In this release of Mantle, our primary focus has been on enhancing the usability of Mantle datasets. We are excited to announce a host of new features designed to improve your experience. Below are detailed descriptions of the key updates and improvements we have made.

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New Features

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SDK Updates

NOTE: We have officially changed the Mantle SDK to use the word dataset instead of entity; however, using entity is still supported for backward compatibility.
The Mantle SDK has two major changes around the functionality of datasets:
  • Querying in the SDK for data_type, name, unique_id, AND properties!
  • Convert a query result to a dataframe in a single step.

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Querying Datasets

The Mantle SDK can now filter datasets by property value: 
fooIsBar = mantle.dataset.build_query()
  .where("props.{foo}.string.eq=Bar").execute()
Or by datatype: 
fooIsBar = mantle.dataset.build_query()
  .where("data_type_unique_id=foo").execute()
This allows for finding any dataset you might need for an analysis (No need to remember Unique IDs! 🥳)

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Converting to a DataFrame

Once you have a list of datasets from your query, you can convert it to a Pandas DataFrame that includes your dataset properties as columns: 
fooIsBarDf = fooIsBar.to_dataframe()
This will automatically convert the list of datasets to a pandas dataframe for easy analysis. For more information, see: SDK Dataset documentation

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All Datasets View

Mantle has introduced a BRAND NEW way to visualize your datasets through the UI, an all datasets view. You can filter datasets by property, data type, and name from one navigation.

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Primitives within Nextflow Parameters

You can now directly access a Mantle parameter of type string, integer, double, bool, or file from directly within your pipeline using Nextflow params. By configuring your input parameters config with: 
"input_integer": {
    "type": "INT"
}
You can access the integer value from within your pipeline: 
params.input_integer
For more information, reach out to the Mantle team.

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Datatype Creation

You can now create new datatypes from the Mantle UI: For instructions on how to see: Create a datatype documentation

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Self-Service Notebook Environments

You can now configure an analysis environment self-service within the Mantle UI. You can configure them to contain pip, conda, or open-source git repos. For more detailed instructions, see: Create an analysis environment documentation

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History View

We have updated the history view on the single dataset view:
  • Now contains back links to analyses, pipelines, and entities
  • [Enterprise Only] Shows any data ingest and egress.