130 Latch E11f4cd6

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

from latch.verified import (
    bulk_rnaseq,
    deseq2,
    mafft,
    trim_galore,
    alphafold,
    colabfold
)

Core Verified Workflows

Bulk RNA-seq Analysis

Alignment and Quantification:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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:

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

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

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

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

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

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

# 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

# 1. Predict structure
structure = alphafold(sequence=protein_seq)

# 2. Custom analysis
results = analyze_structure(pdb=structure)

Single-Cell Workflow

# 1. Filter cells
filtered = emptydrops(matrix=raw_counts)

# 2. RNA velocity
velocity = scvelo(adata=filtered)