## ----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$gene_id # FALSE
#
# #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
# # Feature table (From WT rep 1)
# #¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
# cds <- loadRegion(df.rfp, "cds")
# cds <- ORFik:::removeMetaCols(cds) # Dont need them
# cds <- cds[filterTranscripts(df.rfp)] # Filter to sane transcripts (annotation is not perfect)
# dt <- computeFeatures(cds,
# RFP = fimport(filepath(df.rfp[6,], "pshifted")),
# RNA = fimport(filepath(df.rna[6,], "ofst")), Gtf = df.rfp,
# grl.is.sorted = TRUE, faFile = df.rfp,
# weight.RFP = "score", weight.RNA = "score",
# riboStart = 21, uorfFeatures = FALSE)
# # The features of significant DTEGs.
# dt[names(cds) %in% significant_genes$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]
#