# Ribo-seq pipeline (Yeast)

Haakon Tjeldnes & Kornel Labun

#### 15 July 2025

#### Package

ORFik 1.29.7

# Contents

# 1 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.

# 2 Pipeline

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

- Alexaki et al, 2020 (Homo Sapiens, 32GB ram requirement, 6 samples)

The human genome is large, and this pipeline will fail for all computers with < 32GB ram (we advice 64GB if you are running other things). For those with 8 or 16GB ram computers run the Yeast Ribo-seq pipeline vignette only!

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 = "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)
    #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
    # This step will cache, so it will not rerun unless you set force = TRUE.
    annotation <- getGenomeAndAnnotation(
      organism = organism,
      genome = TRUE, GTF = TRUE,
      phix = TRUE, ncRNA = TRUE, tRNA = TRUE, rRNA = TRUE,
      output.dir = conf["ref"], optimize = 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 >= 64GB ram
    index <- STAR.index(annotation, 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 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)
    # 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")
    #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
    # 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 <- c("ENST00000315377", "ENST00000311672", "ENST00000578186", "ENST00000549017",
                   "ENST00000644684", "ENST00000436146") # Use 6 transcript subset
    # 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)) # 3 cds, 5 NTT, 3 uORFs
    table[ensembl_tx_name == "ENST00000315377" & predicted == TRUE,]
    # this tx has 3 NTTs predicted, 2 in RFP_CO_r1 and 1 in RFP_CO_r2
    print(nrow(table[type %in% c("annotated") & predicted == TRUE ])) # 86 cds predicted
    print(nrow(table[type %in% c("uORF", "uoORF") & predicted == TRUE ])) # 3 uORFs predicted
    # 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!)
    #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
    # uORF analysis (deprecated, but take a look if you want)
    # (using the extension package to ORFik: uORFomePipe)
    # Will create a mysql database + bed12 file of uORFs with color codes + plots + files with results
    #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
    remotes::install_github("Roleren/uORFomePipe", dependencies = TRUE) # Then restart R session!
    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]