> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mantlebio.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Intro to Mantle

This guide introduces Mantle’s capabilities, the end-to-end flow for biological data analysis on Mantle, and how to start analyzing your data.

## What you can do with Mantle

| Capability                                                   | Enables you to...                                                                                                                                            |
| ------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| [Notebooks](features/notebooks/introduction)                 | <ul><li>Interact with your [Mantle Database](features/database)</li><li>Analyze data with custom Python code</li><li>Share results with colleagues</li></ul> |
| [Database](features/database)                                | <ul><li>Organize data files with experimental metadata in one place</li></ul>                                                                                |
| [Pipelines](features/pipelines)                              | <ul><li>Run powerful Nextflow pipelines using a convenient no-code interface</li></ul>                                                                       |

## How to use Mantle for biological data analysis

Open a [Mantle Notebook](/features/notebooks/introduction) and...

<Steps>
  <Step title="Upload data to Mantle">
    * Select one of the built-in data types, e.g. `rnaseq_fastq`. Or, if nothing fits, use the `custom_file` or `custom_directory` types.
  </Step>

  <Step title="Process instrument data files">
    * Often, biological data is in bespoke file types and/or in an unstructured format, and needs to be processed before it undergo statistical analysis.
    * Process files using Python or a [pipeline](features/pipelines).
  </Step>

  <Step title="Explore and discover">
    * Data that is tabular is ready for statistical analysis, machine learning, and plotting.
    * Use custom Python code with packages like Scanpy, Scipy, PyTorch, Scikit-Learn, etc. in a Mantle Notebook.
    * Create graphs for the team meeting or publication.
  </Step>
</Steps>

Get started in minutes with one of our [templates](/workflows/home).