@inbook{4eaf1f32e188487bbe218144b3a0c390,
title = "Guidelines for sample normalization to minimize batch variation for large-scale metabolic profiling of plant natural genetic variance",
abstract = "Recent methodological advances in both liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS) have facilitated the profiling highly complex mixtures of primary and secondary metabolites in order to investigate a diverse range of biological questions. These techniques usually face a large number of potential sources of technical and biological variation. In this chapter we describe guidelines and normalization procedures to reduce the analytical variation, which are essential for the high-throughput evaluation of metabolic variance used in broad genetic populations which commonly entail the evaluation of hundreds or thousands of samples. This chapter specifically deals with handling of large-scale plant samples for metabolomics analysis of quantitative trait loci (mQTL) in order to reduce analytical error as well as batch-to-batch variation.",
keywords = "Batch normalization, GC-MS, LC-MS, Large-scale metabolomics, Natural genetic variation, QTL mapping, Variation",
author = "Saleh Alseekh and Si Wu and Yariv Brotman and Fernie, {Alisdair R.}",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2018",
doi = "10.1007/978-1-4939-7819-9_3",
language = "אנגלית",
series = "Methods in Molecular Biology",
publisher = "Humana Press Inc.",
pages = "33--46",
booktitle = "Methods in Molecular Biology",
}