2025-zoogle-experiments

Purpose

This repository accompanies three pubs: - A framework for modeling human monogenic diseases by deploying organism selection - Modeling human monogenic diseases using the choanoflagellate Salpingoeca rosetta - Modeling human monogenic diseases using the tunicate Ciona intestinalis

Most of the code in this repository is used to process and visualize the data used in the latter two of these publications. Please refer to the individual publications for links to the code in this repository that is relevant to each publication.

Overview of computational analysis

Organism selection data download and processing

1. Download organism selection data and human disease data. This includes data from the following sources:
   • The 2024 organism selection dataset on Zenodo here. We use v1.1.
   • Processed Orphanet data from Compass.
   • ClinVar gene-condition-source-id mapping.
   • HGNC gene set.
   • Species tree from speciesRAX.
2. Process downloaded data so that it's tidy and usable for filtering scripts.
3. Run filtering scripts.

Salpingoeca rosetta RNA-Seq analysis.

1. Download processed RNA-Seq data from Figshare.
2. Generate bar plots and heatmaps of Salpingoeca rosetta gene expression.

Ciona intestinalis scRNA-Seq and RNA-Seq analysis.

1. Download processed scRNA-Seq and RNA-Seq data from Figshare and other sources.
2. Generate mapping files between Ciona gene IDs and other identifiers.
3. Generate scatter plots and other plots of Ciona gene expression.

Utilities

1. Utility to generate a scatter plot of best hits from each organism for a given gene, a la the organism selection publication.

Installation and Setup

This repository uses conda to manage software environments and installations. You can find operating system-specific instructions for installing miniconda here. After installing conda and mamba, run the following command to create the pipeline run environment.

bash mamba env create -n 2025-zoogle-experiments --file envs/dev.yml mamba activate 2025-zoogle-experiments

zoogletools

This repository contains a Python package called zoogletools. This package contains the source code for the filtering pipeline and other utilities.

To install the package, run the following command from the top level of this repository.

bash pip install -e .

Data

To download data for this analysis, we have developed a lightweight Snakemake workflow. Replace <n> with the number of cores you want to use.

bash snakemake --snakefile workflows/download_data.snakefile --use-conda --cores <n>

This will download the data from the URLs specified in the workflows/download_config.yaml file and save it to the data directory. This can take some time (30 minutes to 1 hour) to run, depending on the number of cores you use. Note that the total size of the downloaded data is around 3GB.

Ciona proteome and scRNA-Seq data

Accessing the Ciona proteome, RNA-Seq, and scRNA-Seq data requires a few manual steps.

1. Unzip the following files using your preferred method, e.g. the unzip command. Ensure that the files are unzipped into the same directory as the zipped files, with the same file names.

   • data/Ciona_intestinalis_scRNAseq_data_Piekarz.zip
   • data/Cirobu_RNASeq.tar.gz
   • data/Ciona_gene_models/HT.KY21Gene.protein.2.fasta.zip
   • data/Ciona_gene_models/Ciona_intestinalis.faa.gz

   Example commands are shown below. bash mkdir -p data/Cirobu_RNASeq && tar -xzvf data/Cirobu_RNASeq.tar.gz -C data/Cirobu_RNASeq gunzip data/Ciona_gene_models/Ciona_intestinalis.faa.gz unzip data/Ciona_gene_models/HT.KY21Gene.protein.2.fasta.zip -d data/Ciona_gene_models/HT.KY21Gene.protein.2.fasta unzip data/Ciona_intestinalis_scRNAseq_data_Piekarz.zip -d data/Ciona_intestinalis_scRNAseq_data_Piekarz

2. Download the cell type annotation for the Ciona scRNA-Seq data. You need to create an account with the Broad Institute Single Cell Portal and download the data manually. The webpage for the dataset is here. Navigate to the Download section and click the "Bulk download" button. A command will be generated with an authentication code, which you can run to download the data. An example command is shown below (in the real command, an actual authentication code should appear in place of <auth_code>).

   bash curl -k "https://singlecell.broadinstitute.org/single_cell/api/v1/bulk_download/generate_curl_config?accessions=SCP454&auth_code=<auth_code>&directory=all&context=study" -o cfg.txt; curl -K cfg.txt && rm cfg.txt

   Make sure to run this command in the data/ directory of this repository. This will put the data into data/SCP454/, organized into three directories: cluster/, expression/, and metadata/. The only essential file for downstream analyses is data/SCP454/cluster/ciona10stage.cluster.upload.new.txt, which contains the cell type annotation.

   Note: if you encounter a curl: (2) no URL specified error, make sure you are using the correct authentication code and retry the command.

Overview

Description of the folder structure

This repository contains the following folders: - data: Contains the data for the analysis. - notebooks: Contains notebooks that frame the analyses. - workflows: Contains the Snakemake workflow for downloading the data. - zoogletools: A pip-installable Python package containing the source code for the filtering pipeline and other utilities.

Methods

1. Pre-processing. After downloading the data, the data is preprocessed and filtered using scripts included in the zoogletools package. See this README for more details.

2. Filtering. To run the filtering pipeline, refer to the filtering notebook.

3. Salpingoeca rosetta analysis. To generate plots of Salpingoeca rosetta gene expression, refer to the plotting notebook.

4. Ciona identifier mapping. To see how we generated identifier mappings between Ciona gene IDs and other identifiers, refer to all-v-all BLAST notebook and post-processing notebook.

5. Ciona cell cluster annotation. To see how we generated cell cluster annotations for the Ciona scRNA-Seq data, refer to the cell cluster annotation notebook.

6. Ciona scRNA-Seq analysis. To generate plots of Ciona scRNA-Seq data, refer to the Ciona scRNA-Seq notebook.

Compute Specifications

The analysis was performed on a MacBook Pro with an M1 Pro processor and 32GB of RAM running macOS Sequoia 15.1.

Contributing

See how we recognize feedback and contributions to our code.

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