latchbio-integration — Платформа для биоинформатических рабочих процессов

--- name: latchbio-integration description: Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration. license: Unknown metadata: skill-author: K-Dense Inc. --- # LatchBio Integration ## Overview Latch is a Python framework for building and deploying bioinformatics workflows as serverless pipelines. Built on Flyte, create workflows with @workflow/@task decorators, manage cloud data with LatchFile/LatchDir, configure resources, and integrate Nextflow/Snakemake pipelines. ## Core Capabilities The Latch platform provides four main areas of functionality: ### 1. Workflow Creation and Deployment - Define serverless workflows using Python decorators - Support for native Python, Nextflow, and Snakemake pipelines - Automatic containerization with Docker - Auto-generated no-code user interfaces - Version control and reproducibility ### 2. Data Management - Cloud storage abstractions (LatchFile, LatchDir) - Structured data organization with Registry (Projects → Tables → Records) - Type-safe data operations with links and enums - Automatic file transfer between local and cloud - Glob pattern matching for file selection ### 3. Resource Configuration - Pre-configured task decorators (@small_task, @large_task, @small_gpu_task, @large_gpu_task) - Custom resource specifications (CPU, memory, GPU, storage) - GPU support (K80, V100, A100) - Timeout and storage configuration - Cost optimization strategies ### 4. Verified Workflows - Production-ready pre-built pipelines - Bulk RNA-seq, DESeq2, pathway analysis - AlphaFold and ColabFold for protein structure prediction - Single-cell tools (ArchR, scVelo, emptyDropsR) - CRISPR analysis, phylogenetics, and more ## Quick Start ### Installation and Setup ```bash # Install Latch SDK python3 -m uv pip install latch # Login to Latch latch login # Initialize a new workflow latch init my-workflow # Register workflow to platform latch register my-workflow ``` **Prerequisites:** - Docker installed and running - Latch account credentials - Python 3.8+ ### Basic Workflow Example ```python from latch import workflow, small_task from latch.types import LatchFile @small_task def process_file(input_file: LatchFile) -> LatchFile: """Process a single file""" # Processing logic return output_file @workflow def my_workflow(input_file: LatchFile) -> LatchFile: """ My bioinformatics workflow Args: input_file: Input data file """ return process_file(input_file=input_file) ``` ## When to Use This Skill This skill should be used when encountering any of the following scenarios: **Workflow Development:** - "Create a Latch workflow for RNA-seq analysis" - "Deploy my pipeline to Latch" - "Convert my Nextflow pipeline to Latch" - "Add GPU support to my workflow" - Working with `@workflow`, `@task` decorators **Data Management:** - "Organize my sequencing data in Latch Registry" - "How do I use LatchFile and LatchDir?" - "Set up sample tracking in Latch" - Working with `latch:///` paths **Resource Configuration:** - "Configure GPU for AlphaFold on Latch" - "My task is running out of memory" - "How do I optimize workflow costs?" - Working with task decorators **Verified Workflows:** - "Run AlphaFold on Latch" - "Use DESeq2 for differential expression" - "Available pre-built workflows" - Using `latch.verified` module ## Detailed Documentation This skill includes comprehensive reference documentation organized by capability: ### references/workflow-creation.md **Read this for:** - Creating and registering workflows - Task definition and decorators - Supporting Python, Nextflow, Snakemake - Launch plans and conditional sections - Workflow execution (CLI and programmatic) - Multi-step and parallel pipelines - Troubleshooting registration issues **Key topics:** - `latch init` and `latch register` commands - `@workflow` and `@task` decorators - LatchFile and LatchDir basics - Type annotations and docstrings - Launch plans with preset parameters - Conditional UI sections ### references/data-management.md **Read this for:** - Cloud storage with LatchFile and LatchDir - Registry system (Projects, Tables, Records) - Linked records and relationships - Enum and typed columns - Bulk operations and transactions - Integration with workflows - Account and workspace management **Key topics:** - `latch:///` path format - File transfer and glob patterns - Creating and querying Registry tables - Column types (string, number, file, link, enum) - Record CRUD operations - Workflow-Registry integration ### references/resource-configuration.md **Read this for:** - Task resource decorators - Custom CPU, memory, GPU configuration - GPU types (K80, V100, A100) - Timeout and storage settings - Resource optimization strategies - Cost-effective workflow design - Monitoring and debugging **Key topics:** - `@small_task`, `@large_task`, `@small_gpu_task`, `@large_gpu_task` - `@custom_task` with precise specifications - Multi-GPU configuration - Resource selection by workload type - Platform limits and quotas ### references/verified-workflows.md **Read this for:** - Pre-built production workflows - Bulk RNA-seq and DESeq2 - AlphaFold and ColabFold - Single-cell analysis (ArchR, scVelo) - CRISPR editing analysis - Pathway enrichment - Integration with custom workflows **Key topics:** - `latch.verified` module imports - Available verified workflows - Workflow parameters and options - Combining verified and custom steps - Version management ## Common Workflow Patterns ### Complete RNA-seq Pipeline ```python from latch import workflow, small_task, large_task from latch.types import LatchFile, LatchDir @small_task def quality_control(fastq: LatchFile) -> LatchFile: """Run FastQC""" return qc_output @large_task def alignment(fastq: LatchFile, genome: str) -> LatchFile: """STAR alignment""" return bam_output @small_task def quantification(bam: LatchFile) -> LatchFile: """featureCounts""" return counts @workflow def rnaseq_pipeline( input_fastq: LatchFile, genome: str, output_dir: LatchDir ) -> LatchFile: """RNA-seq analysis pipeline""" qc = quality_control(fastq=input_fastq) aligned = alignment(fastq=qc, genome=genome) return quantification(bam=aligned) ``` ### GPU-Accelerated Workflow ```python from latch import workflow, small_task, large_gpu_task from latch.types import LatchFile @small_task def preprocess(input_file: LatchFile) -> LatchFile: """Prepare data""" return processed @large_gpu_task def gpu_computation(data: LatchFile) -> LatchFile: """GPU-accelerated analysis""" return results @workflow def gpu_pipeline(input_file: LatchFile) -> LatchFile: """Pipeline with GPU tasks""" preprocessed = preprocess(input_file=input_file) return gpu_computation(data=preprocessed) ``` ### Registry-Integrated Workflow ```python from latch import workflow, small_task from latch.registry.table import Table from latch.registry.record import Record from latch.types import LatchFile @small_task def process_and_track(sample_id: str, table_id: str) -> str: """Process sample and update Registry""" # Get sample from registry table = Table.get(table_id=table_id) records = Record.list(table_id=table_id, filter={"sample_id": sample_id}) sample = records[0] # Process input_file = sample.values["fastq_file"] output = process(input_file) # Update registry sample.update(values={"status": "completed", "result": output}) return "Success" @workflow def registry_workflow(sample_id: str, table_id: str): """Workflow integrated with Registry""" return process_and_track(sample_id=sample_id, table_id=table_id) ``` ## Best Practices ### Workflow Design 1. Use type annotations for all parameters 2. Write clear docstrings (appear in UI) 3. Start with standard task decorators, scale up if needed 4. Break complex workflows into modular tasks 5. Implement proper error handling ### Data Management 6. Use consistent folder structures 7. Define Registry schemas before bulk entry 8. Use linked records for relationships 9. Store metadata in Registry for traceability ### Resource Configuration 10. Right-size resources (don't over-allocate) 11. Use GPU only when algorithms support it 12. Monitor execution metrics and optimize 13. Design for parallel execution when possible ### Development Workflow 14. Test locally with Docker before registration 15. Use version control for workflow code 16. Document resource requirements 17. Profile workflows to determine actual needs ## Troubleshooting ### Common Issues **Registration Failures:** - Ensure Docker is running - Check authentication with `latch login` - Verify all dependencies in Dockerfile - Use `--verbose` flag for detailed logs **Resource Problems:** - Out of memory: Increase memory in task decorator - Timeouts: Increase timeout parameter - Storage issues: Increase ephemeral storage_gib **Data Access:** - Use correct `latch:///` path format - Verify file exists in workspace - Check permissions for shared workspaces **Type Errors:** - Add type annotations to all parameters - Use LatchFile/LatchDir for file/directory parameters - Ensure workflow return type matches actual return ## Additional Resources - **Official Documentation**: https://docs.latch.bio - **GitHub Repository**: https://github.com/latchbio/latch - **Slack Community**: Join Latch SDK workspace - **API Reference**: https://docs.latch.bio/api/latch.html - **Blog**: https://blog.latch.bio ## Support For issues or questions: 1. Check documentation links above 2. Search GitHub issues 3. Ask in Slack community 4. Contact support@latch.bio