Supplementary tables:MetaFetcheR: An R package for complete mapping of small compound data

The dataset includes a PDF file containing the results and an Excel file with the following tables: Table S1 Results of comparing the performance of MetaFetcheR to MetaboAnalystR using Diamanti et al. Table S2 Results of comparing the performance of MetaFetcheR to MetaboAnalystR for Priolo et al. Table S3 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool using Diamanti et al. Table S4 Results of comparing the performance of MetaFetcheR to MetaboAnalyst 5.0 webtool for Priolo et al. Table S5 Data quality test results for running 100 iterations on HMDB database. Table S6 Data quality test results for running 100 iterations on KEGG database. Table S7 Data quality test results for running 100 iterations on ChEBI database. Table S8 Data quality test results for running 100 iterations on PubChem database. Table S9 Data quality test results for running 100 iterations on LIPID MAPS database. Table S10 The list of metabolites that were not mapped by MetaboAnalystR for Diamanti et al. Table S11 An example of an input matrix for MetaFetcheR. Table S12 Results of comparing the performance of MetaFetcheR to MS_targeted using Diamanti et al. Table S13 Data set from Diamanti et al. Table S14 Data set from Priolo et al. Table S15 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Diamanti et al. Table S16 Results of comparing the performance of MetaFetcheR to CTS using LIPID MAPS identifiers available in Diamanti et al. Table S17 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. Table S18 Results of comparing the performance of MetaFetcheR to CTS using KEGG identifiers available in Priolo et al. (See the "index" tab in the Excel file for more information) Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. Lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power and large delays in delivery of results. We developed MetaFetcheR, an open-source R package that links metabolite data from several small-compound databases, resolves inconsistencies and covers a variety of use-cases of data fetching. We showed that the performance of MetaFetcheR was superior to existing approaches and databases by benchmarking the performance of the algorithm in three independent case studies based on two published datasets. The dataset was originally published in DiVA and moved to SND in 2024.本数据集包含一份载有研究结果的PDF文件，以及一份包含如下表格的Excel文件： 表S1 基于Diamanti等人研究的MetaFetcheR与MetaboAnalystR性能对比结果 表S2 基于Priolo等人研究的MetaFetcheR与MetaboAnalystR性能对比结果 表S3 基于Diamanti等人研究的MetaFetcheR与MetaboAnalyst 5.0在线工具性能对比结果 表S4 基于Priolo等人研究的MetaFetcheR与MetaboAnalyst 5.0在线工具性能对比结果 表S5 在人类代谢组数据库（HMDB, Human Metabolome Database）上执行100次迭代的数据质量测试结果 表S6 在京都基因与基因组百科全书（KEGG, Kyoto Encyclopedia of Genes and Genomes）上执行100次迭代的数据质量测试结果 表S7 在生物兴趣化学实体数据库（ChEBI, Chemical Entities of Biological Interest）上执行100次迭代的数据质量测试结果 表S8 在PubChem数据库（PubChem）上执行100次迭代的数据质量测试结果 表S9 在LIPID MAPS数据库（LIPID MAPS）上执行100次迭代的数据质量测试结果 表S10 Diamanti等人研究中未被MetaboAnalystR注释的代谢物列表 表S11 MetaFetcheR的输入矩阵示例 表S12 基于Diamanti等人研究的MetaFetcheR与MS_targeted性能对比结果 表S13 Diamanti等人的研究数据集 表S14 Priolo等人的研究数据集 表S15 基于Diamanti等人研究中可用的KEGG标识符，MetaFetcheR与CTS性能对比结果 表S16 基于Diamanti等人研究中可用的LIPID MAPS标识符，MetaFetcheR与CTS性能对比结果 表S17 基于Priolo等人研究中可用的KEGG标识符，MetaFetcheR与CTS性能对比结果 表S18 基于Priolo等人研究中可用的KEGG标识符，MetaFetcheR与CTS性能对比结果 （可查看Excel文件中的"索引"工作表以获取更多信息） 小分子化合物数据库蕴藏着海量的代谢物及代谢通路相关信息。然而，此类数据库数量繁多且信息冗余，给数据分析与标准化工作带来了诸多严峻挑战。若在项目初期未能提前规划数据访问途径，可能会导致化合物标注错误、统计效力降低，以及研究结果交付周期大幅延长。 本研究开发了MetaFetcheR——一款开源R语言包，可整合多个小分子化合物数据库的代谢物数据，解决数据不一致问题，并覆盖多种数据获取应用场景。本研究基于两份已发表的数据集开展三项独立案例研究，通过算法性能基准测试证实，MetaFetcheR的性能优于现有方法与数据库。 本数据集最初发布于DiVA平台，并于2024年迁移至SND平台。