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Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
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SOC 職業分類に基づく
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.
| 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."} |
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.
The Latch platform provides four main areas of functionality:
# Install Latch SDK
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:
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)
This skill should be used when encountering any of the following scenarios:
Workflow Development:
@workflow, @task decoratorsData Management:
latch:/// pathsResource Configuration:
Verified Workflows:
latch.verified moduleThis skill includes comprehensive reference documentation organized by capability:
Read this for:
Key topics:
latch init and latch register commands@workflow and @task decoratorsRead this for:
Key topics:
latch:/// path formatRead this for:
Key topics:
@small_task, @large_task, @small_gpu_task, @large_gpu_task@custom_task with precise specificationsRead this for:
Key topics:
latch.verified module importsfrom 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)
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)
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)
Registration Failures:
latch login--verbose flag for detailed logsResource Problems:
Data Access:
latch:/// path formatType Errors:
For issues or questions: