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tiny-count: adding classifier support to sequence-based counting #287

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Merged
taimontgomery merged 5 commits into from
Mar 6, 2023
Merged

tiny-count: adding classifier support to sequence-based counting #287

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7f2087e
Splitting up the gigantic test class in unit_tests_hts_parsing.py so …
AlexTate Mar 4, 2023
7613db1
Adding a test suite for ReferenceSeqs. The diff on this commit is wil…
AlexTate Mar 4, 2023
9bdce0d
Adding support for classifiers in sequence-based counting. The output…
AlexTate Mar 4, 2023
435ca6f
The validation step for GFFs and SAM @SQ headers now reports when val…
AlexTate Mar 4, 2023
6b0913a
Updating documentation to note that classifiers are used during seque…
AlexTate Mar 4, 2023
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2 changes: 1 addition & 1 deletion doc/tiny-count.md
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Expand Up @@ -27,7 +27,7 @@ Selection occurs in three stages, with the output of each stage as input to the
![Feature Selection Diagram](../images/tiny-count_selection.png)

## Sequence-Based Counting Mode
If GFF files aren't specified in your Paths File, Stage 1 selection is skipped and reads are counted by sequence rather than by feature. Reference sequence names and lengths are determined from the `@SQ` headers of input SAM files. In Stages 2 and 3, these reference sequences behave like features that had matched every rule in Stage 1.
If GFF files aren't specified in your Paths File, Stage 1 selection is skipped and reads are counted by sequence rather than by feature. Reference sequence names and lengths are determined from the `@SQ` headers of input SAM files. In Stages 2 and 3, these reference sequences behave like features that had matched every rule in Stage 1. Selection takes place for both the sense and antisense copy of these sequences, and read counts are subset per-sequence if any `Classify as...` values are provided.

## Stage 1: Feature Attribute Parameters
| _Features Sheet Selectors:_ | Select for... | with value... | Classify as... | Source Filter | Type Filter |
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