RNApysoforms 0.9.0

A Python package designed for visualizing RNA isoform structures and expression levels by leveraging Plotly for interactive plotting and Polars for efficient data manipulation, enabling the creation of fast-rendering, interactive plots.

RNApysoforms

RNApysoforms is a Python package designed for visualizing RNA isoform structures and expression levels. Leveraging Plotly for interactive plotting and Polars for efficient data manipulation, it enables the creation of fast-rendering, interactive plots suitable for both local and web applications. Inspired by the R package ggtranscript, RNApyisoforms brings similar RNA visualization capabilities to the Python ecosystem, facilitating effective exploration and presentation of RNA sequencing data.

Installation

You can install RNApysoforms using pip:

pip install RNApysoforms

Quick Start

Basic usage (quick start)

More vignettes (usage examples)

Recaling introns for a prettier RNA isoform structure plot

Plotting RNA isoform structure and expression

Plotting RNA isoform structure and normalized expression

Test data and documentation

Download small test dataset

Function documentation and vignettes

Issues

Please go through the documentation and vignettes before submitting an issue.

Contributing

Contributions to RNApysoforms are welcome! Please feel free to submit a Pull Request.

The function implementations are under the src/RNApysoforms directory.

Functions

• calculate_exon_number(): Assigns exon numbers to exons, CDS, and introns within a genomic annotation dataset based on transcript structure and strand direction.

• gene_filtering(): Filters genomic annotations and optionally an expression matrix for a specific gene, with options to order and select top expressed transcripts.

• make_plot(): Creates a multi-panel Plotly figure combining transcript structure plots and expression data plots.

• make_traces(): Generates Plotly traces for visualizing transcript structures and expression data.

• read_expression_matrix(): Loads and processes an expression matrix, optionally merging with metadata, performing CPM normalization, and calculating relative transcript abundance.

• read_ensembl_gtf(): Reads a GTF (Gene Transfer Format) file and returns the data as a Polars DataFrame.

• shorten_gaps(): Shortens intron and transcript start gaps between exons in genomic annotations to enhance visualization.

• to_intron(): Converts exon coordinates into corresponding intron coordinates within a genomic annotation dataset.

License

This project is licensed under the MIT License.