# Ribo-seq pipeline (Yeast)

# Introduction

Welcome to the `ORFik` package. `ORFik` is an R package for analysis of transcript and translation features through manipulation of sequence data and NGS data.

This vignette will preview a simple Ribo-seq pipeline using ORFik. It is important you read all the other vignettes before this one, since functions will not be explained here in detail.

# Pipeline

This pipeline will shows steps needed to analyse Ribo-seq from:

- Tsvetkov et al, 2020 (Saccharomyces cerevisiae, 8 GB ram requirement, 6 samples (6 Ribo-seq, 6 RNA-seq))

The following steps are done:

1. Define directory paths
2. Download Ribo-seq & RNA-seq data from SRA (subset to 2 million reads per library)
3. Download genome annotation and contaminants
4. Trim & Align data
5. Make ORFik experiment
6. QC
7. Heatmaps
8. Count table analysis: TE tables
9. Differentially translated genes
10. Peak analysis
11. Feature table
12. Gene plotting (Advanced)
13. uORF analysis (Advanced)

    #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
    # 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!)