Daphnia magna annotation R package (org.Dmagna.eg.db) for Daphmag2.4 (GCA_001632505.1) assembly

This repository provides a custom-built R OrgDb annotation package org.Dmagna.eg.db for the freshater crustacean Daphnia magna based on the Daphmag2.4 assembly (NCBI GCA_001632505.1). The Daphmag2.4 assembly, derived from the D. magna Xinb3 strain (GCA_001632505.1), was among the earliest D. magna genome assemblies made publicly available. (Note: This is a scaffold-level genome assembly consisting of 28.801 scaffolds and no assembled chromosomes.) The package contains curated mappings between Daphmag2.4 gene identifiers and functional annotations (EntrezGene IDs where available, gene symbols/descriptions, GO terms, PFAM domains, and orthology-based functional annotations), enabling downstream functional enrichment and pathway analyses inside the Bioconductor ecosystem (e.g., clusterProfiler, topGO, gProfileR, etc. compatible workflows).Note: This package will only work with your data when your mapping was performed against the identifiers provided in Daphmag2.4 ! Provenance and methods: Genome assembly used: Daphmag2.4 (NCBI Assembly GCA_001632505.1). Assembly record: https://www.ncbi.nlm.nih.gov/assembly/GCA_001632505.1 Annotation strategy: Primary homology search using BLASTx against curated protein databases to identify best-hit annotations for protein-coding genes. Complementary orthology and functional annotation with eggNOG-mapper to transfer GO terms, PFAM domains, KEGG Orthology (KOs) and COG categories where appropriate. AnnotationForge & AnnotationDbi were used to build the OrgDb (AnnotationDb) package from custom annotation tables, producing standard Bioconductor-compatible mappings (ENTREZID, SYMBOL, GENENAME, GO, PFAM, and orthology-derived annotations). This annotation approach follows the concepts and methods described in great detail in the following publication: Loll, Alexandra, Hannes Reinwald, Steve U. Ayobahan, Bernd Göckener, Gabriela Salinas, Christoph Schäfers, Karsten Schlich, Gerd Hamscher, and Sebastian Eilebrecht. “Short-Term Test for Toxicogenomic Analysis of Ecotoxic Modes of Action in Lemna Minor.” Environmental Science & Technology 56, no. 16 (August 16, 2022): 11504–15.DOI: https://doi.org/10.1021/acs.est.2c01777 How to install: Type the following code into your R console: # 1. Download the org.Dmagna.eg.db package and install ---------------------------- orgDb_url = "https://zenodo.org/records/17976141/files/org.Dmagna.eg.db.tar.gz?download=1" ## Define the destination file path dest_file = file.path(tempdir(), "org.Dmagna.eg.db.tar.gz") ## Download the file # We use mode = "wb" (write binary) to ensure the compressed file is not corrupted during download. download.file(url = orgDb_url, destfile = dest_file, mode = "wb") ## Install orgDb package from local tar.gz install.packages(dest_file, type="source", repos=NULL) # 2. Restart R session then load package --------------------------------------------- require(org.Dmagna.eg.db) require(AnnotationDbi) ## Check for columns and keytypes: columns(org.Dmagna.eg.db) keytypes(org.Dmagna.eg.db) Using custom orgDb with clusterProfiler - Code example: # query the org.Dmagna.eg.db for particular Lemna gene IDs (GID) --------------- gid = unique(keys(org.Dmagna.eg.db, keytype = "GID")) head(gid) # Have a quick look at the ids # Inspect the columns provided columns(org.Dmagna.eg.db) # Specify columns for which to retrieve infos col = c("UNIPROT_BestDescr","description_BestDescr","evalue_BestDescr", "identity_BestDescr", "PFAMs_eNOG") df1 = select(org.Dmagna.eg.db, keys = gid, keytype = "GID", columns = col) View(df1) # Inspect the results # retrieve infos for GO terms df = select(org.Dmagna.eg.db, keys = gid, keytype = "GID", columns = "GO") View(df) # Inspect the results # Running ORA in clusterProfiler using org.Dmagna.eg.db ------------------------ require(clusterProfiler) require(dplyr) # Specify example gene set for ORA # Let's find the genes with the most GO terms & pick the top 1000 as a random gene selection gene_set = table(df$GID) %>% sort(., decreasing = T) %>% names %>% .[1:1000] genLs = list(setA = gene_set[200:225], setB = gene_set[400:425], setC = gene_set[800:825]) # Run ORA with specified gene set. This might run a while ... res = compareCluster(genLs, fun = "enrichGO", OrgDb = "org.Dmagna.eg.db", keyType = "GID", ont = "BP", universe = gid) # Compute semantic similiarities among GO terms for Biological Processes (BP): d = GOSemSim::godata(annoDb = "org.Dmagna.eg.db", ont = "BP", computeIC=FALSE, keytype = "GID") res = enrichplot::pairwise_termsim(res, method = "Wang", semData = d) # Rmv GO terms with redudant biological information resS = simplify(res, .8) # resort results after pvalues resS@compareClusterResult = resS@compareClusterResult[order(resS@compareClusterResult$pvalue),] View(res@compareClusterResult) # Network plot clusterProfiler::emapplot(resS, showCategory = 5) clusterProfiler::cnetplot(resS, showCategory = 5)