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A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry. | LitMetric

AI Article Synopsis

  • Label-free quantitation using mass spectrometry (LC-MS) is a cost-effective and straightforward method for analyzing protein and metabolite expression, but it faces challenges with data interpretation due to the large volume generated by advanced LC-MS techniques.
  • The AB3D algorithm has been developed to improve this process by effectively detecting and quantifying peaks in MS1 spectral data, reducing false positives/negatives compared to existing tools.
  • Testing of AB3D showed it to be significantly faster and more reliable in identifying known peptides from complex biological samples than popular software like MZmine 2 and MSight, demonstrating its potential for enhancing biomarker and drug discovery research.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4252003PMC
http://dx.doi.org/10.1186/s12859-014-0376-0DOI Listing

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