Department of machine learning and bioinformatics, Bioinformatics Center, Kyoto University, Uji, Japan.
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.
Brief Bioinform. 2019 Nov 27;20(6):2028-2043. doi: 10.1093/bib/bby066.
Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Understanding biochemical characteristics of the metabolites is an essential and significant part of metabolomics to enlarge the knowledge of biological systems. It is also the key to the development of many applications and areas such as biotechnology, biomedicine or pharmaceuticals. However, the identification of the metabolites remains a challenging task in metabolomics with a huge number of potentially interesting but unknown metabolites. The standard method for identifying metabolites is based on the mass spectrometry (MS) preceded by a separation technique. Over many decades, many techniques with different approaches have been proposed for MS-based metabolite identification task, which can be divided into the following four groups: mass spectra database, in silico fragmentation, fragmentation tree and machine learning. In this review paper, we thoroughly survey currently available tools for metabolite identification with the focus on in silico fragmentation, and machine learning-based approaches. We also give an intensive discussion on advanced machine learning methods, which can lead to further improvement on this task.
代谢组学涉及大量代谢物的研究，这些代谢物是存在于生物系统中的小分子。它们具有许多重要的功能，如能量运输、信号传递、细胞构建块和抑制/催化。了解代谢物的生化特性是代谢组学的一个重要组成部分，它可以扩大对生物系统的认识。它也是生物技术、生物医学或制药等许多应用和领域发展的关键。然而，代谢物的鉴定仍然是代谢组学中的一个具有挑战性的任务，因为有大量潜在的但未知的代谢物。鉴定代谢物的标准方法是基于质谱 (MS) ，之前是分离技术。在过去的几十年里，已经提出了许多具有不同方法的技术来进行基于 MS 的代谢物鉴定任务，这些技术可以分为以下四类：质谱数据库、计算机模拟碎片、碎片树和机器学习。在这篇综述文章中，我们全面调查了目前可用于代谢物鉴定的工具，重点是计算机模拟碎片和基于机器学习的方法。我们还对先进的机器学习方法进行了深入讨论，这些方法可以进一步提高这个任务的效果。
