LTRpred(ict)

A bioinformatics pipeline for meta-genome scale functional de novo annotation of LTR retrotransposons

Due to their enormous contribution to genome structure and genome evolution transposable elements allow us to study fundamental mechanisms of phenotypic adaptation, diversification, and evolution. In particular, understanding the recognition and regulation of transposable elements by the genetic regulatory machinery will enable us to systematically identify the key players and key processes that enable niche adaptation and species diversification on the genetic level.

The LTRpred pipeline aims to provide an integrated software framework to predict potentially functional LTR transposons in any genomic sequence of interest. First, LTRpred retrieves de novo annotations of retrotransposons via LTRharvest and LTRdigest and second efficiently screens, filters and annotates those predictions for potentially functional elements.

LTR transposons have the capacity to move to new sites in genomes through a copy-and-paste mechanism and by doing so are able to contribute generatively to genome evolution and environmental sensing on the genetic level. Hence, predicting the presence of LTR transposons within genomes as well as their capacity to perform this copy-and-paste strategy enables us to quantify the extent to which transposons shape the adaptation and evolution of life in general.

In particular the following analyses can be performed with LTRpred:

De novo prediction and annotation

de novo prediction of LTR retrotransposons (nested, overlapping, or pure template) using LTRharvest and LTRdigest
annotation of predicted LTR retrotransposons using Dfam or Repbase as reference
solo LTR prediction based on specialized BLAST searches
LTR retrotransposons family clustering using vsearch
open reading frame prediction in LTR retrotransposons using usearch
age estimation of predicted LTR retrotransposons in Mya (not implemented yet, but soon to come..)
CHH, CHG, CG, ... content quantification in predicted LTR retrotransposons
filtering for (potentially) functional LTR retrotransposons
quality assesment of input genomes used to predict LTR retrotransposons

Meta-Genomics Analyses

run LTRpred on entire kingdoms of life using only one command (see ?LTRpred.meta)
perform meta genomics studies customized for LTR retrotransposons
cluster LTR retrotransposons within and between species
quantify the diversity space of LTR retrotransposons for entire kingdoms of life

Install

# install the current version of LTRpred on your system
source("http://bioconductor.org/biocLite.R")
biocLite("devtools")
biocLite("HajkD/LTRpred")

Discussions and Bug Reports

I would be very happy to learn more about potential improvements of the concepts and functions provided in this package.

Furthermore, in case you find some bugs or need additional (more flexible) functionality of parts of this package, please let me know:

https://github.com/HajkD/LTRpred/issues

NEWS

The current status of the package as well as a detailed history of the functionality of each version of LTRpred can be found in the NEWS section.

Tutorials

This tutorial introduces users to LTRpred:

Introduction to LTRpred

Users can also read the tutorials within (RStudio) :

library(LTRpred)
browseVignettes("LTRpred")

In the LTRpred framework users can find:

De Novo Annotation Functions:

LTRpred() : Major pipeline to predict LTR retrotransposons in a given genome
LTRpred.meta : Perform Meta-Analyses with LTRpred
meta.summarize() : Summarize (concatenate) all predictions of a LTRpred.meta() run
meta.apply() : Apply functions to meta data generated by LTRpred()
LTRharvest() : Run LTRharvest to predict putative LTR Retrotransposons
LTRdigest() : Run LTRdigest to predict putative LTR Retrotransposons

Sequence Clustering and Similarity Computations

CLUSTpred() : Cluster Sequences with VSEARCH
cluster.members() : Select members of a specific cluster
clust2fasta() : Export sequences of TEs belonging to the same cluster to fasta files
AllPairwiseAlign() : Compute all pairwise (global) alignments with VSEARCH
filter.uc() : Filter for cluster members
SimMatAbundance() : Compute histogram shape similarity between species

LTR Copy Number Estimation

ltr.cn() : Detect solo LTR copies of predicted LTR transposons
cn2bed() : Write copy number estimation results to BED file format.

Filter Functions

filter.jumpers() : Detect LTR retrotransposons that are potential jumpers
tidy.datasheet() : Select most important columns of 'LTRpred' output for further analytics

Import the Output Files of the Prediction Tools:

read.prediction() : Import the output of LTRharvest or LTRdigest
read.tabout() : Import information sheet returned by LTRdigest
read.orfs() : Read output of ORFpred()
read.seqs() : Import sequences of predicted LTR transposons
read.ltrpred() : Import the data sheet file generated by LTRpred()
read.uc() : Read file in USEARCH cluster format
read.blast6out() : Read file in blast6out format generated by USEARCH or VSEARCH

Export the Output Files of the Prediction Tools:

pred2bed() : Format LTR prediction data to BED file format
pred2fasta() : Save the sequence of the predicted LTR Transposons in a fasta file
pred2gff() : Format LTR prediction data to GFF3 file format
pred2annotation() : Match LTRharvest, LTRdigest, or LTRpred prediction with a given annotation file in GFF3 format
pred2csv() : Format LTR prediction data to CSV file format

Analytics Tools:

ORFpred() : Open Reading Frame prediction in putative LTR transposons

Annotation and Validation:

dfam.query() : Annotation of de novo predicted LTR transposons via Dfam searches
read.dfam() : Import Dfam Query Output
repbase.clean() : Clean the initial Repbase database for BLAST
repbase.query() : Query the RepBase to annotate putative LTRs
repbase.filter() : Filter the Repbase query output

Methylation Context Estimation

motif.count() : Low level function to detect motifs in strings

Visualization Framework

plot_ltrsim_individual() : Plot the age distribution of predicted LTR transposons
plot_ltrwidth_individual() : Plot the width distribution of putative LTR transposons or LTRs for individual species
plot_ltrwidth_species() : Plot the width distribution of putative LTR transposons or LTRs for all species
plot_ltrwidth_kingdom() : Plot the width distribution of putative LTR transposons or LTRs for all kingdoms
plot_copynumber_individual() : Plot the copy number distribution of putative LTR transposons or LTRs for individual species
plot_copynumber_species() : Plot the copy number distribution of putative LTR transposons or LTRs for all species
plot_copynumber_kingdom() : Plot the copy number distribution of putative LTR transposons or LTRs for all kingdoms
plotLTRRange() : Plot Genomic Ranges of putative LTR transposons
PlotSimCount() : Plot LTR Similarity vs. predicted LTR count
plotSize() : Plot Genome size vs. LTR transposon count
plotSizeJumpers() : Plot Genome size vs. LTR transposon count for jumpers
plotFamily() : Visualize the Superfamily distribution of predicted LTR retrotransposons
plotDomain() : Visualize the Protein Domain distribution of predicted LTR retrotransposons
plotCN() : Plot correlation between LTR copy number and methylation context
plotCluster() : Plot correlation between Cluster Number and any other variable
PlotInterSpeciesCluster() : Plot inter species similarity between TEs (for a specific cluster)
PlotMainInterSpeciesCluster() : Plot inter species similarity between TEs (for the top n clusters)

Minor helper functions

bcolor() : Beautiful colors for plots
file.move() : Move folders from one location to another
get.pred.filenames() : Retrieve file names of files genereated by LTRpred
get.seqs() : Quickly retrieve the sequences of a 'Biostrings' object
ws.wrap.path() : Wrap whitespace in paths
rename.fasta() : rename.fasta

Acknowledgement

I would like to thank the Paszkowski team for incredible support and motivating discussions that led to the realization of this project.

gogleva / ltrpred Goto Github PK

ltrpred's Introduction