin
Journal:  Anal Bioanal Chem       Date:  2016-04-27       Impact factor: 4.142

  8 in total

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