液相層析串聯質譜 (LC-MS/MS) 技術已成為代謝體學與蛋白質體學的主要平台。獲得的 LC-MS/MS 數據由可用的軟體工具處理，用於分子鑑定和定量。數據可視化是一種直接的品質檢測解決方式，因為用戶可以直接檢查 LC-MS/MS 數據中的原始信號和雜訊。然而，人工檢查是一項耗時的工作，尤其是對於包含數千個 LC-MS/MS 文件的數據集。必須有一個軟體工具可以自動從 LC-MS/MS 代謝體學和蛋白質體學數據中提取特徵峰，導出圖形以供用戶輕鬆檢查信號。因此，我們開發了一個名為 DeNox 的可視化工具，它可以有效地從 LC-MS 數據中提取信號並以三幅圖呈現信號，包括 (1) 洗脫熱圖 (滯留時間為 橫軸，質荷比為縱軸)，(2) 質譜信息 (質荷比為橫軸，信號強度為縱軸)，以及 (3) 萃取離子層析圖 (滯留時間為橫軸，信號強度為縱軸)。我們的工具亦提供八個測量值供用戶檢查定量的代謝物和鑑定的肽的品質，提高定量/鑑定結果的準確性和可靠性。此軟體已公開上傳於GitHub以供下載：https://github.com/ICMOL/DeNox。;Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become a predominant platform for metabolomics and proteomics.The acquired LC-MS/MS data are processed by available software tools for molecular identification and quantitation. Data visualization is a straightforward solution for quality inspection because users can directly exam raw signals and noises in LC-MS/MS data. Nevertheless, manual inspection is a time-consuming work, especially for a dataset with thousands of LC-MS/MS files. It is essential to have a software tool which can automatically extract feature peaks from LC-MS/MS metabolomics and proteomics data, exporting figures for users to easily exam the signals. We thus developed a visualization tool, called DeNox, which can efficiently extract signals from LC-MS data and present the signals in three figures, including (1) an elution profile (the retention time as the x-axis and m/z as the y-axis), (2) a spectrum information (the m/z as the x-axis and intensity as the y-axis), and (3) an extracted ion chromatogram (the retention time as the x-axis and intensity as the y-axis). Our tool also provides eight measures for users to inspect the quality of quantified metabolites and identified peptides, improving the accuracy and reliability of quantitation/identification results. The software tool is now available at: https://github.com/ICMOL/DeNox.
