## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----eval = FALSE, echo = TRUE, message = FALSE------------------------------- # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Ribo-seq HEK293 (2020) Investigative analysis of quality of new Ribo-seq data # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Article: https://f1000research.com/articles/9-174/v2#ref-5 # # Design: Wild type (WT) vs codon optimized (CO) (gene F9) # library(ORFik) # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Config # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Specify paths wanted for NGS data, genome, annotation and STAR index # # If you use local files, make a conf variable with existing directories # # Seperate Ribo-seq and RNA-seq into separate folders with type argument # conf <- config.exper(experiment = "Tsvetkov_Yeast", # assembly = "Yeast_SacCer3", # type = c("Ribo-seq", "RNA-seq")) # # Will create default config paths, if you want more control of where the # # data is stored, check out function config() function # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Download fastq files for experiment and rename (Skip if you have the files already) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # SRA Meta data download (work for ERA, DRA and GEO too) # study <- download.SRA.metadata("PRJNA644594", auto.detect = TRUE) # # Auto detection worked, all Ribo-seq and RNA-seq samples detected # # NOTE: Could not detect condition CO, only wild type (WT) # # # Split study into (Ribo-seq / RNA-seq) # study.rfp <- study[LIBRARYTYPE == "RFP",] # study.rna <- study[LIBRARYTYPE == "RNA",] # # Download fastq files (uses SRR numbers (RUN column) from study)) # # The sample_title column had good names to rename files: # download.SRA(study.rfp, conf["fastq Ribo-seq"], # rename = study.rfp$sample_title, subset = 2000000) # download.SRA(study.rna, conf["fastq RNA-seq"], # rename = study.rna$sample_title, subset = 2000000) # # # Which organism is this, scientific name, like "Homo sapiens" or "Danio rerio" # organism <- study$ScientificName[1] # Usually you find organism here, else set it yourself # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Annotation (Download genome, transcript annotation and contaminants) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # annotation <- getGenomeAndAnnotation( # organism = organism, # genome = TRUE, GTF = TRUE, # phix = TRUE, ncRNA = TRUE, tRNA = TRUE, rRNA = TRUE, # output.dir = conf["ref"], optimize = TRUE, gene_symbols = TRUE, # pseudo_5UTRS_if_needed = 100 # If species have not 5' UTR (leader) definitions, make 100nt pseudo leaders. # ) # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # STAR index (index the genome and contaminants seperatly) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Remove max.ram = 20 and SAsparse = 2, if you have more than 64GB ram # index <- STAR.index(annotation, max.ram = 20, SAsparse = 2) # # # Show all annotations you have made with ORFik so far, validate your genome has gtf, genome and STAR index flags as TRUE. # list.genomes() # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Alignment (with depletion of phix, rRNA, ncRNA and tRNAs) & (with MultiQC of final STAR alignment) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # paired.end.rfp <- study.rfp$LibraryLayout == "PAIRED" # paired.end.rna <- study.rna$LibraryLayout == "PAIRED" # # STAR.align.folder(conf["fastq Ribo-seq"], conf["bam Ribo-seq"], index, # paired.end = paired.end.rfp, # steps = "tr-ge", # (trim needed: adapters found, then genome) # adapter.sequence = "TCGTATGCCGTC", # Adapters are not auto detected by fastp # trim.front = 0, min.length = 20) # # STAR.align.folder(conf["fastq RNA-seq"], conf["bam RNA-seq"], index, # paired.end = paired.end.rna, # steps = "tr-ge", # (trim needed: adapters found, then genome) # adapter.sequence = "TCGTATGCCGTC", # Adapters are not auto detected by fastp # trim.front = 0, min.length = 20) # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Create experiment (Starting point if alignment is finished) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # We now collect all the information into 1 object per library type # library(ORFik) # create.experiment(file.path(conf["bam Ribo-seq"], "aligned/"), # exper = conf["exp Ribo-seq"], # fa = annotation["genome"], # txdb = paste0(annotation["gtf"], ".db"), # organism = organism, # pairedEndBam = paired.end.rfp, # rep = study.rfp$REPLICATE, # condition = study.rfp$CONDITION, # runIDs = study.rfp$Run) # # create.experiment(file.path(conf["bam RNA-seq"], "aligned/"), # exper = conf["exp RNA-seq"], # fa = annotation["genome"], # txdb = paste0(annotation["gtf"], ".db"), # organism = organism, # pairedEndBam = paired.end.rna, # rep = study.rna$REPLICATE, # condition = study.rna$CONDITION, # runIDs = study.rna$Run) # # library(ORFik) # # Show the experiments you have made with ORFik so far # list.experiments(validate = FALSE) # df.rfp <- read.experiment("Tsvetkov_Yeast_Ribo-seq") # df.rna <- read.experiment("Tsvetkov_Yeast_RNA-seq") # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Convert files and run Annotation vs alignment QC # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # General QC # ORFikQC(df.rfp) # ORFikQC(df.rna) # # After ribo-seq QC is done, check that reads are centering on ~28nt if normal riboseq, # # and hopefully > 20% of alignments overlaps mrna. # # # PCA for Ribo-seq vs RNA-seq # fpkm_table <- cbind(countTable(df.rfp, type = "fpkm"), countTable(df.rna, type = "fpkm")) # pcaPlot(fpkm_table) # The samples seperate well between library types # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # P-shifting of Ribo-seq reads: # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # From ORFikQC it looks like 20, 21, 27:30 are candidates for Ribosomal footprints # shiftFootprintsByExperiment(df.rfp, accepted.lengths = c(20:21, 27:30)) # # Now check if you are happy with shifts, these libraries have some interesting # # periodicity for read length 20 and 27, # # it has identical amount of reads in frame 0 and 1, not optimal for ORF detection. # shiftPlots(df.rfp, output = "auto", downstream = 30) # Barplots, better details # shiftPlots(df.rfp, output = "auto", downstream = 30, type = "heatmap") # Heatmaps, better overview # # # Ribo-seq specific QC # remove.experiments(df.rfp) # Remove loaded data (it is not pshifted) # RiboQC.plot(df.rfp, BPPARAM = BiocParallel::SerialParam(progressbar = TRUE)) # # A high rRNA concentration, using rRNA depletion protocols before sequencing could have fixed this # remove.experiments(df.rfp) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Create heatmaps (Ribo-seq) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Pre-pshifting # heatMapRegion(df.rfp, region = c("TIS"), shifting = "5prime", type = "ofst", # outdir = file.path(QCfolder(df.rfp), "heatmaps/pre-pshift/")) # remove.experiments(df.rfp) # # After pshifting # heatMapRegion(df.rfp, region = c("TIS"), shifting = "5prime", type = "pshifted", # outdir = file.path(QCfolder(df.rfp), "heatmaps/pshifted/")) # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Count table analysis: TE tables # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Shifting looks good, let's make count tables of pshifted libraries: # # As a note: Correlation between count tables of pshifted vs raw libs is ~ 40% usually. # countTable_regions(df.rfp, lib.type = "pshifted", rel.dir = "pshifted") # # # TE per library match # countsRFP <- countTable(df.rfp, region = "cds", type = "fpkm", collapse = FALSE, count.folder = "pshifted") # countsRNA <- countTable(df.rna, region = "mrna", type = "fpkm", collapse = FALSE) # countsTE <- (countsRFP + 1) / (countsRNA + 1) # with pseudo count # summary(countsTE) # Good stability of TE, no strong ribosome abundance regulation # # # TE per condition (WT vs CO) (collapse replicates) # countsRFP <- countTable(df.rfp, region = "cds", type = "fpkm", collapse = TRUE, count.folder = "pshifted") # countsRNA <- countTable(df.rna, region = "mrna", type = "fpkm", collapse = TRUE) # countsTE <- (countsRFP + 1) / (countsRNA + 1) # with pseudo count # summary(countsTE) # Quite similar abundance over groups # # TE merged all libraries # countsRFP <- countTable(df.rfp, region = "cds", type = "fpkm", collapse = "all", count.folder = "pshifted")[[1]] # countsRNA <- countTable(df.rna, region = "mrna", type = "fpkm", collapse = "all")[[1]] # countsTE <- (countsRFP + 1) / (countsRNA + 1) # with pseudo count # summary(countsTE[countsRFP > 10 & countsRNA > 10]) # Gene with biggest normalized ratio is 8 ribosomes per mrna fragment # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Differential translation analysis (condition: WT vs CO) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # The design is by default chosen by this factor: The condition column in this case # design(df.rfp, multi.factor = FALSE) # # We now run, and here get 210 unique DTEG genes # res <- DTEG.analysis(df.rfp, df.rna) # # Now let's check if the Heat shock group overexpress the HSP90 Gene (formal name: HSC82): # symbols <- symbols(df.rfp) # Let's fetch the gene symbols table we made earlier # HSP90_tx_id <- symbols[grep("HSC82", external_gene_name, ignore.case = T)]$ensembl_tx_name # res[id == HSP90_tx_id] # # It does, good good (Not for subset, not enough coverage there, only if you downloaded full libraries). # # How is it regulated ? # res[id == HSP90_tx_id]$Regulation # By mRNA abundance (No change in subset) # significant_genes <- res[Regulation != "No change",] # # # If you downloaded the full libraries, do this to use pshifted libraries instead. # # Not a valid result for pshifted libraries using subset # res <- DTEG.analysis(df.rfp, df.rna, design = "condition", # RFP_counts = countTable(df.rfp, region = "cds", type = "summarized", # count.folder = "pshifted")) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Peak detection (strong peaks in CDS) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # peaks <- findPeaksPerGene(loadRegion(df.rfp, "cds"), reads = RFP_WT_r1, type = "max") # # # Where along the coding sequences are the strongest peaks ? # ORFik::windowCoveragePlot(peaks, type = "cds", scoring = "transcriptNormalized") # # # The gene does not have a strong max peak in WT rep1 # "YMR186W_mRNA" %in% peaks$gene_id # FALSE # # peaks_HSR <- findPeaksPerGene(loadRegion(df.rfp, "cds"), reads = RFP_HSR_r1, type = "max") # # The gene does not have a strong max peak in CO rep1 either # "YMR186W_mRNA" %in% peaks$gene_id # FALSE # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Codon analysis (From WT rep 1 & HSR rep 1) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # codon_table <- codon_usage_exp(df.rfp[df.rfp$rep == 1,], outputLibs(df.rfp[df.rfp$rep == 1,], type = "pshifted", output.mode = "list"), # cds = loadRegion(df.rfp, "cds", filterTranscripts(df.rfp, minThreeUTR = NULL))) # codon_usage_plot(codon_table) # There is an increased dwell time on (R:CGC) of A-sites in both conditions # codon_usage_plot(codon_table, ignore_start_stop_codons = TRUE) # # There is an increased dwell time on (R:CGG) of A-sites of HSP condition, why ? # # # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Feature table (From HSR rep 3) # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # cds <- loadRegion(df.rfp, "cds") # cds <- ORFik:::removeMetaCols(cds) # Dont need them # cds <- cds[filterTranscripts(df.rfp, minThreeUTR = NULL)] # Filter to sane transcripts (annotation is not perfect) # dt <- computeFeatures(cds, # RFP = fimport(filepath(df.rfp[6,], "pshifted")), # RNA = fimport(filepath(df.rna[6,], "ofst")), Gtf = df.rfp, # grl.is.sorted = TRUE, faFile = df.rfp, # weight.RFP = "score", weight.RNA = "score", # riboStart = 21, uorfFeatures = FALSE) # # The features of significant DTEGs. # dt[names(cds) %in% significant_genes$id,] # # All genes with strong 3nt periodicity of Ribo-seq # dt[ORFScores > 5,] # # Not all genes start with ATG, possible errors in annotation # table(dt$StartCodons) # 5 Genes with ATA start codons ? # # All genes with strong start codon peak # dt[startCodonCoverage > 5,] # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # Gene plotting (advanced under development!) # # (Using package that extends ORFik for interactive html plots (RiboCrypt)) # # Will create interactive plot for Ribo-seq and RNA-seq sample: Wild type rep 3 # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # This package also available on Bioconductor since Bioc version 3.14 # # BiocManager::install("RiboCrypt") # devtools::install_github("m-swirski/RiboCrypt", dependencies = TRUE) # Restart R if you already had RiboCrypt installed # library(RiboCrypt) # cds <- loadRegion(df.rfp, "cds") # mrna <- loadRegion(df.rfp, "mrna") # RiboCrypt::multiOmicsPlot_list(mrna[HSP90_tx_id], cds[HSP90_tx_id], reference_sequence = findFa(df.rfp@fafile), reads = list(fimport(filepath(df.rna[6,], "ofst")), fimport(filepath(df.rfp[6,], "pshifted"))), # ylabels = c("RNA", "RFP"), withFrames = c(F, T), frames_type = "columns") # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # # All ORF type predictions # # Prediction using peridicity (Similar to RiboCode, ORFScore, minimum coverage, and comparison # # to upstream and downstream window) # # Will create 3 files in format (.rds), GRangesList of candidate ORFs, of predicted ORFs and a table # # of all scores per ORF used for prediction # #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤# # prediction_output_folder <- file.path(libFolder(df.rfp), "predicted_orfs") # tx_subset <- symbols[grep("^HSP|^HSC", external_gene_name)]$ensembl_tx_name # Predict on all HSP/HSC genes # # Run on 2 first libraries # ORFik::detect_ribo_orfs(df.rfp[1:2,], prediction_output_folder, # c("uORF", "uoORF", "annotated", "NTE", "NTT", "doORF", "dORF"), # startCodon = "ATG|CTG|TTG|GTG", # mrna = loadRegion(df.rfp, "mrna", tx_subset), # cds = loadRegion(df.rfp, "cds", tx_subset)) # Human also has a lot of ACG uORFs btw # table <- riboORFs(df.rfp[1:2,], type = "table", prediction_output_folder) # # Remember we are only predicting on 2 million reads, so we wont find that much # print(table(table[predicted == TRUE,]$type)) # 16 N-terminal extension of CDS predicted. # table[ensembl_tx_name == HSP90_tx_id & predicted == TRUE,] # # # I highly advice to check results with results of the python predictor RiboCode, # # it is by far the best alternative to ORFik prediction out there (I have tested: # # RiboTaper (deprecated), ORFquant (very bad), RiboTricer (very bad), RiboNT (OK), RiboCode (very good!)) # # I will link to my optimized github fork of RiboCode which supports input of ORFik covRle objects later # # (100x speedup compared to bam input, by using directly an internal hdf5 file!) #