Daily Archives: 2026年9月11日

肠道微生物组多组学：鸟枪法宏基因组学、代谢组学与精准益生菌

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解码肠道微生物组。整合多组学。设计精准益生菌策略。

关于本课程

本次为期3天的研讨会将实用性地介绍肠道微生物组多组学，结合鸟枪法宏基因组学、代谢组学、机器学习和精准益生菌设计。参与者将学习如何使用免费获取的计算工具来分析微生物群落、解释代谢物图谱、整合多组学数据集，并识别具有生物学意义的特征。

每天均包含使用 Google Colab 或 Jupyter Notebook 的动手实践活动，使本研讨会非常适合寻求现代微生物组数据分析实践经验的科研人员和专业人士。

目标

旨在为参与者提供实用的知识和计算技能，以分析肠道微生物组宏基因组和代谢组数据，整合多组学数据集，并将微生物特征转化为数据驱动的精准益生菌策略。

研讨会目标

  • 了解肠道微生物组研究和鸟枪法宏基因组学的基础知识。
  • 对微生物组数据集进行分类学和功能分析。
  • 探索关键的肠道微生物代谢物及代谢组学工作流程。
  • 将统计学和机器学习方法应用于微生物组数据。
  • 整合宏基因组和代谢组数据集。
  • 识别微生物、代谢和功能生物标志物。
  • 理解微生物与代谢物之间的关系。
  • 探索数据驱动的方法，用于精准益生菌候选菌株的优先筛选。
  • 获得使用开源和免费生物信息学工具的实践经验。

研讨会结构

📅 第1天：用于肠道微生物组分析的鸟枪法宏基因组学

  • 重点：了解使用鸟枪法宏基因组学进行肠道微生物组分析、分类学分析、微生物多样性及功能通路解释。
  • 介绍健康、疾病、营养和治疗研究中的肠道微生物组及宿主-微生物相互作用。
  • 了解16S rRNA测序与用于微生物组分析的鸟枪法宏基因组学之间的区别。
  • 处理 FASTQ 数据，进行质量控制、预处理和微生物组数据集的序列读取清洗。
  • 在物种和菌株水平分辨率下，对肠道微生物组样本进行分类学分析。
  • 微生物丰度、多样性分析、功能基因分析及通路水平解释。
  • 解释与肠道健康、菌群失调、疾病状态及益生菌相关性相关的微生物组特征。

🛠️ 动手实践：

  • 对肠道微生物组数据集进行分类学和功能分析。

🧰 涵盖工具：Google Colab, Python, FastQC, fastp, Kraken2, Bracken, MetaPhlAn, HUMAnN, pandas, Matplotlib

📅 第2天：肠道代谢组学与微生物-代谢物分析

  • 重点：探索肠道代谢组学、微生物代谢物、数据标准化、生物标志物发现以及微生物-代谢物关联概念。
  • 介绍用于肠道微生物组和宿主反应研究的靶向和非靶向代谢组学。
  • 概述用于肠道代谢物分析的 LC-MS（液相色谱-质谱）、GC-MS（气相色谱-质谱）和基于 NMR（核磁共振）的代谢组学平台。
  • 了解代谢组学数据预处理、标准化、缩放和质量评估。
  • 研究关键的肠道相关代谢物，如短链脂肪酸 (SCFAs)、胆汁酸、色氨酸代谢物以及 TMA/TMAO（三甲胺/氧化三甲胺）。
  • 应用主成分分析 (PCA)、样本聚类和差异代谢物分析来识别代谢特征。
  • 了解肠道健康与疾病研究中的生物标志物发现及微生物-代谢物关联概念。

🛠️ 动手实践：

  • 识别和可视化关键的肠道代谢组学特征。

🧰 涵盖工具：Google Colab, Python, pandas, NumPy, SciPy, scikit-learn, Matplotlib, MetaboAnalyst, GNPS

📅 第3天：多组学整合、机器学习与精准益生菌

  • 重点：整合宏基因组和代谢组数据以发现生物标志物、构建预测模型，并优先筛选精准益生菌候选菌株。
  • 整合宏基因组和代谢组数据以进行肠道微生物组多组学分析。
  • 微生物-代谢物相关性分析，以识别微生物与代谢物之间的功能关系。
  • 多组学生物标志物发现、特征选择和数据整合策略。
  • 基于随机森林的预测、ROC-AUC 分析以及肠道微生物组数据集的模型评估。
  • 了解特征重要性，并介绍用于可解释机器学习的 SHAP 方法。
  • 识别与肠道健康和疾病相关特征相关的微生物和代谢功能缺陷。
  • 精准益生菌候选菌株优先筛选、下一代益生菌、合生元及微生物群落概念。

🛠️ 动手实践：

  • 构建多组学模型以识别关键的微生物/代谢物特征，并优先筛选益生菌候选菌株。

🧰 涵盖工具：Google Colab, Python, pandas, scikit-learn, SHAP, SciPy, Matplotlib, NetworkX, MOFA2, mixOmics

谁应该报名？

  • 研究人员与研究学者
  • 博士及博士后研究人员
  • 学术界人士与教职员工
  • 微生物学家与微生物组研究人员
  • 生物信息学家与计算生物学家
  • 生物技术与生命科学专业人士
  • 代谢组学与多组学研究人员
  • 营养学与食品科学研究人员
  • 益生菌与功能性食品研究人员
  • 制药与生物技术研发专业人士
  • 从事宿主-微生物相互作用及微生物治疗研究的科学家
  • 对多组学、机器学习和精准微生物组研究感兴趣的专业人士

重要日期

注册截止 2026年9月16日 印度标准时间 (IST) 下午 4:30

研讨会日期 2026年9月16日 – 2026年9月18日 印度标准时间 (IST) 下午 5:30

研讨会成果

研讨会结束后，参与者将能够：

  • 解读鸟枪法宏基因组数据集和微生物丰度图谱。
  • 分析肠道微生物组中的功能基因和代谢通路。
  • 处理并可视化肠道代谢组学数据集。
  • 识别重要的代谢物和微生物生物标志物。
  • 进行微生物-代谢物相关性分析。
  • 应用 PCA、随机森林、ROC-AUC 和特征重要性分析。
  • 整合微生物组和代谢组数据集以进行多组学解释。
  • 识别潜在的功能性和代谢性菌群失调特征。
  • 基于多组学证据优先筛选潜在的益生菌菌株或微生物群落。
  • 使用 Google Colab 和开源工具开发可重复的工作流程。
Gut Microbiome Multi-Omics: Shotgun Metagenomics, Metabolomics & Precision Probiotics

September 16, 2026 Registration closes September 16, 2026

Gut Microbiome Multi-Omics: Shotgun Metagenomics, Metabolomics & Precision Probiotics

Decode the Gut Microbiome. Integrate Multi-Omics. Design Precision Probiotic Strategies. Enroll Now Batch Enrolment More Details

Mode:
Virtual / Online
Type:
Mentor Based
Level:
Advanced
Duration:
3 Days(60-90 min per day)
Starts:
16 September 2026
Time:
5:30 PM IST

About This Course

This 3-day workshop provides a practical introduction to gut microbiome multi-omics, combining shotgun metagenomics, metabolomics, machine learning, and precision probiotic design. Participants will learn how to analyze microbial communities, interpret metabolite profiles, integrate multi-omics datasets, and identify biologically meaningful signatures using freely accessible computational tools.

Each day includes a hands-on activity using Google Colab or Jupyter Notebook, making the workshop suitable for researchers and professionals seeking practical exposure to modern microbiome data analysis.

Aim

To equip participants with practical knowledge and computational skills to analyze gut microbiome metagenomic and metabolomic data, integrate multi-omics datasets, and translate microbial signatures into data-driven precision probiotic strategies. Workshop Objectives

Understand the fundamentals of gut microbiome research and shotgun metagenomics.
Perform taxonomic and functional profiling of microbiome datasets.
Explore key gut microbial metabolites and metabolomics workflows.
Apply statistical and machine learning approaches to microbiome data.
Integrate metagenomic and metabolomic datasets.
Identify microbial, metabolic, and functional biomarkers.
Understand microbe–metabolite relationships.
Explore data-driven approaches for precision probiotic candidate prioritization.
Gain hands-on experience with open-source and freely accessible bioinformatics tools.

Workshop Structure

📅 Day 1: Shotgun Metagenomics for Gut Microbiome Profiling

Focus: Understanding gut microbiome profiling using shotgun metagenomics, taxonomic analysis, microbial diversity, and functional pathway interpretation.
Introduction to gut microbiome and host–microbe interactions in health, disease, nutrition, and therapeutic research.
Understanding the difference between 16S rRNA sequencing and shotgun metagenomics for microbiome analysis.
Working with FASTQ data, quality control, preprocessing, and sequence-read cleaning for microbiome datasets.
Taxonomic profiling of gut microbiome samples at species and strain-level resolution.
Microbial abundance, diversity analysis, functional gene profiling, and pathway-level interpretation.
Interpreting microbiome signatures linked with gut health, dysbiosis, disease states, and probiotic relevance.

🛠️ Hands-on:

Taxonomic and functional profiling of a gut microbiome dataset.

🧰 Tools Covered: Google Colab, Python, FastQC, fastp, Kraken2, Bracken, MetaPhlAn, HUMAnN, pandas, Matplotlib

📅 Day 2: Gut Metabolomics & Microbe–Metabolite Analysis

Focus: Exploring gut metabolomics, microbial metabolites, data normalization, biomarker discovery, and microbe–metabolite association concepts.
Introduction to targeted and untargeted metabolomics for gut microbiome and host-response research.
Overview of LC-MS, GC-MS, and NMR-based metabolomics platforms used in gut metabolite profiling.
Understanding metabolomics data preprocessing, normalization, scaling, and quality assessment.
Studying key gut-related metabolites such as SCFAs, bile acids, tryptophan metabolites, and TMA/TMAO.
Applying PCA, sample clustering, and differential metabolite analysis for metabolic signature identification.
Understanding biomarker discovery and microbe–metabolite association concepts in gut health and disease research.

🛠️ Hands-on:

Identification and visualization of key gut metabolomic signatures.

🧰 Tools Covered: Google Colab, Python, pandas, NumPy, SciPy, scikit-learn, Matplotlib, MetaboAnalyst, GNPS

📅 Day 3: Multi-Omics Integration, Machine Learning & Precision Probiotics

Focus: Integrating metagenomics and metabolomics data to discover biomarkers, build prediction models, and prioritize precision probiotic candidates.
Integration of metagenomics and metabolomics data for gut microbiome multi-omics analysis.
Microbe–metabolite correlation analysis for identifying functional relationships between microbes and metabolites.
Multi-omics biomarker discovery, feature selection, and data integration strategies.
Random Forest-based prediction, ROC-AUC analysis, and model evaluation for gut microbiome datasets.
Understanding feature importance and introduction to SHAP for interpretable machine learning.
Identification of microbial and metabolic functional deficits linked with gut health and disease-associated signatures.
Precision probiotic candidate prioritization, next-generation probiotics, synbiotics, and microbial consortia concepts.

🛠️ Hands-on:

Build a multi-omics model to identify key microbial/metabolite features and prioritize probiotic candidates.

🧰 Tools Covered: Google Colab, Python, pandas, scikit-learn, SHAP, SciPy, Matplotlib, NetworkX, MOFA2, mixOmics Who Should Enrol?

Researchers and Research Scholars
PhD and Postdoctoral Researchers
Academicians and Faculty Members
Microbiologists and Microbiome Researchers
Bioinformaticians and Computational Biologists
Biotechnology and Life Science Professionals
Metabolomics and Omics Researchers
Nutrition and Food Science Researchers
Probiotic and Functional Food Researchers
Pharmaceutical and Biotechnology R&D Professionals
Scientists working in host–microbe interactions and microbial therapeutics
Professionals interested in multi-omics, machine learning, and precision microbiome research

Important Dates Registration Ends

September 16, 2026 IST 4:30 PM

Workshop Dates

September 16, 2026 – September 18, 2026 IST 5:30 PM

Workshop Outcomes

By the end of the workshop, participants will be able to:

Interpret shotgun metagenomics datasets and microbial abundance profiles.
Analyze functional genes and metabolic pathways in the gut microbiome.
Process and visualize gut metabolomics datasets.
Identify important metabolites and microbial biomarkers.
Perform microbe–metabolite correlation analysis.
Apply PCA, Random Forest, ROC-AUC, and feature importance analysis.
Integrate microbiome and metabolomics datasets for multi-omics interpretation.
Identify potential functional and metabolic dysbiosis signatures.
Prioritize potential probiotic strains or microbial consortia based on multi-omics evidence.
Develop reproducible workflows using Google Colab and open-source tools.