Genotations

Python library to work with genomes and annotations, mostly Ensembl genomes. Also supports visualization of transcripts/gene features and primer selection. As pandas and polars are libraries of everyday use for many python developers this library focus on annotations representation in a dataframe way.

The library allows:

• downloading Ensembl annotations and genomes (uses genomepy under the hood)
• working with genomic annotations like with polars dataframes
• getting sequences for selected genes
• visualizing the genes features
• designing primers for selected transcripts with Primer3 python wrapper

Usage

Install with pip:

pip install genotations

In some cases you may also need to install ucsc annotation tools, you can add them to your micromamba/conda environment as they are installed from bioconda channel. Here how it may look in your environment file:

name: genotations
channels:
  - conda-forge
  - BjornFJohansson
  - bioconda
  - defaults
dependencies:
  - python=3.10
  - ucsc-bedtogenepred
  - ucsc-genepredtobed
  - ucsc-genepredtogtf
  - ucsc-gff3togenepred
  - ucsc-gtftogenepred
  - pip
  - pip:
      - genotations

Now you can start using it, for example:

from genotations import ensembl
human = ensembl.human # getting human genome
mouse = ensembl.mouse # getting mosue genome
mouse.annotations.exons().annotations_df # getting exons as DataFrame
mouse.annotations.protein_coding().exons().annotations_df # getting exons of protein coding genes
mouse.annotations.transcript_gene_names_df # getting transcript gene names
mouse.annotations.with_gene_name_contains("Foxo1").protein_coding().transcripts() #getting only coding Foxo1 transcripts
mouse.annotations.with_gene_name_contains("Foxo1").genes_visual(mouse.genome)[0].plot() # plotting features of the Foxo1 gene
cow_assemblies = ensembl.search_assemblies("Bos taurus") # you can also search genomes by species name if it exists in Ensembl
cow1 = ensembl.SpeciesInfo("Cow", cow_assemblies[-1][0]) # selecting one of several cow assemblies
cow1.annotations.annotations_df # getting annotations as dataframe

You can also use the library to annotate existing gene expression data with gene and transcript symbols and features. For example

from genotations.quantification import *
from genotations import ensembl
base = "."
examples = base / "examples"
data = examples / "data"
expressions = pl.read_parquet(str(data / "PRJNA543661_transcripts.parquet"))
with_expressions_summaries(expressions, min_avg_value = 1)
expressions_ext = ensembl.mouse.annotations.extend_with_annotations_and_sequences(expressions, ensembl.mouse.genome) # extend expression data with annotations and sequences

For more examples, check example notebook to see the usage and API

Working with the library code

Use micromamba (or conda) and environment.yaml to install the dependencies

micromamba create -f environment.yaml
micromamba activate genotations