Sum(lengths(regmatches(genes, gregexpr("\\AT", genes, ignore.case = TRUE))))
#Clc genomics workbench negative binomial output code#
The code is: reads <- as.matrix(read.csv("genereads_ONLY4.txt", sep = '\t', row.names = 1, header = TRUE)) So I wrote a function to convert all of the information I had to either symbols or entrez IDs. The study of the COVID-19 pandemic often involves sequencing the SARS-CoV-2. Discovery and Research Bioinformatics analysis of Nanopore sequencing data for SARS-CoV-2. The gmt and fgsea information I'm using can only read gene symbols (e.g. Introducing CLC Genomics Workbench 12 an exciting new release that’s making. Okay, I found a round about way to solve this, at least for my scenario. Test1 <- getBM(attributes='external_gene_name', ListDatasets(useMart(biomart="plants_mart",host=""))Įnsembl = useDataset("athaliana_eg_gene",mart= mart) Mart = useMart(biomart="plants_mart",host="")
Meta <- read.table("metatest4.txt", header = TRUE, fileEncoding= "UTF-16LE") Reads <- as.matrix(read.csv("genereads_ONLY4.txt", sep = '\t', row.names = 1, header = TRUE)) #reads file was created in CLC Genomics Workbench, then the reads column copied and pasted for #data load in and conversions, meta matrix/design creation: I've tried various types of attributes, but none of them have worked. The original list from CLC has all Arabidopsis genes (27,655) and the outputs from getBM() generally have 12,085 gene names or less.Īnybody done this type of conversion before with success? When I use biomaRt's getBM() function, it returns a lot less genes than the list of genes I'm reading into it. I would like to convert them all to gene names, so I can run it through a GO terms R package (the package seemingly does not read IDs like AT1G01390). The list of genes contains a mix of gene names (i.e. I have an output of RNA-seq reads from CLC genomics workbench, for Arabidopsis thaliana.
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