Lemna minor annotation R package (org.Lminor.eg.db) and corresponding files behind the custom built

This public repository containing the following files: Custom built annotation R package for the <em>Lemna minor </em>reference genome [ org.Lminor.eg.db.7z ]. The package was built via AnnotationForge using sequence homology of protein coding genes for functional characterisation (Description, PFAMs, GO terms). A combined approach using blastx and EMBL's eggNOG mapper was used for this task. This package is compatible with clusterProfiler for downstream functional enrichment analysis (ORA / GSEA) of <em>L. minor</em> transcriptomic / proteomic data.<br> <br> For <strong>how to install and use this package</strong> in your R session,<strong> check the R code example below</strong>.<br> Reference genome, genome annotation (gtf), gene coding sequences (cds) and cds translated peptide sequences (cds.pep) of the duckweed <em>Lemna minor </em>[ Lminor_refGenome_GTF_CDS.7z ].<br> The reference genome assembly fasta was downloaded from www.lemna.org. Matching GTF annotation file was generated via '<em>gffread</em>', from the GFF<em> </em>annotation file available here.<br> With the cds translated peptide file, a blastp search was performed against a custom plant protein sequence database [ Lminor_ref.org.Db4blastp.7z ]. The custom database was built from the proteomes of well annotated reference plant species. (For details refer to the readme file within the compressed folder) For more details please refer to our publication in XXX<br> DOI: XXX <pre><code class="language-bash"># 1. Download and unzip (7zip format) the org.Lminor.eg.db package. # 2. Install the package via: orgDb = "path/to/org.Lminor.eg.db/" install.packages(orgDb, type="source", repos=NULL) # 3. Restart R session then load package require(org.Lminor.eg.db) require(AnnotationDbi) # to check for columns and keytypes: columns(org.Lminor.eg.db) keytypes(org.Lminor.eg.db) # query the org.Lminor.eg.db for particular Lemna gene IDs (GID) gid = keys(org.Lminor.eg.db, keytype="GID") col = columns(org.Lminor.eg.db)[c(5,17,9,15,1,8,14)] df = select(org.Lminor.eg.db, keys=gid[1000:1100], columns=col, keytype="GID") View(df) ### Running overrepresenation analysis in clusterProfiler using the Lminor annotation package ### # ORA for multiple gene sets via compareCluster() require(clusterProfiler) genLs = list(setA = gid[1:40], setB = gid[100:140], setC = gid[1000:1040]) res = compareCluster(genLs, fun = "enrichGO", OrgDb = "org.Lminor.eg.db", keyType = "GID", ont = "BP", universe = gid) # Compute semantic similiarities among GO terms: d = GOSemSim::godata('org.Lminor.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$pvalue),] View(res@compareClusterResult) # Network plot emapplot(resS, showCategory = 30)</code></pre>本公开仓库包含如下文件：针对小浮萍（*Lemna minor*）参考基因组定制开发的注释R包[org.Lminor.eg.db.7z]。该包通过AnnotationForge，基于蛋白编码基因的序列同源性完成功能注释（涵盖基因描述信息、PFAM结构域、基因本体（Gene Ontology, GO）术语），本次注释采用了blastx与EMBL的eggNOG映射器相结合的联合分析策略。本R包可兼容clusterProfiler，用于开展小浮萍（*L. minor*）转录组与蛋白质组数据的下游功能富集分析，包括过表达富集分析（ORA）与基因集富集分析（GSEA）。 <br><br>如需了解**该R包的安装与使用方法**，请参阅下方的R代码示例。 <br>小浮萍（*Lemna minor*）的参考基因组、基因组注释文件（GTF格式）、基因编码序列（CDS）以及CDS翻译得到的肽序列文件（cds.pep）[Lminor_refGenome_GTF_CDS.7z]。 <br>该参考基因组组装的FASTA文件下载自www.lemna.org。配套的GTF注释文件通过gffread工具，由本仓库提供的GFF注释文件生成。 <br>针对CDS翻译得到的肽序列文件，我们基于自定义植物蛋白序列数据库开展了blastp搜索[Lminor_ref.org.Db4blastp.7z]。该自定义数据库由多个经过精细注释的模式植物物种的蛋白质组构建而成，详细说明请参阅压缩包内的README文件。 <br>更多研究细节请参阅我们发表于XXX的学术论文，DOI：XXX。 <pre><code class="language-bash"># 1. 下载并解压（7zip格式）org.Lminor.eg.db包。 # 2. 通过如下命令安装该包：orgDb = "path/to/org.Lminor.eg.db/" install.packages(orgDb, type="source", repos=NULL) # 3. 重启R会话并加载包：require(org.Lminor.eg.db) require(AnnotationDbi) # 用于检查可查询的列与键类型：columns(org.Lminor.eg.db) keytypes(org.Lminor.eg.db) # 针对特定的浮萍基因ID（GID）查询org.Lminor.eg.db包： gid = keys(org.Lminor.eg.db, keytype="GID") col = columns(org.Lminor.eg.db)[c(5,17,9,15,1,8,14)] df = select(org.Lminor.eg.db, keys=gid[1000:1100], columns=col, keytype="GID") View(df) ### 使用Lminor注释包在clusterProfiler中开展过表达富集分析 ### # 通过compareCluster()对多组基因集开展ORA分析： require(clusterProfiler) genLs = list(setA = gid[1:40], setB = gid[100:140], setC = gid[1000:1040]) res = compareCluster(genLs, fun = "enrichGO", OrgDb = "org.Lminor.eg.db", keyType = "GID", ont = "BP", universe = gid) # 计算GO术语间的语义相似性： d = GOSemSim::godata('org.Lminor.eg.db', ont="BP", computeIC=FALSE, keytype = "GID") res = enrichplot::pairwise_termsim(res, method = "Wang", semData = d) # 移除冗余生物学信息的GO术语： resS = simplify(res, .8) # 根据p值对结果进行排序： resS@compareClusterResult = resS@compareClusterResult[order(resS@compareClusterResult$pvalue),] View(res@compareClusterResult) # 绘制网络图： emapplot(resS, showCategory = 30)</code></pre>