Workflows developed with the SDK feature:

• Instant no-code interfaces for accessibility and publication
• First class static typing
• Containerization + versioning of every registered change
• Reliable + scalable managed cloud infrastructure
• Single line definition of arbitrary resource requirements (eg. CPU, GPU) for serverless execution

The Latch SDK is a framework to build workflows. A collection of existing and maintained workflows for common biological assays can be found at Latch Verified.

Quickstart

Getting your hands dirty with SDK is the best way to understand how it works. Run the following three commands in your terminal to register your first workflow to LatchBio.

Prerequisite: ensure that docker is present and running on your machine. (Install docker here if you don't already have it installed.)

First, install latch through pip .

$ python3 -m pip install latch

Then, create some boilerplate code for your new workflow.

$ latch init testworkflow

Finally register the boilerplate code to LatchBio.

$ latch register testworkflow

This might take 3-10 minutes depending on your network connection. (Subsequent registers will complete in seconds by reusing the image layers from this initial register.) The registration process will:

• Build a docker image containing your workflow code
• Serialize your code and register it with your LatchBio account
• Push your docker image to a managed container registry

When registration has completed, you should be able to navigate here and see your new workflow in your account.

If you are having issues with registration or have general questions, please file an issue on github.

Installation

The SDK is distributed on pip. Install in a fresh virtual environment for best behavior.

Virtualenvwrapper is recommended.

python3 -m pip install latch

Note that a local installation of docker is required to register workflows.

Examples

Latch Verified features list of well-curated workflows developed by the Latch team.

• Bulk RNA-seq
• Differential Expression
• Pathway Analysis

We'll maintain a growing list of well documented examples developed by our community members here. Please open a pull request to feature your own:

Gene Editing

• Guide Counter
• Batch-GE: Gene editing analysis

Phylogenetics

• Seq-to-tree: Evolutionary history inference
• Codon optimization estimation
• Metamage: Taxonomy classification

Single-cell Analysis

• ArchR: Single-cell chromatin accessibility analysis
• emptydropsR: Ambient RNA detection
• scVelo: Single-cell RNA velocity analysis

Protein Engineering

• UniRep: mLSTM "babbler" deep representation learner for protein engineering
• FAMSA: Multiple sequence protein alignment