Ribo-seq pipeline
Package
ORFik 1.16.3
Contents
1Introduction
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.
2Pipeline
This pipeline will shows steps needed to analyse Ribo-seq from Alexaki et al, 2020
The following steps are done:
- Define directory paths
- Download Ribo-seq & RNA-seq data from SRA (subset to 2 million reads per library)
- Download genome annotation and contaminants
- Trim & Align data
- Make ORFik experiment
- QC
- Heatmaps
- Count table analysis: TE tables
- Differentially translated genes
- Peak analysis
- Feature table
- Gene plotting (Advanced)
- 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)
#¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤¤#
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), "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), "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]