kozakSequenceScore {ORFik} | R Documentation

## Make a score for each ORFs start region by proximity to Kozak

### Description

The closer the sequence is to the Kozak sequence the higher the score, based on the experimental pwms from article referenced. Minimum score is 0 (worst correlation), max is 1 (the best base per column was chosen).

### Usage

    kozakSequenceScore(grl, tx, faFile, species = "human", include.N = FALSE)

### Arguments

`grl` | a `GRangesList` grouped by ORF
`tx` | a `GRangesList`, the reference area for ORFs, each ORF must have a coresponding tx.
`faFile` | `FaFile`, BSgenome, fasta/index file path or an ORFik `experiment`. This file is usually used to find the transcript sequences from some GRangesList.
`species` | ("human"), which species to use, currently supports human (Homo sapiens), zebrafish (Danio rerio) and mouse (Mus musculus). Both scientific or common name for these species will work. You can also specify a pfm for your own species. Syntax of pfm is an rectangular integer matrix, where all columns must sum to the same value, normally 100. See example for more information. Rows are in order: c("A", "C", "G", "T")
`include.N` | logical (F), if TRUE, allow N bases to be counted as hits, score will be average of the other bases. If True, N bases will be added to pfm, automaticly, so dont include them if you make your own pfm.

### Details

Ranges that does not have minimum 15 length (the kozak requirement as a sliding window of size 15 around grl start), will be set to score 0. Since they should not have the posibility to make an efficient ribosome binding.

### Value

a numeric vector with values between 0 and 1

an integer vector, one score per orf

### References

doi: https://doi.org/10.1371/journal.pone.0108475

### See Also

Other features: `computeFeaturesCage()`, `computeFeatures()`, `countOverlapsW()`, `disengagementScore()`, `distToCds()`, `distToTSS()`, `entropy()`, `floss()`, `fpkm_calc()`, `fpkm()`, `fractionLength()`, `initiationScore()`, `insideOutsideORF()`, `isInFrame()`, `isOverlapping()`, `orfScore()`, `rankOrder()`, `ribosomeReleaseScore()`, `ribosomeStallingScore()`, `startRegionCoverage()`, `startRegion()`, `stopRegion()`, `subsetCoverage()`, `translationalEff()`

### Examples

    # Usually the ORFs are found in orfik, which makes names for you etc.
    # Here we make an example from scratch
    seqName <- "Chromosome"
    ORF1 <- GRanges(seqnames = seqName,
                       ranges = IRanges(c(1007, 1096), width = 60),
                       strand = c("+", "+"))
    ORF2 <- GRanges(seqnames = seqName,
                        ranges = IRanges(c(400, 100), width = 30),
                        strand = c("-", "-"))
    ORFs <- GRangesList(tx1 = ORF1, tx2 = ORF2)
    ORFs <- makeORFNames(ORFs) # need ORF names
    tx <- extendLeaders(ORFs, 100)
    # get faFile for sequences
    faFile <- FaFile(system.file("extdata/Danio_rerio_sample", "genome_dummy.fasta", package = "ORFik"))
    kozakSequenceScore(ORFs, tx, faFile)
    # For more details see vignettes.

[Package ORFik version 1.14.7 Index]