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skills/latchbio-integration/references/verified-workflows.md

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+# Verified Workflows
+
+## Overview
+Latch Verified Workflows are production-ready, pre-built bioinformatics pipelines developed and maintained by Latch engineers. These workflows are used by top pharmaceutical companies and biotech firms for research and discovery.
+
+## Available in Python SDK
+
+The `latch.verified` module provides programmatic access to verified workflows from Python code.
+
+### Importing Verified Workflows
+
+```python
+from latch.verified import (
+    bulk_rnaseq,
+    deseq2,
+    mafft,
+    trim_galore,
+    alphafold,
+    colabfold
+)
+```
+
+## Core Verified Workflows
+
+### Bulk RNA-seq Analysis
+
+**Alignment and Quantification:**
+```python
+from latch.verified import bulk_rnaseq
+from latch.types import LatchFile
+
+# Run bulk RNA-seq pipeline
+results = bulk_rnaseq(
+    fastq_r1=LatchFile("latch:///data/sample_R1.fastq.gz"),
+    fastq_r2=LatchFile("latch:///data/sample_R2.fastq.gz"),
+    reference_genome="hg38",
+    output_dir="latch:///results/rnaseq"
+)
+```
+
+**Features:**
+- Read quality control with FastQC
+- Adapter trimming
+- Alignment with STAR or HISAT2
+- Gene-level quantification with featureCounts
+- MultiQC report generation
+
+### Differential Expression Analysis
+
+**DESeq2:**
+```python
+from latch.verified import deseq2
+from latch.types import LatchFile
+
+# Run differential expression analysis
+results = deseq2(
+    count_matrix=LatchFile("latch:///data/counts.csv"),
+    sample_metadata=LatchFile("latch:///data/metadata.csv"),
+    design_formula="~ condition",
+    output_dir="latch:///results/deseq2"
+)
+```
+
+**Features:**
+- Normalization and variance stabilization
+- Differential expression testing
+- MA plots and volcano plots
+- PCA visualization
+- Annotated results tables
+
+### Pathway Analysis
+
+**Enrichment Analysis:**
+```python
+from latch.verified import pathway_enrichment
+
+results = pathway_enrichment(
+    gene_list=LatchFile("latch:///data/deg_list.txt"),
+    organism="human",
+    databases=["GO_Biological_Process", "KEGG", "Reactome"],
+    output_dir="latch:///results/pathways"
+)
+```
+
+**Supported Databases:**
+- Gene Ontology (GO)
+- KEGG pathways
+- Reactome
+- WikiPathways
+- MSigDB collections
+
+### Sequence Alignment
+
+**MAFFT Multiple Sequence Alignment:**
+```python
+from latch.verified import mafft
+from latch.types import LatchFile
+
+aligned = mafft(
+    input_fasta=LatchFile("latch:///data/sequences.fasta"),
+    algorithm="auto",
+    output_format="fasta"
+)
+```
+
+**Features:**
+- Multiple alignment algorithms (FFT-NS-1, FFT-NS-2, G-INS-i, L-INS-i)
+- Automatic algorithm selection
+- Support for large alignments
+- Various output formats
+
+### Adapter and Quality Trimming
+
+**Trim Galore:**
+```python
+from latch.verified import trim_galore
+
+trimmed = trim_galore(
+    fastq_r1=LatchFile("latch:///data/sample_R1.fastq.gz"),
+    fastq_r2=LatchFile("latch:///data/sample_R2.fastq.gz"),
+    quality_threshold=20,
+    adapter_auto_detect=True
+)
+```
+
+**Features:**
+- Automatic adapter detection
+- Quality trimming
+- FastQC integration
+- Support for single-end and paired-end
+
+## Protein Structure Prediction
+
+### AlphaFold
+
+**Standard AlphaFold:**
+```python
+from latch.verified import alphafold
+from latch.types import LatchFile
+
+structure = alphafold(
+    sequence_fasta=LatchFile("latch:///data/protein.fasta"),
+    model_preset="monomer",
+    use_templates=True,
+    output_dir="latch:///results/alphafold"
+)
+```
+
+**Features:**
+- Monomer and multimer prediction
+- Template-based modeling option
+- MSA generation
+- Confidence metrics (pLDDT, PAE)
+- PDB structure output
+
+**Model Presets:**
+- `monomer`: Single protein chain
+- `monomer_casp14`: CASP14 competition version
+- `monomer_ptm`: With pTM confidence
+- `multimer`: Protein complexes
+
+### ColabFold
+
+**Optimized AlphaFold Alternative:**
+```python
+from latch.verified import colabfold
+
+structure = colabfold(
+    sequence_fasta=LatchFile("latch:///data/protein.fasta"),
+    num_models=5,
+    use_amber_relax=True,
+    output_dir="latch:///results/colabfold"
+)
+```
+
+**Features:**
+- Faster than standard AlphaFold
+- MMseqs2-based MSA generation
+- Multiple model predictions
+- Amber relaxation
+- Ranking by confidence
+
+**Advantages:**
+- 3-5x faster MSA generation
+- Lower compute cost
+- Similar accuracy to AlphaFold
+
+## Single-Cell Analysis
+
+### ArchR (scATAC-seq)
+
+**Chromatin Accessibility Analysis:**
+```python
+from latch.verified import archr
+
+results = archr(
+    fragments_file=LatchFile("latch:///data/fragments.tsv.gz"),
+    genome="hg38",
+    output_dir="latch:///results/archr"
+)
+```
+
+**Features:**
+- Arrow file generation
+- Quality control metrics
+- Dimensionality reduction
+- Clustering
+- Peak calling
+- Motif enrichment
+
+### scVelo (RNA Velocity)
+
+**RNA Velocity Analysis:**
+```python
+from latch.verified import scvelo
+
+results = scvelo(
+    adata_file=LatchFile("latch:///data/adata.h5ad"),
+    mode="dynamical",
+    output_dir="latch:///results/scvelo"
+)
+```
+
+**Features:**
+- Spliced/unspliced quantification
+- Velocity estimation
+- Dynamical modeling
+- Trajectory inference
+- Visualization
+
+### emptyDropsR (Cell Calling)
+
+**Empty Droplet Detection:**
+```python
+from latch.verified import emptydrops
+
+filtered_matrix = emptydrops(
+    raw_matrix_dir=LatchDir("latch:///data/raw_feature_bc_matrix"),
+    fdr_threshold=0.01
+)
+```
+
+**Features:**
+- Distinguish cells from empty droplets
+- FDR-based thresholding
+- Ambient RNA removal
+- Compatible with 10X data
+
+## Gene Editing Analysis
+
+### CRISPResso2
+
+**CRISPR Editing Assessment:**
+```python
+from latch.verified import crispresso2
+
+results = crispresso2(
+    fastq_r1=LatchFile("latch:///data/sample_R1.fastq.gz"),
+    amplicon_sequence="AGCTAGCTAG...",
+    guide_rna="GCTAGCTAGC",
+    output_dir="latch:///results/crispresso"
+)
+```
+
+**Features:**
+- Indel quantification
+- Base editing analysis
+- Prime editing analysis
+- HDR quantification
+- Allele frequency plots
+
+## Phylogenetics
+
+### Phylogenetic Tree Construction
+
+```python
+from latch.verified import phylogenetics
+
+tree = phylogenetics(
+    alignment_file=LatchFile("latch:///data/aligned.fasta"),
+    method="maximum_likelihood",
+    bootstrap_replicates=1000,
+    output_dir="latch:///results/phylo"
+)
+```
+
+**Features:**
+- Multiple tree-building methods
+- Bootstrap support
+- Tree visualization
+- Model selection
+
+## Workflow Integration
+
+### Using Verified Workflows in Custom Pipelines
+
+```python
+from latch import workflow, small_task
+from latch.verified import bulk_rnaseq, deseq2
+from latch.types import LatchFile, LatchDir
+
+@workflow
+def complete_rnaseq_analysis(
+    fastq_files: List[LatchFile],
+    metadata: LatchFile,
+    output_dir: LatchDir
+) -> LatchFile:
+    """
+    Complete RNA-seq analysis pipeline using verified workflows
+    """
+    # Run alignment for each sample
+    aligned_samples = []
+    for fastq in fastq_files:
+        result = bulk_rnaseq(
+            fastq_r1=fastq,
+            reference_genome="hg38",
+            output_dir=output_dir
+        )
+        aligned_samples.append(result)
+
+    # Aggregate counts and run differential expression
+    count_matrix = aggregate_counts(aligned_samples)
+    deseq_results = deseq2(
+        count_matrix=count_matrix,
+        sample_metadata=metadata,
+        design_formula="~ condition"
+    )
+
+    return deseq_results
+```
+
+## Best Practices
+
+### When to Use Verified Workflows
+
+**Use Verified Workflows for:**
+1. Standard analysis pipelines
+2. Well-established methods
+3. Production-ready analyses
+4. Reproducible research
+5. Validated bioinformatics tools
+
+**Build Custom Workflows for:**
+1. Novel analysis methods
+2. Custom preprocessing steps
+3. Integration with proprietary tools
+4. Experimental pipelines
+5. Highly specialized workflows
+
+### Combining Verified and Custom
+
+```python
+from latch import workflow, small_task
+from latch.verified import alphafold
+from latch.types import LatchFile
+
+@small_task
+def preprocess_sequence(raw_fasta: LatchFile) -> LatchFile:
+    """Custom preprocessing"""
+    # Custom logic here
+    return processed_fasta
+
+@small_task
+def postprocess_structure(pdb_file: LatchFile) -> LatchFile:
+    """Custom post-analysis"""
+    # Custom analysis here
+    return analysis_results
+
+@workflow
+def custom_structure_pipeline(input_fasta: LatchFile) -> LatchFile:
+    """
+    Combine custom steps with verified AlphaFold
+    """
+    # Custom preprocessing
+    processed = preprocess_sequence(raw_fasta=input_fasta)
+
+    # Use verified AlphaFold
+    structure = alphafold(
+        sequence_fasta=processed,
+        model_preset="monomer_ptm"
+    )
+
+    # Custom post-processing
+    results = postprocess_structure(pdb_file=structure)
+
+    return results
+```
+
+## Accessing Workflow Documentation
+
+### In-Platform Documentation
+
+Each verified workflow includes:
+- Parameter descriptions
+- Input/output specifications
+- Method details
+- Citation information
+- Example usage
+
+### Viewing Available Workflows
+
+```python
+from latch.verified import list_workflows
+
+# List all available verified workflows
+workflows = list_workflows()
+
+for workflow in workflows:
+    print(f"{workflow.name}: {workflow.description}")
+```
+
+## Version Management
+
+### Workflow Versions
+
+Verified workflows are versioned and maintained:
+- Bug fixes and improvements
+- New features added
+- Backward compatibility maintained
+- Version pinning available
+
+### Using Specific Versions
+
+```python
+from latch.verified import bulk_rnaseq
+
+# Use specific version
+results = bulk_rnaseq(
+    fastq_r1=input_file,
+    reference_genome="hg38",
+    workflow_version="2.1.0"
+)
+```
+
+## Support and Updates
+
+### Getting Help
+
+- **Documentation**: https://docs.latch.bio
+- **Slack Community**: Latch SDK workspace
+- **Support**: support@latch.bio
+- **GitHub Issues**: Report bugs and request features
+
+### Workflow Updates
+
+Verified workflows receive regular updates:
+- Tool version upgrades
+- Performance improvements
+- Bug fixes
+- New features
+
+Subscribe to release notes for update notifications.
+
+## Common Use Cases
+
+### Complete RNA-seq Study
+
+```python
+# 1. Quality control and alignment
+aligned = bulk_rnaseq(fastq=samples)
+
+# 2. Differential expression
+deg = deseq2(counts=aligned)
+
+# 3. Pathway enrichment
+pathways = pathway_enrichment(genes=deg)
+```
+
+### Protein Structure Analysis
+
+```python
+# 1. Predict structure
+structure = alphafold(sequence=protein_seq)
+
+# 2. Custom analysis
+results = analyze_structure(pdb=structure)
+```
+
+### Single-Cell Workflow
+
+```python
+# 1. Filter cells
+filtered = emptydrops(matrix=raw_counts)
+
+# 2. RNA velocity
+velocity = scvelo(adata=filtered)
+```