diff --git a/WORKSHOP_THEMES_ANALYSIS.md b/WORKSHOP_THEMES_ANALYSIS.md new file mode 100644 index 0000000..f6e0e35 --- /dev/null +++ b/WORKSHOP_THEMES_ANALYSIS.md @@ -0,0 +1,463 @@ +# QLS-MiCM Workshop Themes Analysis + +*Analysis Date: January 2026* + +This document provides a comprehensive analysis of the current QLS-MiCM workshop offerings, identifies thematic categories, and recommends missing topics for future development. + +--- + +## Current Workshop Inventory + +The QLS-MiCM organization currently hosts **37 repositories** covering various computational biology and data science topics. + +--- + +## Proposed Learning Pathways (Ready to Use) + +Based on existing workshops, here are populated learning pathways following the template format: + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Programming BasicsR TrackPython TrackMachine LearningRNA-seqGenetics
+ 1. HowToThinkInCode
+ 2. Intro-to-UNIX
+ 3. IntroToGitHub + 		+ 1. IntroToR
+ 2. StatsInR
+ 3. Data Wrangling
+ 4. Advanced Data Viz + 		+ 1. IntroToPython
+ 2. Data Processing
+ 3. Intermediate Python
+ 4. Dimensionality Reduction + 		+ 1. Intro to ML
+ 2. Supervised ML
+ 3. Intro to CNNs + 		+ 1. Intro to RNA-seq
+ 2. Data Processing
+ 3. Statistical Foundations
+ 4. Differential Expression
+ 5. Single-Cell RNA-seq + 		+ 1. Data Processing
+ 2. GWAS & PRS
+ 3. Intro to PRS +
Pre-requisites
NoneNoneNonePython TrackR Track + UNIXPython Track
+ +### Additional Pathways + + + + + + + + + + + + + + + + + + + + +
MATLAB TrackSingle-Cell AnalysisStructural Biology
+ 1. Intro to MATLAB
+ 2. Exploring MATLAB
+ 3. Image Processing + 		+ 1. Intro to Single-Cell
+ 2. Single-Cell RNA-seq
+ 3. Clustering & Trajectory + 		+ 1. Molecular Simulations
+ 2. 🚧 Gap: Protein Structure Prediction
+ 3. 🚧 Gap: Molecular Docking +
Pre-requisites
NoneR Track + RNA-seq basicsPython Track
+ +--- + +## Proposed NEW Pathways (To Be Developed) + +These pathways represent gaps in the current offerings: + + + + + + + + + + + + + + + + + + + + + + + +
🆕 Reproducible Research🆕 Multi-Omics🆕 Cloud & HPC🆕 Microbiome
+ 1. IntroToGitHub (exists)
+ 2. 🚧 Containerization (Docker)
+ 3. 🚧 Workflow Managers
+ 4. 🚧 FAIR Data Principles + 		+ 1. RNA-seq pathway (exists)
+ 2. 🚧 ATAC-seq Analysis
+ 3. 🚧 Spatial Transcriptomics
+ 4. 🚧 Multi-Omics Integration + 		+ 1. Intro-to-UNIX (exists)
+ 2. 🚧 Intro to HPC (SLURM)
+ 3. 🚧 Cloud Computing Basics
+ 4. 🚧 Nextflow/Snakemake + 		+ 1. UNIX + Python (exists)
+ 2. 🚧 16S rRNA Analysis
+ 3. 🚧 Shotgun Metagenomics
+ 4. 🚧 QIIME2 Workshop +
Pre-requisites
Programming BasicsR Track + RNA-seqUNIX + PythonUNIX + Python/R
+ +--- + +## Workshop Themes & Categories + +### Theme 1: Programming Fundamentals +*Foundation skills for computational work* + +| Workshop | Language | Description | +|----------|----------|-------------| +| IntroToPython | Python | Python fundamentals | +| IntroToR | R | R programming basics | +| Intro-to-UNIX | Shell | Command-line computing | +| Intro-to-MATLAB | MATLAB | MATLAB introduction | +| Exploring_MATLAB | MATLAB | Extended MATLAB exploration | +| HowToThinkInCode | - | Computational thinking concepts | +| IntroToGitHub | R | Version control basics | +| intermediate_python_fall_2023 | Python | Intermediate Python skills | + +**Coverage Assessment:** ✅ Strong coverage of major scientific computing languages + +--- + +### Theme 2: Data Processing & Wrangling +*Handling and transforming data* + +| Workshop | Language | Description | +|----------|----------|-------------| +| DataProcessingInPython | Python | Data manipulation with Python | +| Data_Wrangling_Fall2022 | R | Data wrangling techniques | +| DataProcessingForGenetics | Python | Genetics-specific data processing | + +**Coverage Assessment:** ⚠️ Moderate - could expand with more domain-specific data handling + +--- + +### Theme 3: Statistics & Visualization +*Statistical methods and data presentation* + +| Workshop | Language | Description | +|----------|----------|-------------| +| StatsInR | R | Statistical analysis fundamentals | +| Advanced-Data-Visualization | R | Advanced visualization (most popular: 9 stars) | +| 2022_Dim_Reduction | Python | Dimensionality reduction techniques | + +**Coverage Assessment:** ⚠️ Moderate - statistics coverage could be expanded + +--- + +### Theme 4: RNA Sequencing & Transcriptomics +*Bulk RNA-seq analysis pipeline* + +| Workshop | Language | Description | +|----------|----------|-------------| +| Introduction-to-RNA-seq | Shell | RNA-seq basics | +| RNA-seq-Data-Processing | Python | RNA-seq data processing | +| Statistical-Foundations-of-RNA-seq | R | Statistical methods for RNA-seq | +| Differential-Gene-Expression-Analysis | HTML/R | DGE analysis | +| BootcampF22_RNAseqQuantification | - | RNA-seq quantification | +| BootcampF22_IntroRNAseqFormats | - | RNA-seq file formats | +| BootcampF22_LongRead_RNAseq | - | Long-read sequencing | +| Transcriptomics-Bootcamp-2022 | - | Comprehensive bootcamp | +| ngsintro_summer2022 | HTML | NGS formats and preprocessing | + +**Coverage Assessment:** ✅ Excellent - comprehensive RNA-seq curriculum + +--- + +### Theme 5: Single-Cell Analysis +*Single-cell technologies* + +| Workshop | Language | Description | +|----------|----------|-------------| +| Single-Cell-RNA-seq | R | Single-cell RNA-seq analysis | +| BootcampF22_Intro_Singlecell | HTML | Introduction to single-cell | +| BootcampF22_singleCell_Clustering_trajectoryInference | - | Clustering & trajectory analysis | + +**Coverage Assessment:** ✅ Good foundation coverage + +--- + +### Theme 6: Genetics & Genomics +*Genetic variation and association studies* + +| Workshop | Language | Description | +|----------|----------|-------------| +| GWAS-and-Polygenic-Risk-Scores | Python | GWAS and PRS analysis | +| Intro_PRS | R | Introduction to PRS | +| ChIP_seq | - | ChIP-seq analysis (forked) | +| Proteogenomics | HTML | Proteogenomics approaches | + +**Coverage Assessment:** ⚠️ Moderate - could expand variant analysis + +--- + +### Theme 7: Machine Learning & AI +*ML/AI applications in life sciences* + +| Workshop | Language | Description | +|----------|----------|-------------| +| Summer23-Intro-to-ML-with-Python | Python | ML fundamentals | +| SupervisedML | Python | Supervised learning methods | +| Fall23_Intro-to-CNNs | Python | Convolutional neural networks | + +**Coverage Assessment:** ⚠️ Limited - significant expansion opportunity + +--- + +### Theme 8: Structural Biology & Simulations +*Molecular structures and dynamics* + +| Workshop | Language | Description | +|----------|----------|-------------| +| IntroToMolecularSimulations | Python | Molecular dynamics basics | +| ImageProcessingInMATLAB | MATLAB | Image processing for biology | + +**Coverage Assessment:** ❌ Minimal - major gap area + +--- + +## Gap Analysis: Missing Topics + +### High Priority (Essential for Modern Computational Biology) + +#### 1. **Deep Learning & Neural Networks Expansion** +- Transformer models for biology (protein language models, genomic foundation models) +- Graph neural networks for molecular data +- Generative AI applications in drug discovery +- Natural language processing for biomedical text mining + +#### 2. **Cloud Computing & High-Performance Computing** +- Introduction to HPC clusters (SLURM, PBS) +- Cloud computing for bioinformatics (AWS, Google Cloud, Azure) +- Containerization (Docker, Singularity) for reproducible research +- Workflow managers (Nextflow, Snakemake) + +#### 3. **Multi-Omics Integration** +- Integrative multi-omics analysis +- Spatial transcriptomics +- ATAC-seq and chromatin accessibility +- Metabolomics data analysis +- Epigenomics (DNA methylation, histone modifications) + +#### 4. **Reproducibility & Best Practices** +- Reproducible research practices +- Data management and FAIR principles +- Scientific computing best practices +- Literate programming (R Markdown, Quarto, Jupyter best practices) + +### Medium Priority (Valuable Additions) + +#### 5. **Variant Analysis & Clinical Genomics** +- Variant calling and annotation +- Structural variant analysis +- Clinical interpretation of variants +- Pharmacogenomics + +#### 6. **Structural Bioinformatics** +- Protein structure prediction (AlphaFold, ESMFold) +- Molecular docking +- Structure-based drug design +- Cryo-EM data processing + +#### 7. **Metagenomics & Microbiome** +- 16S rRNA analysis +- Shotgun metagenomics +- Microbiome data analysis with QIIME2 +- Functional annotation of microbial communities + +#### 8. **Systems Biology** +- Pathway analysis +- Network biology +- Gene regulatory networks +- Metabolic modeling + +### Lower Priority (Specialized Topics) + +#### 9. **Time Series & Longitudinal Data** +- Time series analysis for biological data +- Longitudinal study design and analysis +- Dynamic modeling + +#### 10. **Advanced Statistics** +- Bayesian statistics for biology +- Causal inference methods +- Survival analysis in clinical research +- Mixed effects models + +#### 11. **Image Analysis** +- Deep learning for biological image analysis +- Cell segmentation and tracking +- Histopathology image analysis +- High-content screening analysis + +#### 12. **Database & Data Engineering** +- SQL for biological databases +- Building and querying biological databases +- APIs for biological data retrieval +- Data pipelines and ETL + +--- + +## Recommendations Summary + +### Immediate Development Priorities + +| Rank | Workshop Topic | Rationale | +|------|---------------|-----------| +| 1 | **Workflow Managers (Nextflow/Snakemake)** | Critical for reproducible pipelines; bridges all sequencing workshops | +| 2 | **Intro to HPC & Cloud Computing** | Essential skill gap; enables larger-scale analyses | +| 3 | **Multi-Omics Integration** | Builds on strong RNA-seq foundation | +| 4 | **AlphaFold & Protein Structure Prediction** | High interest topic; minimal structural biology coverage | +| 5 | **Spatial Transcriptomics** | Natural extension of single-cell expertise | + +### Suggested Workshop Series + +**Series A: From Sequences to Systems** +1. Intro to RNA-seq (existing) +2. Single-Cell RNA-seq (existing) +3. *NEW: Spatial Transcriptomics* +4. *NEW: Multi-Omics Integration* + +**Series B: Reproducible Bioinformatics** +1. IntroToGitHub (existing) +2. *NEW: Containerization with Docker/Singularity* +3. *NEW: Workflow Managers* +4. *NEW: HPC & Cloud Computing* + +**Series C: AI for Life Sciences** +1. Intro to ML (existing) +2. Supervised ML (existing) +3. CNNs (existing) +4. *NEW: Deep Learning for Genomics* +5. *NEW: Protein Language Models & AlphaFold* + +**Series D: Genomic Variation** +1. GWAS & PRS (existing) +2. *NEW: Variant Calling & Annotation* +3. *NEW: Clinical Genomics Interpretation* + +--- + +## Current Strengths + +1. **RNA-seq Pipeline**: Comprehensive coverage from basics to advanced analysis +2. **Programming Languages**: Good coverage of R, Python, MATLAB, and Shell +3. **Single-Cell**: Solid foundation with room to grow +4. **Accessibility**: Materials are public and well-organized + +## Areas for Growth + +1. **Reproducibility Infrastructure**: Containers, workflows, version control expansion +2. **AI/ML Depth**: Current coverage is introductory; deeper content needed +3. **Structural Biology**: Minimal coverage despite field importance +4. **Cloud/HPC**: Missing critical computational infrastructure skills +5. **Multi-Omics**: Siloed by data type; integration workshops needed + +--- + +## Appendix: Complete Repository List + +| Repository | Primary Language | Stars | Theme | +|------------|-----------------|-------|-------| +| Advanced-Data-Visualization | R | 9 | Statistics & Viz | +| SupervisedML | Python | - | Machine Learning | +| Workshop_Template | - | - | Infrastructure | +| DataProcessingInPython | Python | - | Data Processing | +| Exploring_MATLAB | MATLAB | - | Programming | +| IntroToPython | Python | - | Programming | +| Single-Cell-RNA-seq | R | 2 | Single-Cell | +| Statistical-Foundations-of-RNA-seq | R | 3 | RNA-seq | +| Introduction-to-RNA-seq | Shell | - | RNA-seq | +| RNA-seq-Data-Processing | Python | - | RNA-seq | +| Differential-Gene-Expression-Analysis | HTML | - | RNA-seq | +| DataProcessingForGenetics | Python | - | Genetics | +| GWAS-and-Polygenic-Risk-Scores | Python | - | Genetics | +| Proteogenomics | HTML | - | Genetics | +| IntroToR | R | - | Programming | +| StatsInR | R | - | Statistics | +| Intro-to-UNIX | Shell | 2 | Programming | +| Intro-to-MATLAB | MATLAB | - | Programming | +| HowToThinkInCode | - | - | Programming | +| IntroToGitHub | R | - | Programming | +| IntroToMolecularSimulations | Python | 2 | Structural | +| Fall23_Intro-to-CNNs | Python | - | Machine Learning | +| Summer23-Intro-to-ML-with-Python | Python | 2 | Machine Learning | +| Transcriptomics-Bootcamp-2022 | - | 3 | RNA-seq | +| intermediate_python_fall_2023 | Python | - | Programming | +| ImageProcessingInMATLAB | MATLAB | - | Structural | +| ChIP_seq | - | - | Genetics | +| BootcampF22_RNAseqQuantification | - | - | RNA-seq | +| BootcampF22_IntroRNAseqFormats | - | - | RNA-seq | +| BootcampF22_Intro_Singlecell | HTML | - | Single-Cell | +| BootcampF22_singleCell_Clustering_trajectoryInference | - | - | Single-Cell | +| BootcampF22_LongRead_RNAseq | - | - | RNA-seq | +| Data_Wrangling_Fall2022 | R | - | Data Processing | +| ngsintro_summer2022 | HTML | - | RNA-seq | +| Intro_PRS | R | - | Genetics | +| 2022_Dim_Reduction | Python | - | Statistics | + +--- + +*Document prepared for QLS-MiCM workshop planning*