Research Object Crate for Use Case 5: Small Molecules to Induce a Biological Process

The workflow starts with selecting Autophagy as the search term. Gene sets with set labels containing Autophagy were queried from Enrichr[1]. Identified matching terms from the MGI Mammalian Phenotype Level 4 2019[2] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for MGI_Mammalian_Phenotype_Level_4_2019. All the identified gene sets were combined using the union set operation. Reversers and mimickers from over 1 million signatures were identified using SigCom LINCS[4]. Resolved drugs from the LINCS L1000 Chemical Perturbagens library. Identified matching terms from the KEGG 2021 Human[6] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for KEGG_2021_Human. All the identified gene sets were combined using the union set operation. Reversers and mimickers from over 1 million signatures were identified using SigCom LINCS[4]. Identified matching terms from the GO Biological Process 2021[7] library were assembled into a collection of gene sets. A GMT was extracted from the Enrichr results for GO_Biological_Process_2021. All the identified gene sets were combined using the union set operation. Reversers and mimickers from over 1 million signatures were identified using SigCom LINCS[4]. Resolved drugs from the LINCS L1000 Chemical Perturbagens library. Resolved drugs from the LINCS L1000 Chemical Perturbagens library. The mean across multiple Scored Drugs is computed. The drugs were filtered by FDA Approved Drugs with the help of PubChem APIs[8]. 1. Xie, Z. et al. Gene Set Knowledge Discovery with Enrichr. Current Protocols vol. 1 (2021). doi:10.1002/cpz1.90 2. Blake, J. A. et al. Mouse Genome Database (MGD): Knowledgebase for mouse–human comparative biology. Nucleic Acids Research vol. 49 D981–D987 (2020). doi:10.1093/nar/gkaa1083 4. Evangelista, J. E. et al. SigCom LINCS: data and metadata search engine for a million gene expression signatures. Nucleic Acids Research vol. 50 W697–W709 (2022). doi:10.1093/nar/gkac328 6. Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Research vol. 51 D587–D592 (2022). doi:10.1093/nar/gkac963 7. Ashburner, M. et al. Gene Ontology: tool for the unification of biology. Nature Genetics vol. 25 25–29 (2000). doi:10.1038/75556 8. Kim, S. et al. PubChem 2023 update. Nucleic Acids Research vol. 51 D1373–D1380 (2022). doi:10.1093/nar/gkac956

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