Package: wormneuroatlas
Worm Neuro AtlasNeural signal propagation atlas [1], genome [2], and single-cell transcriptome [3], neuropeptide/GPCR deorphanization [4], anatomical connectome [5,6], monoaminergic connectome [7], and chemical-synapse sign predictions [8] all in one place. Worm Neuro Atlas allows to build a basic version of the neuropeptidergic connectome [9] ([9] contains more detailed analysis). Please cite the code if you use it, along with the papers containing the datasets you use.Read the full documentation herehttps://francescorandi.github.io/wormneuroatlas/Run a demo of Worm Neuro Atlas in a Jupyter notebook on Colab herehttps://colab.research.google.com/drive/1j59Mv_PSaD4Nj2ITNDQWVxWfrl6ssqXy?usp=sharingInstallationwormenuroatlascan be installed via pippip install wormneuroatlas
# or
python -m pip install wormneuroatlasor by cloning this repository and pip install the module, e.g. viagit clone git@github.com:francescorandi/wormneuroatlas.git
cd wormneuroatlas
python -m pip install .NeuroAtlas classNeuroAtlas is the main class that aggregates all the datasets, and directly handles the Signal Propagation Atlas, the anatomical connectome, and the monoaminergic connectome [7].You can access the wild-type and unc-31 signal propagation atlas, for example, viaNeuroAtlas.get_signal_propagation_atlas(strain="wt") # or strain="unc31"NeuroAtlas will use the other classes of the Python module (WormFunctionalConnectivity, WormBase, Cengen, PeptideGPCR, ...) to access data from other datasets like neural signal propagation, genome, single-cell transcriptome and aggregate those datasets.
For example, you can get gene expression levels of genes flp-1 and aqp-1 in neurons AVAL and AVDR viaNeuroAtlas.get_gene_expression(gene_names=["flp-1","aqp-1"], neuron_names=["AVAL","AVDR"]).SeeExamplesfor more information on how to access the various datasets.NeuroAtlas also manages the conversions between different conventions for neural IDs. NeuroAtlas can be instantiated to maintain the "exact" neural identities, or to merge neurons into classes (i.e. to approximate neuron identities):merge_bilateral=Truewill merge results for, e.g., AVAL and AVAR into the class AVA_,merge_dorsoventral=Truewill merge RMED and RMEV into RME_, whilemerge_numbered=Truewill merge VB3, VB4, ... into VB. These options can be combined to merge, for example, SMBVL, SMBVR, SMBDL, and SMBDR into SMB__ withmerge_bilateral=True, merge_dorsoventral=True.Cengen classThe Cengen class interfaces with single-cell RNASeq database from the CeNGEN project. Its main function is Cengen.get_gene_expression(), which is called byNeuroAtlas.get_gene_expression()after converting neuron IDs to CeNGEN-style IDs.PeptideGPCRThe PeptideGPCR class provides an interface to the neuropeptide/GPCR deorphanization in [4]. The two main functions arePeptideGPCR.get_gpcrs_binding_to(peptides)andPeptideGPCR.get_peptides_binding_to(gpcrs)which return the GPCRs binding to given peptides and the peptides binding to given GPCRs, respectively.WormBase classThe WormBase class uses the REST API provided by wormbase.org. WormBase currently has methods to retrieve lists of transcripts for given genes, as well as functions to convert WormBase-style gene IDs to gene names, etc.Examplesplot_signal_propagation.py shows you how to access the signal propagation data,gene_expression.py shows you how to get gene expression data from CeNGEN,peptideGPCR_binding.py shows you how to get the peptides binding to given GPCRs and vice versa,make_peptidergic_connectome.py shows you how to combine these functions to compile the neuropeptidergic connectome [9] using gene expression and neuropeptide/GPCR deorphanization.ReferencesRandi et al., arXiv 2022https://arxiv.org/abs/2208.04790WormBase, wormbase.orgTaylor et al., Cell 2021Beets et al., bioRxiv 2022https://www.biorxiv.org/content/10.1101/2022.10.30.514428v1White et al., Phil. Trans. R. Soc 1986Witvliet et al., Nature 2021Bentley et al., PLOS Comp. Bio. 2016Fenyves et al., PLOS Comp. Bio. 2020Ripoll-Sanchez et al., bioRxiv 2022https://www.biorxiv.org/content/10.1101/2022.10.30.514396v2