# Bioinformatics and Genomics - Processes Backlog

## Overview

This document outlines the key processes for the Bioinformatics and Genomics specialization. These processes cover the full spectrum of computational biology workflows, from sequence analysis and variant interpretation to multi-omics integration and clinical genomics applications.

## Process Categories

### 1. Genomic Analysis and Variant Calling

#### 1.1 Whole Genome Sequencing Pipeline (wgs-analysis-pipeline)
**Priority:** High
**Complexity:** High
**Description:** End-to-end pipeline for whole genome sequencing analysis including read alignment, variant calling, annotation, and quality control. Implements GATK best practices with support for germline and somatic workflows.

**Key Activities:**
- FASTQ quality assessment and preprocessing
- Read alignment with BWA-MEM2 or similar
- Duplicate marking and base quality recalibration
- Germline/somatic variant calling with GATK or DeepVariant
- Structural variant detection with Manta/DELLY
- Comprehensive variant annotation with VEP/ANNOVAR
- Multi-sample joint genotyping

**Outputs:** Aligned BAM files, annotated VCF files, QC reports, analysis summary

---

#### 1.2 Clinical Variant Interpretation (clinical-variant-interpretation)
**Priority:** High
**Complexity:** High
**Description:** Systematic process for interpreting genetic variants according to ACMG/AMP guidelines for clinical reporting. Includes pathogenicity classification, evidence curation, and report generation.

**Key Activities:**
- Variant classification using ACMG criteria
- Population frequency filtering (gnomAD, ExAC)
- Literature and database evidence review
- In silico prediction tool integration (CADD, REVEL)
- Inheritance pattern analysis
- Clinical correlation assessment
- Report generation with actionable findings

**Outputs:** Classified variants, evidence summaries, clinical reports

---

#### 1.3 Pharmacogenomics Analysis (pharmacogenomics-analysis)
**Priority:** Medium
**Complexity:** Medium
**Description:** Analysis of genetic variants affecting drug metabolism, efficacy, and adverse reactions. Supports precision medicine by identifying patient-specific pharmacogenomic markers.

**Key Activities:**
- Star allele calling for key PGx genes
- Diplotype to phenotype translation
- Drug-gene interaction assessment
- Clinical actionability evaluation
- Integration with PharmGKB/CPIC guidelines
- Dosing recommendation generation
- Pharmacogenomic report creation

**Outputs:** PGx genotypes, phenotype predictions, drug recommendations

---

### 2. Transcriptomics and Gene Expression

#### 2.1 RNA-seq Differential Expression Analysis (rnaseq-differential-expression)
**Priority:** High
**Complexity:** Medium
**Description:** Comprehensive RNA-seq analysis workflow for identifying differentially expressed genes between experimental conditions. Includes normalization, statistical modeling, and pathway enrichment.

**Key Activities:**
- Read quality control and preprocessing
- Alignment with STAR or pseudo-alignment with Salmon
- Gene-level quantification and normalization
- Batch effect assessment and correction
- Differential expression with DESeq2/edgeR
- Multiple testing correction
- Gene ontology and pathway enrichment analysis

**Outputs:** Normalized counts, differential expression results, pathway reports, visualizations

---

#### 2.2 Single-Cell RNA-seq Analysis (scrnaseq-analysis)
**Priority:** High
**Complexity:** High
**Description:** End-to-end analysis pipeline for single-cell RNA sequencing data including quality control, normalization, clustering, cell type annotation, and trajectory analysis.

**Key Activities:**
- Cell barcode and UMI processing
- Quality filtering (doublet removal, ambient RNA)
- Normalization and feature selection
- Dimensionality reduction (PCA, UMAP)
- Clustering and marker gene identification
- Automated cell type annotation
- Trajectory and pseudotime analysis
- Cell-cell communication analysis

**Outputs:** Annotated cell clusters, marker genes, trajectory analysis, cell type proportions

---

#### 2.3 Spatial Transcriptomics Analysis (spatial-transcriptomics)
**Priority:** Medium
**Complexity:** High
**Description:** Analysis of spatially-resolved transcriptomics data to understand gene expression patterns in tissue context. Integrates with histological imaging for comprehensive tissue characterization.

**Key Activities:**
- Image processing and spot detection
- Expression quantification per spatial location
- Spatial clustering and domain identification
- Integration with H&E pathology images
- Spatially variable gene identification
- Cell type deconvolution
- Ligand-receptor spatial mapping

**Outputs:** Spatial expression maps, tissue domains, spatially variable genes

---

### 3. Proteomics and Mass Spectrometry

#### 3.1 Mass Spectrometry Proteomics Pipeline (ms-proteomics-pipeline)
**Priority:** High
**Complexity:** High
**Description:** Comprehensive pipeline for processing and analyzing mass spectrometry-based proteomics data including protein identification, quantification, and statistical analysis.

**Key Activities:**
- Raw file conversion and peak detection
- Database searching with MaxQuant/MSFragger
- False discovery rate control
- Label-free or labeled quantification
- Normalization and missing value imputation
- Differential abundance analysis
- Post-translational modification identification
- Protein-protein interaction network analysis

**Outputs:** Protein identifications, quantification matrices, differential proteins, PTM sites

---

#### 3.2 Multi-Omics Data Integration (multi-omics-integration)
**Priority:** High
**Complexity:** High
**Description:** Integrative analysis combining genomics, transcriptomics, proteomics, and metabolomics data to provide comprehensive biological insights and systems-level understanding.

**Key Activities:**
- Data preprocessing and harmonization
- Feature matching across omics layers
- Multi-omics factor analysis (MOFA)
- Network-based integration
- Pathway-level integration
- Correlation analysis across data types
- Integrative clustering and patient stratification
- Causal inference modeling

**Outputs:** Integrated datasets, multi-omics clusters, pathway associations, biomarker panels

---

### 4. Structural Biology and Drug Discovery

#### 4.1 Protein Structure Prediction (protein-structure-prediction)
**Priority:** Medium
**Complexity:** High
**Description:** Computational prediction of protein 3D structures using AlphaFold, RoseTTAFold, and homology modeling approaches. Includes structure validation and quality assessment.

**Key Activities:**
- Sequence-based structure prediction with AlphaFold
- Template selection for homology modeling
- Model quality assessment (pLDDT, QMEAN)
- Structure refinement and optimization
- Active site and binding pocket identification
- Structure comparison and alignment
- Functional annotation from structure

**Outputs:** Predicted 3D structures, confidence scores, functional annotations

---

#### 4.2 Molecular Docking and Virtual Screening (molecular-docking)
**Priority:** Medium
**Complexity:** Medium
**Description:** Computational docking of small molecules to protein targets for drug discovery and lead optimization. Includes virtual screening of compound libraries.

**Key Activities:**
- Protein preparation and grid generation
- Ligand preparation and conformer generation
- Molecular docking with AutoDock/GOLD
- Scoring function evaluation
- Pose analysis and visualization
- Virtual screening of compound libraries
- Binding affinity prediction
- ADMET property prediction

**Outputs:** Docking poses, binding scores, ranked compound lists, lead candidates

---

### 5. Metagenomics and Microbiome

#### 5.1 16S rRNA Microbiome Analysis (16s-microbiome-analysis)
**Priority:** Medium
**Complexity:** Medium
**Description:** Analysis of 16S rRNA amplicon sequencing data for microbial community profiling including taxonomic classification, diversity analysis, and differential abundance testing.

**Key Activities:**
- Read quality filtering and denoising
- ASV/OTU generation with QIIME2/DADA2
- Taxonomic classification
- Alpha and beta diversity analysis
- Differential abundance testing
- Phylogenetic analysis
- Functional prediction (PICRUSt2)
- Community composition visualization

**Outputs:** Taxonomic profiles, diversity metrics, differentially abundant taxa, functional predictions

---

#### 5.2 Shotgun Metagenomics Pipeline (shotgun-metagenomics)
**Priority:** Medium
**Complexity:** High
**Description:** Comprehensive analysis of shotgun metagenomic sequencing data for taxonomic and functional profiling of microbial communities with strain-level resolution.

**Key Activities:**
- Read quality control and host decontamination
- Taxonomic profiling with MetaPhlAn/Kraken2
- Metagenomic assembly with MEGAHIT
- Gene prediction and annotation
- Functional profiling with HUMAnN
- Antimicrobial resistance gene detection
- Strain-level analysis
- Metabolic pathway reconstruction

**Outputs:** Taxonomic profiles, assembled contigs, functional annotations, AMR genes

---

### 6. Clinical and Translational Genomics

#### 6.1 Tumor Molecular Profiling (tumor-molecular-profiling)
**Priority:** High
**Complexity:** High
**Description:** Comprehensive molecular characterization of tumors for precision oncology including somatic mutation calling, copy number analysis, fusion detection, and biomarker assessment.

**Key Activities:**
- Tumor-normal paired analysis
- Somatic mutation calling with Mutect2
- Copy number alteration detection
- Gene fusion identification
- Microsatellite instability assessment
- Tumor mutational burden calculation
- Actionable mutation identification
- Therapy matching and clinical trial matching

**Outputs:** Somatic mutations, CNV profiles, fusions, TMB score, therapy recommendations

---

#### 6.2 Rare Disease Diagnostic Pipeline (rare-disease-diagnostics)
**Priority:** High
**Complexity:** High
**Description:** Specialized workflow for diagnosing rare genetic diseases using exome/genome sequencing with phenotype-driven prioritization and family-based analysis.

**Key Activities:**
- Trio/family-based variant calling
- Phenotype-driven gene prioritization (HPO)
- De novo mutation identification
- Compound heterozygosity detection
- Variant of uncertain significance review
- Gene-disease association validation
- Clinical report generation
- Reanalysis and data reinterpretation

**Outputs:** Candidate diagnostic variants, prioritized gene lists, clinical reports

---

#### 6.3 Newborn Screening Genomics (newborn-screening-genomics)
**Priority:** Medium
**Complexity:** Medium
**Description:** Rapid genomic analysis workflow optimized for newborn screening applications with focus on turnaround time and actionable findings.

**Key Activities:**
- Rapid sequencing data processing
- Targeted gene panel analysis
- Pathogenic variant identification
- Carrier status determination
- Integration with biochemical screening
- Rapid clinical reporting
- Quality assurance and proficiency testing
- Follow-up recommendation generation

**Outputs:** Screening results, carrier reports, recommendations for follow-up

---

### 7. Bioinformatics Infrastructure and Quality

#### 7.1 Analysis Pipeline Validation (pipeline-validation)
**Priority:** High
**Complexity:** Medium
**Description:** Systematic validation of bioinformatics pipelines for accuracy, reproducibility, and clinical use including benchmark testing and performance characterization.

**Key Activities:**
- Reference dataset selection (GIAB, Genome in a Bottle)
- Accuracy assessment (sensitivity, specificity, PPV)
- Reproducibility testing across runs
- Performance benchmarking
- Edge case testing
- Documentation of limitations
- Version control and change management
- Regulatory compliance documentation

**Outputs:** Validation reports, performance metrics, SOPs, compliance documentation

---

#### 7.2 Genomic Data Governance (genomic-data-governance)
**Priority:** High
**Complexity:** Medium
**Description:** Establishing and maintaining data governance frameworks for genomic data including privacy protection, consent management, and regulatory compliance.

**Key Activities:**
- Data classification and sensitivity assessment
- Access control policy implementation
- Consent tracking and management
- De-identification and anonymization procedures
- Audit trail maintenance
- HIPAA/GDPR compliance verification
- Data sharing agreement management
- Breach response procedures

**Outputs:** Governance policies, compliance reports, audit logs, data sharing agreements

---

#### 7.3 Reproducible Research Workflow (reproducible-research)
**Priority:** Medium
**Complexity:** Medium
**Description:** Implementing reproducible research practices for bioinformatics analyses including workflow management, containerization, and documentation standards.

**Key Activities:**
- Workflow development with Nextflow/Snakemake
- Container creation with Docker/Singularity
- Version control for code and data
- Environment specification and packaging
- Documentation and methods writing
- Data and code archival
- FAIR principles implementation
- Continuous integration setup

**Outputs:** Reproducible workflows, containers, documentation, archived datasets

---

### 8. Emerging Technologies and Advanced Methods

#### 8.1 Long-Read Sequencing Analysis (long-read-analysis)
**Priority:** Medium
**Complexity:** High
**Description:** Specialized analysis workflows for long-read sequencing data from Oxford Nanopore and PacBio platforms including assembly, structural variant detection, and methylation calling.

**Key Activities:**
- Basecalling and quality assessment
- Long-read alignment with Minimap2
- De novo assembly
- Structural variant detection
- Phasing and haplotype resolution
- Native DNA methylation calling
- Transcript isoform detection
- Repeat element analysis

**Outputs:** Assemblies, phased variants, methylation profiles, full-length transcripts

---

#### 8.2 CRISPR Screen Analysis (crispr-screen-analysis)
**Priority:** Medium
**Complexity:** Medium
**Description:** Analysis of pooled CRISPR screening data for gene essentiality, drug target identification, and functional genomics studies.

**Key Activities:**
- Guide RNA read counting
- Normalization and quality control
- Gene-level aggregation
- Statistical analysis (MAGeCK, BAGEL)
- Essential gene identification
- Drug-gene interaction analysis
- Pathway enrichment analysis
- Hit validation prioritization

**Outputs:** Gene essentiality scores, drug targets, pathway associations, validation candidates

---

## Implementation Priority Matrix

| Process | Priority | Complexity | Dependencies |
|---------|----------|------------|--------------|
| wgs-analysis-pipeline | High | High | None |
| clinical-variant-interpretation | High | High | wgs-analysis-pipeline |
| rnaseq-differential-expression | High | Medium | None |
| scrnaseq-analysis | High | High | None |
| ms-proteomics-pipeline | High | High | None |
| multi-omics-integration | High | High | Multiple omics pipelines |
| tumor-molecular-profiling | High | High | wgs-analysis-pipeline |
| rare-disease-diagnostics | High | High | wgs-analysis-pipeline, clinical-variant-interpretation |
| pipeline-validation | High | Medium | All pipelines |
| genomic-data-governance | High | Medium | None |
| pharmacogenomics-analysis | Medium | Medium | wgs-analysis-pipeline |
| spatial-transcriptomics | Medium | High | scrnaseq-analysis |
| protein-structure-prediction | Medium | High | None |
| molecular-docking | Medium | Medium | protein-structure-prediction |
| 16s-microbiome-analysis | Medium | Medium | None |
| shotgun-metagenomics | Medium | High | None |
| newborn-screening-genomics | Medium | Medium | wgs-analysis-pipeline |
| reproducible-research | Medium | Medium | None |
| long-read-analysis | Medium | High | None |
| crispr-screen-analysis | Medium | Medium | None |

## Cross-References

### Related Specializations
- **Data Science and Machine Learning**: Deep learning for genomics, biomarker discovery, predictive modeling
- **Data Engineering**: Genomic data pipelines, data lakes, ETL workflows
- **DevOps/SRE**: Pipeline deployment, CI/CD for bioinformatics, cloud infrastructure
- **Software Architecture**: Scalable genomics platforms, API design, microservices
- **Security and Compliance**: HIPAA compliance, genomic data protection, consent management

### External Standards and Guidelines
- ACMG/AMP variant interpretation guidelines
- GA4GH data sharing framework
- FAIR data principles
- GATK best practices
- ENCODE analysis standards
- nf-core community pipelines

## Version History

| Version | Date | Changes |
|---------|------|---------|
| 1.0 | 2026-01-23 | Initial processes backlog creation |
