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Bioinformatics and Statistics: LC‐MS(/MS) Data Preprocessing for Biomarker Discovery

This chapter provides an overview of the main steps of LC-MS(/MS) data pre-processing workflows. It discusses the main characteristics of these steps and provides a detailed functional description of the currently available algorithmic approaches. As an example, the chapter presents the main steps of the Threshold Avoiding Proteomics Pipeline, which includes several novel concepts to increase the accuracy of peptide quantification and to increase the extracted dynamic concentration range of compounds. The chapter further outlines a quality control method to assess and compare the relative performance of various LC-MS(/MS) data pre-processing workflows integrated in the msComapre framework using a set of differentially spiked LC-MS datasets. The chapter discusses the most common quantitative data pre-processing errors and provides visualization methods to identify these errors. Finally the chapter provides an overview of future development trends of LC-MS(/MS) data pre-processing algorithm development stressing the need for easy-to-use high-throughput bioinformatics platforms using modern parallel computational resources to alleviate current data pre-processing and analysis bottlenecks.

Print publication date: 10 Jun 2013
Copyright year: 2013
Print ISBN: 978-1-84973-422-6
PDF eISBN: 978-1-84973-436-3
From the book series:
RSC Drug Discovery