ArticleBMC bioinformatics2020
IP4M: an integrated platform for mass spectrometry-based metabolomics data mining.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A priori sample size determination and power analysis in metabolic phenotyping and integrative metabolomics: an application framework based on a systematic review of literature.Metabolomics : Official journal of the Metabolomic Society · 2026Pooled it
- Metabolomics and metabolites in cancer diagnosis and treatment.Molecular biomedicine · 2025Pooled it
- ACE inhibitory casein peptide lowers blood pressure and reshapes gut microbiota in a randomized double blind placebo controlled trial.Scientific reports · 2025Trial
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- Multi-omics analysis reveals Lactobacillus and Indolelactic acid involved in small intestinal adaptation of piglet with short bowel syndrome.Nutrition & metabolism · 2025Article
- Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review.Briefings in bioinformatics · 2024Review
- Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.PLoS computational biology · 2024Article
- Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case.BMC medical informatics and decision making · 2024Article
- Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy.Frontiers in immunology · 2024Review
- Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.International journal of medical sciences · 2024Review
- Metabolomics meets systems immunology.EMBO reports · 2023Review
- Supplementation ofMicroorganisms · 2023Article
- The use of ecological analytical tools as an unconventional approach for untargeted metabolomics data analysis: the case of Cecropia obtusifolia and its adaptive responses to nitrate starvation.Functional & integrative genomics · 2022Article
- PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest.Interdisciplinary sciences, computational life sciences · 2022Article
- The Chemistry of Green and Roasted Coffee by Selectable 1D/2D Gas Chromatography Mass Spectrometry with Spectral Deconvolution.Molecules (Basel, Switzerland) · 2022Article
- Recent advances in proteomics and metabolomics in plants.Molecular horticulture · 2022Review
- Treatment of Cancer Gene Changes in Chronic Myeloid Leukemia by Big Data Analysis Platform-Based Dasatinib.Applied bionics and biomechanics · 2022Article
- Theabrownin andFrontiers in pharmacology · 2022Article
- Distinct Urinary Metabolic Biomarkers of Human Colorectal Cancer.Disease markers · 2022Article
- Optimization of metabolomic data processing using NOREVA.Nature protocols · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundMetabolomics data analyses rely on the use of bioinformatics tools. Many integrated multi-functional tools have been developed for untargeted metabolomics data processing and have been widely used. More alternative platforms are expected for both basic and advanced users.
resultsIntegrated mass spectrometry-based untargeted metabolomics data mining (IP4M) software was designed and developed. The IP4M, has 62 functions categorized into 8 modules, covering all the steps of metabolomics data mining, including raw data preprocessing (alignment, peak de-convolution, peak picking, and isotope filtering), peak annotation, peak table preprocessing, basic statistical description, classification and biomarker detection, correlation analysis, cluster and sub-cluster analysis, regression analysis, ROC analysis, pathway and enrichment analysis, and sample size and power analysis. Additionally, a KEGG-derived metabolic reaction database was embedded and a series of ratio variables (product/substrate) can be generated with enlarged information on enzyme activity. A new method, GRaMM, for correlation analysis between metabolome and microbiome data was also provided. IP4M provides both a number of parameters for customized and refined analysis (for expert users), as well as 4 simplified workflows with few key parameters (for beginners who are unfamiliar with computational metabolomics). The performance of IP4M was evaluated and compared with existing computational platforms using 2 data sets derived from standards mixture and 2 data sets derived from serum samples, from GC-MS and LC-MS respectively.
conclusionIP4M is powerful, modularized, customizable and easy-to-use. It is a good choice for metabolomics data processing and analysis. Free versions for Windows, MAC OS, and Linux systems are provided.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.