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Formally, a workflow can be described as a directed acyclic graph (DAG), where each node in the graph is called a task. This computational graph is a flexible model to describe most any bioinformatics analysis. In this example, a workflow ingests sequencing files in FastQ format and produces a sorted assembly file. The workflow’s DAG has two tasks. The first task turns the FastQ files into a single BAM file using an assembly algorithm. The second task sorts the assembly from the first task. The final output is a useful assembly conducive to downstream analysis and visualization in tools like IGV. The Latch SDK lets you define your workflow tasks as python functions. The parameters in the function signature define the task inputs and return values define the task outputs. The body of the function holds the task logic, which can be written in plain python or can be subprocessed through a program/library in any language. 
@small_task
def assembly_task(read1: LatchFile, read2: LatchFile) -> LatchFile:

    # A reference to our output.
    sam_file = Path("covid_assembly.sam").resolve()

    _bowtie2_cmd = [
        "bowtie2/bowtie2",
        "--local",
        "-x",
        "wuhan",
        "-1",
        read1.local_path,
        "-2",
        read2.local_path,
        "--very-sensitive-local",
        "-S",
        str(sam_file),
    ]

    subprocess.run(_bowtie2_cmd)

    return LatchFile(str(sam_file), "latch:///covid_assembly.sam")
These tasks are then “glued” together in another function that represents the workflow. The workflow function body simply chains the task functions by calling them and passing returned values to downstream task functions. Notice that our workflow function calls the task that we just defined, assembly_task, as well as another task we can assume was defined elsewhere, sort_bam_task. You must not write actual logic in the workflow function body. It can only be used to call task functions and pass task function return values to downstream task functions. Additionally all task functions must be called with keyword arguments. You also cannot access variables directly in the workflow function; in the example below, you would not be able to pass in read1=read1.local_path. 
@workflow
def assemble_and_sort(read1: LatchFile, read2: LatchFile) -> LatchFile:

    sam = assembly_task(read1=read1, read2=read2)
    return sort_bam_task(sam=sam)
Workflow function docstrings also contain markdown formatted documentation and a DSL to specify the presentation of parameters when the workflow interface is generated. We’ll add this content to the docstring of the workflow function we just wrote. 
@workflow
def assemble_and_sort(read1: LatchFile, read2: LatchFile) -> LatchFile:
    """Description...

    markdown header
    ----

    Write some documentation about your workflow in
    markdown here:

    > Regular markdown constructs work as expected.

    # Heading

    * content1
    * content2

    __metadata__:
        display_name: Assemble and Sort FastQ Files
        author:
            name:
            email:
            github:
        repository:
        license:
            id: MIT

    Args:

        read1:
          Paired-end read 1 file to be assembled.

          __metadata__:
            display_name: Read1

        read2:
          Paired-end read 2 file to be assembled.

          __metadata__:
            display_name: Read2
    """

    sam = assembly_task(read1=read1, read2=read2)
    return sort_bam_task(sam=sam)

​
Workflow Code Structure

So far we have defined workflows and tasks as python functions but we don’t know where to put them or what supplementary files might be needed to run the code on the Latch platform. Workflow code needs to live in directory with three necessary elements:
  • a file named Dockerfile that defines the computing environment of your tasks
  • a file named version that holds the plaintext version of the workflow
  • a directory named wf that holds the python code needed for the workflow.
  • task and workflow functions must live in a wf/__init__.py file
These three elements must be named as specified above. The directory should have the following structure: 
├── Dockerfile
├── version
└── wf
    └── __init__.py
The SDK ships with easily retrievable example workflow code. Just type latch init myworkflow to construct a directory structured as above for reference or boilerplate.

​
Example Dockerfile

Note: you are required to use our base image for the time being. 
FROM 812206152185.dkr.ecr.us-west-2.amazonaws.com/latch-base:9a7d-main

# Its easy to build binaries from source that you can later reference as
# subprocesses within your workflow.
RUN curl -L https://sourceforge.net/projects/bowtie-bio/files/bowtie2/2.4.4/bowtie2-2.4.4-linux-x86_64.zip/download -o bowtie2-2.4.4.zip &&\
    unzip bowtie2-2.4.4.zip &&\
    mv bowtie2-2.4.4-linux-x86_64 bowtie2

# Or use managed library distributions through the container OS's package
# manager.
RUN apt-get update -y &&\
    apt-get install -y autoconf samtools


# You can use local data to construct your workflow image.  Here we copy a
# pre-indexed reference to a path that our workflow can reference.
COPY reference /root/reference
ENV BOWTIE2_INDEXES="reference"

COPY wf /root/wf

# STOP HERE:
# The following lines are needed to ensure your build environement works
# correctly with latch.
ARG tag
ENV FLYTE_INTERNAL_IMAGE $tag
RUN  sed -i 's/latch/wf/g' flytekit.config
RUN python3 -m pip install --upgrade latch
WORKDIR /root

​
Example version File

You can use any versioning scheme that you would like, as long as each register has a unique version value. We recommend sticking with semantic versioning. 
v0.0.0

​
Example wf/__init__.py File

import subprocess
from pathlib import Path

from latch import small_task, workflow
from latch.types import LatchFile


@small_task
def assembly_task(read1: LatchFile, read2: LatchFile) -> LatchFile:

    # A reference to our output.
    sam_file = Path("covid_assembly.sam").resolve()

    _bowtie2_cmd = [
        "bowtie2/bowtie2",
        "--local",
        "-x",
        "wuhan",
        "-1",
        read1.local_path,
        "-2",
        read2.local_path,
        "--very-sensitive-local",
        "-S",
        str(sam_file),
    ]

    subprocess.run(_bowtie2_cmd)

    return LatchFile(str(sam_file), "latch:///covid_assembly.sam")


@small_task
def sort_bam_task(sam: LatchFile) -> LatchFile:

    bam_file = Path("covid_sorted.bam").resolve()

    _samtools_sort_cmd = [
        "samtools",
        "sort",
        "-o",
        str(bam_file),
        "-O",
        "bam",
        sam.local_path,
    ]

    subprocess.run(_samtools_sort_cmd)

    return LatchFile(str(bam_file), "latch:///covid_sorted.bam")


@workflow
def assemble_and_sort(read1: LatchFile, read2: LatchFile) -> LatchFile:
    """Description...

    markdown header
    ----

    Write some documentation about your workflow in
    markdown here:

    > Regular markdown constructs work as expected.

    # Heading

    * content1
    * content2

    __metadata__:
        display_name: Assemble and Sort FastQ Files
        author:
            name:
            email:
            github:
        repository:
        license:
            id: MIT

    Args:

        read1:
          Paired-end read 1 file to be assembled.

          __metadata__:
            display_name: Read1

        read2:
          Paired-end read 2 file to be assembled.

          __metadata__:
            display_name: Read2
    """
    sam = assembly_task(read1=read1, read2=read2)
    return sort_bam_task(sam=sam)

​
What happens at registration?

Now that we’ve defined our functions, we are ready to register our workflow with the LatchBio platform. This will give us:
  • a no-code interface
  • managed cloud infrastructure for workflow execution
  • a dedicated API endpoint for programmatic execution
  • hosted documentation
  • parallelized CSV-to-batch execution
To register, we type latch register <directory_name> into our terminal (where directory_name is the name of the directory holding our code, Dockerfile and version file). The registration process requires a local installation of Docker. To re-register changes, make sure you update the value in the version file. (The value of the version is not important, only that it is distinct from previously registered versions).
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