Dataset Information

Automatic peak assignment and visualisation of copolymer mass spectrometry data using the 'genetic algorithm'.

ABSTRACT: Copolymer analysis is vitally important as the materials have a wide variety of applications due to their tunable properties. Processing mass spectrometry data for copolymer samples can be very complex due to the increase in the number of species when the polymer chains are formed by two or more monomeric units. In this paper, we describe the use of the genetic algorithm for automated peak assignment of copolymers synthesised by a variety of polymerisation methods. We find that in using this method we are able to easily assign copolymer spectra in a few minutes and visualise them into heat maps. These heat maps allow us to look qualitatively at the distribution of the chains, by showing how they alter with different polymerisation techniques, and by changing the initial copolymer composition. This methodology is simple to use and requires little user input, which makes it well suited for use by less expert users. The data outputted by the automatic assignment may also allow for more complex data processing in the future.

SUBMITTER: Town JS

PROVIDER: S-EPMC7507196 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Automatic peak assignment and visualisation of copolymer mass spectrometry data using the 'genetic algorithm'.

Town James S JS Gao Yuqui Y Hancox Ellis E Liarou Evelina E Shegiwal Ataulla A Atkins Christophe J CJ Haddleton David D

Rapid communications in mass spectrometry : RCM 20200212

Copolymer analysis is vitally important as the materials have a wide variety of applications due to their tunable properties. Processing mass spectrometry data for copolymer samples can be very complex due to the increase in the number of species when the polymer chains are formed by two or more monomeric units. In this paper, we describe the use of the genetic algorithm for automated peak assignment of copolymers synthesised by a variety of polymerisation methods. We find that in using this met ...[more]

PMID: 31721321

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Project description:BACKGROUND: Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study. RESULTS: We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools. CONCLUSIONS: AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Dataset Information

Automatic peak assignment and visualisation of copolymer mass spectrometry data using the 'genetic algorithm'.

Publications

Automatic peak assignment and visualisation of copolymer mass spectrometry data using the 'genetic algorithm'.

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