## ----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 = "Alexaki_Human",
#                       assembly = "Homo_sapiens_GRCh38_101",
#                       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("PRJNA591214", 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$ScientificName[1] # Usually you find organism here, else set it yourself
#  paired.end.rfp <- study.rfp$LibraryLayout == "PAIRED"
#  paired.end.rna <- study.rna$LibraryLayout == "PAIRED"
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # 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"],
#    assembly_type = "primary_assembly"
#  )
#  
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # 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, wait = TRUE, max.ram = 20, SAsparse = 2)
#  
#  # Show all annotations you have made with ORFik so far
#  list.genomes()
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # Alignment (with depletion of phix, rRNA, ncRNA and tRNAs) & (with MultiQC of final STAR alignment)
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  
#  STAR.align.folder(conf["fastq Ribo-seq"], conf["bam Ribo-seq"], index,
#                    paired.end = paired.end.rfp,
#                    steps = "tr-co-ge", # (trim needed: adapters found, then genome)
#                    adapter.sequence = "auto", # Adapters are auto detected
#                    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-co-ge", # (trim needed: adapters found, then genome)
#                    adapter.sequence = "auto", # Adapters are auto detected
#                    trim.front = 0, min.length = 20)
#  
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # Create experiment (Starting point if alignment is finished)
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  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 = c(1,2,3,1,2,3),
#                    condition = rep(c("CO", "WT"), each = 3))
#  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 = c(1,2,3,1,2,3),
#                    condition = rep(c("CO", "WT"), each = 3))
#  
#  library(ORFik)
#  # Show the experiments you have made with ORFik so far
#  list.experiments()
#  df.rfp <- read.experiment("Alexaki_Human_Ribo-seq")
#  df.rna <- read.experiment("Alexaki_Human_RNA-seq")
#  
#  
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # Convert files and run Annotation vs alignment QC
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # General QC
#  ORFikQC(df.rfp)
#  ORFikQC(df.rna)
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # P-shifting of Ribo-seq reads:
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # From ORFikQC it looks like 20, 21, 27 and 28 are candidates for Ribosomal footprints
#  shiftFootprintsByExperiment(df.rfp, accepted.lengths = c(20:21, 27:28))
#  # 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))
#  remove.experiments(df.rfp)
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # Create heatmaps (Ribo-seq)
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # Pre-pshifting
#  heatMapRegion(df.rfp, region = c("TIS", "TTS"), 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", "TTS"), 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
#  # 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
#  # TE merged all libraries
#  countsRFP <- countTable(df.rfp, region = "cds", type = "fpkm", collapse = "all", count.folder = "pshifted")
#  countsRNA <- countTable(df.rna, region = "mrna", type = "fpkm", collapse = "all")
#  countsTE <- (countsRFP + 1) / (countsRNA + 1) # with pseudo count
#  
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # 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 11 unique DTEG genes
#  res <- DTEG.analysis(df.rfp, df.rna)
#  # Now let's check if the CO group overexpress the F9 Gene (ENSG00000101981):
#  significant_genes <- res[Regulation != "No change",]
#  gene_names <- txNamesToGeneNames(significant_genes$id, df.rfp)
#  "ENSG00000101981" %in% unique(gene_names) # TRUE
#  # It does, good good.
#  # How is it regulated ?
#  significant_genes[which(gene_names == "ENSG00000101981"),] # By mRNA abundance
#  # 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")
#  ORFik::windowCoveragePlot(peaks, type = "cds", scoring = "transcriptNormalized")
#  # The gene does not have a strong max peak in WT rep1
#  "ENSG00000101981" %in% peaks$gene_id # FALSE
#  
#  peaks_CO <- findPeaksPerGene(loadRegion(df.rfp, "cds"), reads = RFP_CO_r1, type = "max")
#  # The gene does not have a strong max peak in CO rep1 either
#  "ENSG00000101981" %in% peaks_CO$gene_id # FALSE
#  
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # Feature table (From WT rep 1)
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  cds <- loadRegion(df.rfp, "cds")
#  cds <- ORFik:::removeMetaCols(cds) # Dont need them
#  cds <- cds[filterTranscripts(df.rfp)] # 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 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)
#  # 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)
#  library(RiboCrypt)
#  cds <- loadRegion(df.rfp, "cds")
#  RiboCrypt::multiOmicsPlot_list(cds[1640], cds[1640], 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")
#  
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  # uORF analysis (advanced under development!)
#  # (using the extension package to ORFik: uORFomePipe)
#  # Will create a mysql database + bed12 file of uORFs with color codes + plots + files with results
#  #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
#  devtools::install_github("Roleren/uORFomePipe", dependencies = TRUE)
#  library(uORFomePipe)
#  find_uORFome("/media/roler/S/data/Bio_data/projects/Alexaki_uORFome/",
#               df.rfp = df.rfp, df.rna = df.rna, df.cage = NULL, biomart = NULL,
#               startCodons = "ATG|CTG|TTG|GTG", BPPARAM = BiocParallel::MulticoreParam(2))
#  
#  grl <- getUorfsInDb()
#  pred <- readTable("finalPredWithProb")$prediction
#  cov <- readTable("startCodonCoverage")
#  grl[pred == 1 & rowSums(cov) > 5]
#  

