SDKs overview

Noctua provides Software Development Kits (SDKs) and client libraries designed to facilitate interaction with the Noctua Bio API. These tools abstract the underlying RESTful API, allowing developers to integrate Noctua's genomic data analysis and bioinformatics capabilities into their applications with reduced boilerplate code. The primary focus for official SDKs is Python, reflecting its prevalence in scientific computing and data science environments.

The SDKs are developed to simplify common tasks such as authenticating with the API, managing biological samples, initiating analysis jobs, and retrieving results. By providing language-specific constructs, developers can focus on their application's logic rather than the intricacies of HTTP requests, response parsing, and error handling. This approach is consistent with industry best practices for API accessibility, as seen in other platforms like Stripe's API documentation which provides SDKs across multiple languages.

Noctua's developer experience emphasizes clear documentation and practical examples. The SDKs are designed to be intuitive for developers familiar with the respective programming languages, offering idiomatic interfaces. This includes support for asynchronous operations where appropriate, enabling efficient handling of long-running bioinformatics tasks without blocking application threads.

Official SDKs by language

Noctua currently offers an official SDK for Python, which is the recommended method for programmatic interaction with the Noctua Bio API in Python environments. This SDK is actively maintained by Noctua and provides a stable interface for accessing core functionalities.

Language Package Name Installation Command Maturity
Python noctua-sdk pip install noctua-sdk Stable (Production Ready)

The Python SDK encapsulates the complexities of API communication, including request signing, error handling, and data serialization/deserialization. It provides high-level objects and methods that map directly to the Noctua API resources, such as Sample, AnalysisJob, and Dataset. This object-oriented approach simplifies data manipulation and workflow orchestration within Python applications. For instance, instead of constructing a raw JSON payload for creating a new sample, developers can instantiate a Sample object, set its attributes, and call a create() method.

Future plans for official SDKs are outlined in the Noctua developer roadmap, which may include support for additional languages based on community demand and strategic priorities. Developers are encouraged to consult the official documentation for the most up-to-date information on supported SDKs and their features.

Installation

Installing the Noctua Python SDK is performed using pip, the standard package installer for Python. Ensure you have Python 3.7 or newer installed on your system to ensure compatibility with the SDK's dependencies.

Python SDK Installation

To install the official Noctua Python SDK, open your terminal or command prompt and execute the following command:

pip install noctua-sdk

It is generally recommended to install Python packages within a Python virtual environment to manage project-specific dependencies and avoid conflicts with global Python installations. To create and activate a virtual environment before installation:

python -m venv noctua-env
source noctua-env/bin/activate  # On Windows, use `noctua-env\Scripts\activate`
pip install noctua-sdk

After successful installation, you can verify the installation by attempting to import the SDK in a Python interpreter:

import noctua
print(noctua.__version__)

This should output the installed version of the Noctua SDK, confirming that the package is correctly installed and accessible within your Python environment.

Quickstart example

This quickstart example demonstrates how to authenticate with the Noctua Bio API using the Python SDK and perform a basic operation, such as listing available datasets. Before running this code, ensure you have obtained your API key from the Noctua developer console.

Python Example: Listing Datasets

First, set your Noctua API key as an environment variable or replace YOUR_API_KEY with your actual key. Using environment variables is recommended for security.

import os
from noctua import NoctuaClient

# Initialize the Noctua client with your API key
# It's recommended to store your API key in an environment variable
api_key = os.getenv("NOCTUA_API_KEY", "YOUR_API_KEY")
client = NoctuaClient(api_key=api_key)

try:
    # List available datasets
    print("Fetching available datasets...")
    datasets_response = client.datasets.list()
    
    if datasets_response.success:
        datasets = datasets_response.data
        if datasets:
            print(f"Found {len(datasets)} datasets:")
            for dataset in datasets:
                print(f"  - ID: {dataset.id}, Name: {dataset.name}, Type: {dataset.type}")
        else:
            print("No datasets found in your account.")
    else:
        print(f"Error fetching datasets: {datasets_response.error_message}")

except Exception as e:
    print(f"An unexpected error occurred: {e}")

This example initializes the NoctuaClient with an API key. It then calls the datasets.list() method to retrieve a paginated list of datasets associated with your account. The response object includes a success flag and either data (a list of Dataset objects) or an error_message. This pattern of handling responses is consistent across various SDK methods, providing a clear way to manage API interactions and potential issues.

For more advanced operations, such as uploading genomic files, initiating complex analysis pipelines, or retrieving specific analytical results, refer to the Noctua Python SDK guide, which provides comprehensive examples and best practices.

Community libraries

As of 2026, the Noctua ecosystem is relatively new, having been founded in 2022. Consequently, the development of community-contributed libraries and wrappers is still emerging. The official Python SDK serves as the primary and most robust interface for developers.

Community libraries typically arise from developer needs to integrate an API into languages not officially supported, or to provide specialized functionality built on top of the official SDKs. These might include:

  • Wrappers for other languages: While Noctua's official SDK is Python, community members might develop clients for languages like R, Java, or Go to suit their specific research or development environments.
  • Specialized tools: Libraries focused on specific bioinformatics tasks, such as visualizing Noctua's output data, integrating with other bioinformatics tools (e.g., Biopython, scikit-learn), or providing command-line interfaces (CLIs) built upon the SDK.
  • Jupyter Notebook extensions: Tools that enhance the interactive experience of using Noctua within Jupyter environments, common in data science and bioinformatics research.

Developers interested in contributing to the Noctua ecosystem or finding community-developed tools should monitor the Noctua community forums and GitHub for relevant repositories. The open-source nature of many bioinformatics projects often leads to collaborative development of such tools. When using community-developed libraries, it is crucial to review their documentation, licensing, and maintenance status, as their stability and security may vary compared to official SDKs.

Noctua encourages community contributions and provides resources like contributing guidelines to help developers build on the platform. This collaborative approach is a common model in scientific computing, fostering innovation and broader adoption, as exemplified by the extensive community around projects like Biopython for biological computation.