Latchbio Integration

Latchbio Integration is a community skill for managing bioinformatics workflows on the Latch platform, covering workflow registration, data management, parameter configuration, execution monitoring, and result retrieval for computational biology pipelines.

What Is This?

Overview

Latchbio Integration provides tools for running and managing bioinformatics workflows through the Latch Bio cloud platform. It covers workflow registration that packages Python-based analysis pipelines as versioned workflows with typed parameter interfaces, data management that organizes input datasets and output results in the Latch Data filesystem with folder structures and access controls, parameter configuration that defines workflow inputs with type validation and default values for reproducible execution, execution monitoring that tracks workflow runs with status updates, log streaming, and failure alerts, and result retrieval that downloads output files and extracts summary metrics from completed runs. The skill enables bioinformatics teams to deploy and manage analysis pipelines on scalable cloud infrastructure.

Who Should Use This

This skill serves bioinformatics engineers deploying analysis workflows, research teams running genomics pipelines on cloud infrastructure, and computational biology groups standardizing analysis environments across projects.

Why Use It?

Problems It Solves

Local bioinformatics pipelines require manual environment configuration that varies across machines and researchers. Workflow parameters passed through command-line arguments lack type validation and documentation. Analysis results stored on local filesystems are not accessible to collaborators without manual file sharing. Long-running genomics workflows require monitoring that local execution does not provide reliably.

Core Highlights

Workflow packager registers Python analysis code as versioned cloud workflows with typed interfaces. Data manager organizes files in the Latch filesystem with structured folders and permissions. Run executor launches workflows with validated parameters on cloud compute resources. Status tracker monitors execution progress with log access and completion notifications.

How to Use It?

Basic Usage

from latch.resources\
  .workflow import workflow
from latch.resources\
  .tasks import (
    small_task)
from latch.types\
  import LatchFile

@small_task
def process_fastq(
  input_file: LatchFile,
  quality_threshold:
    int = 30
) -> LatchFile:
  local = input_file\
    .local_path
  output_path = (
    '/root/filtered.fq')
  import subprocess
  subprocess.run([
    'fastp',
    '-i', local,
    '-o', output_path,
    '-q', str(
      quality_threshold)
  ], check=True)
  return LatchFile(
    output_path,
    'latch:///results'
    '/filtered.fq')

@workflow
def fastq_pipeline(
  reads: LatchFile,
  min_quality:
    int = 30
) -> LatchFile:
  return process_fastq(
    input_file=reads,
    quality_threshold=(
      min_quality))

Real-World Examples

from latch import executions
from pathlib import Path

class BatchRunner:
  def __init__(
    self,
    workflow_name: str,
    version: str
  ):
    self.wf_name = (
      workflow_name)
    self.version = version
    self.runs = []

  def submit(
    self,
    params: dict
  ) -> str:
    exe = executions\
      .launch(
        self.wf_name,
        self.version,
        params)
    self.runs.append(
      exe.id)
    return exe.id

  def submit_batch(
    self,
    param_list:
      list[dict]
  ) -> list[str]:
    ids = []
    for params\
        in param_list:
      run_id = (
        self.submit(params))
      ids.append(run_id)
    return ids

  def check_status(
    self
  ) -> dict:
    results = {}
    for rid in self.runs:
      exe = executions\
        .get(rid)
      results[rid] = (
        exe.status)
    return results

Advanced Tips

Define workflow parameters with Latch metadata annotations that generate documentation and input forms automatically for non-technical users. Use multi-task workflows to parallelize independent analysis steps across separate compute instances for faster completion. Version workflows with semantic tags so collaborators can reference specific pipeline releases in their analyses.

When to Use It?

Use Cases

Deploy a FASTQ processing pipeline as a cloud workflow with typed file inputs and quality parameters. Run batch analysis across multiple samples by submitting parameterized workflow executions. Share analysis workflows with collaborators who can execute them through the Latch web interface.

Related Topics

Bioinformatics workflows, Latch Bio, cloud pipelines, genomics analysis, workflow orchestration, and computational biology infrastructure.

Important Notes

Requirements

Latch Bio account with workspace access. Latch SDK installed for workflow development. Docker environment for local workflow testing before cloud registration.

Usage Recommendations

Do: define typed parameters for all workflow inputs to catch configuration errors before execution starts. Use Latch Data paths for file outputs so results persist in the platform filesystem. Test workflows locally with the Latch SDK before registering cloud versions.

Don't: hard-code file paths that reference local machine locations instead of Latch Data paths. Skip workflow versioning since unversioned updates can break dependent analyses. Store credentials or secrets in workflow parameters rather than using platform secret management.

Limitations

Workflow execution is limited to compute resources available in the Latch cloud environment. Container image size constraints may require optimizing dependency installations for large bioinformatics toolsets. Platform availability and pricing depend on the Latch subscription tier and usage quotas.