McClintock: An Integrated Pipeline for Detecting Transposable Element Insertions in Whole-Genome Shotgun Sequencing Data

Abstract

Transposable element (TE) insertions are among the most challenging types of variants to detect in genomic data because of their repetitive nature and complex mechanisms of replication . Nevertheless, the recent availability of large resequencing data sets has spurred the development of many new methods to detect TE insertions in whole-genome shotgun sequences. Here we report an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote, Saccharomyces cerevisiae. We find substantial variation among McClintock component methods in their ability to detect nonreference TEs in the yeast genome, but show that nonreference TEs at nearly all biologically realistic locations can be detected in simulated data by combining multiple methods that use split-read and read-pair evidence. In general, our results reveal that split-read methods detect fewer nonreference TE insertions than read-pair methods, but generally have much higher positional accuracy. Analysis of a large sample of real yeast genomes reveals that most McClintock component methods can recover known aspects of TE biology in yeast such as the transpositional activity status of families, target preferences, and target site duplication structure, albeit with varying levels of accuracy. Our work provides a general framework for integrating and analyzing results from multiple TE detection methods, as well as useful guidance for researchers studying TEs in yeast resequencing data.

Trapping a somatic endogenous retrovirus into a germline piRNA cluster immunizes the germline against further invasion

“…We first searched for new ZAM insertions in euchromatin within the RevI-H2 genome using the McClintock pipeline [28], which identified seven ZAM insertions on the X-chromosome, including the known ZAM insertion in the white locus at X: 2,799,672..2,799,675. None of these Xchromosome insertions are found in known piRNA clusters.…”

Asexual reproduction drives the reduction of transposable element load

“…To detect specific reference (present in the reference genome) and non-reference TE insertions in all samples, the McClintock pipeline was utilized (Nelson et al, 2017) . This pipeline combines six different, benchmarked programs in a standardized fashion.…”

Phylogenetic Conflict in Bears Identified by Automated Discovery of Transposable Element Insertions in Low Coverage Genomes

“…While TE calls from Mobster and RetroSeq were almost concordant, still overlapping calls were used to increase the reliability of the calls. For TE calling, integration of multiple callers is recognized as an appropriate strategy to enhance the consistency of TE predictions (Lin et al 2015;Nelson et al 2016), and this functionality is implemented in TeddyPi for both, Ref+ (Table 1) is higher than the estimated sensitivity of RetroSeq for 10X whole genome sequencing data (Keane et al 2013). The reliability of TeddyPi is equally as estimates from Mobster analyses of high-quality human data .…”

“…We developed the TeddyPi (TE detection and discovery for phylogenetic inference) pipeline 4 64 to process data from TE and SV callers. TeddyPi pursues the idea of integrating different TE callers (Lin et al 2015;Nelson et al 2016) and extends it to routinely integrate TE insertion datasets from multiple samples to track integrations of TEs in orthologous loci and to create presence/absence tables for phylogenetic inference. The general effectiveness of TeddyPi and its reliability for extracting TE insertions from low-coverage genomes in non-model organisms was evaluated using all bears of the Ursine subfamily and the monotypic Tremarctinae.…”

“…TeddyPi is open source and can be accessed on https://github.com/mobilegenome/teddypi. Easy configuration and a modular architecture makes it convenient to adapt TeddyPi to process data from a broad range of TE/SV callers or other integration pipelines such as SVMerge or McClintock(Wong et al 2010;Nelson et al 2016). TeddyPi can be applied to any group of organisms where accurate TE/SV calling is feasible.…”