quality.filter: Pipeline to eliminate false positive predictions of...
In HajkD/LTRpred: De novo functional annotation of retrotransposons

quality.filter

R Documentation

Pipeline to eliminate false positive predictions of retrotransposons

Description

This function takes an LTRpred output table as input and eliminates false positive predictions.

Usage

quality.filter(pred, sim, n.orfs, strategy = "default")

Arguments

`pred`	`LTRpred.tbl` generated with `LTRpred`
`sim`	LTR similarity threshold. Only putative LTR transposons that fulfill this LTR similarity threshold will be retained.
`n.orfs`	minimum number of ORFs detected in the putative LTR transposon.
`strategy`	quality filter strategy. Options are `strategy = "default"` : see section `Quality Control` `strategy = "stringent"` : in addition to filter criteria specified in section `Quality Control`, the filter criteria `!is.na(protein_domain)) \| (dfam_target_name != "unknown")` is applied

Details

Quality Control

ltr.similarity: Minimum similarity between LTRs. All TEs not matching this criteria are discarded.
n.orfs: minimum number of Open Reading Frames that must be found between the LTRs. All TEs not matching this criteria are discarded.
PBS or Protein Match: elements must either have a predicted Primer Binding Site or a protein match of at least one protein (Gag, Pol, Rve, ...) between their LTRs. All TEs not matching this criteria are discarded.
The relative number of N's (= nucleotide not known) in TE <= 0.1. The relative number of N's is computed as follows: absolute number of N's in TE / width of TE.

Value

A quality filtered LTRpred.tbl.

Author(s)

Hajk-Georg Drost

Examples

# example prediction file generated by LTRpred 
pred.file <- system.file("Athaliana_TAIR10_chr_all_LTRpred_DataSheet.csv", package = "LTRpred")
# read LTRpred generated prediction file (data sheet)
pred <- read.ltrpred(pred.file)
# apply quality filter
pred <- quality.filter(pred, sim = 70, n.orfs = 1)

HajkD/LTRpred documentation built on April 22, 2022, 4:35 p.m.