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šŸ”  Semantic Types

Santiago Castro Dau edited this page Jun 18, 2024 · 38 revisions

A Short Introduction to Semantic Types

Inputs and outputs in Qiime2 are compressed files with specific structures and contents. Only these special compressed files (called artifacts in Qiime 2 terminology) can be used or produced by Qiime2 actions, which are programs that fetch, transform, or analyze the data contained in the artifacts. The structure and contents of a given artifact are constrained by its associated semantic type. Qiime2 uses semantic types to check that only valid inputs are used and/or that valid outputs are produced for a given action.

See Qiime2 Documentation on semantic types and actions for more information.

Below we list some semantic types that are especially relevant to q2-moshpit actions (semantic types are actually implemented in a different repository; q2-types). By providing this information we hope to help users navigate their way through the functionality that q2-moshpit offers. The links below will take you to semantic-type information pages, each of which contains the following sections:

  • Short description of the contents of artifacts with the given semantic type.
  • Artifact Format: The Python class that defines the given semantic type. Inside this class, the expected folder structure as well as the names of the files that the artifact should have are defined.
  • Expected Folder Structure: Tree diagram of a folder and files that could comply with the given semantic type.
  • Where to find <semantic-type-name>: list actions where the given semantic type is used, either as input, output, or both.

Lastly, below you will find semantic types grouped by their corresponding composite type, which is just another semantic type that is used to group related semantic types.

Semantic Type Documentation

SampleData

Data organized by sample.

FeatureData

Data organized by feature (e.g. MAGs, sequences, taxonomy).

GenomeData

FeatureTable

ReferenceDB

Reference databases.

ProfileHMM

Profile hidden Markov Models for protein sequences.

Varia

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