Local assembly of long reads enables phylogenomics of transposable elements in a polyploid cell line

Abstract

Animal cell lines often undergo extreme genome restructuring events, including polyploidy and segmental aneuploidy that can impede de novo whole-genome assembly (WGA). In some species like Drosophila, cell lines also exhibit massive proliferation of transposable elements (TEs). To better understand the role of transposition during animal cell culture, we sequenced the genome of the tetraploid Drosophila S2R+ cell line using long-read and linked-read technologies. WGAs for S2R+ were highly fragmented and generated variable estimates of TE content across sequencing and assembly technologies. We therefore developed a novel WGA-independent bioinformatics method called TELR that identifies, locally assembles, and estimates allele frequency of TEs from long-read sequence data (https://github.com/bergmanlab/telr). Application of TELR to a ∼130x PacBio dataset for S2R+ revealed many haplotype-specific TE insertions that arose by transposition after initial cell line establishment and subsequent tetraploidization. Local assemblies from TELR also allowed phylogenetic analysis of paralogous TEs, which revealed that proliferation of TE families in vitro can be driven by single or multiple source lineages. Our work provides a model for the analysis of TEs in complex heterozygous or polyploid genomes that are recalcitrant to WGA and yields new insights into the mechanisms of genome evolution in animal cell culture.

TrEMOLO: Accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches

“…We compared the results obtained by TrEMOLO, TELR (24) and TLDR (25), two recently developed computational tools dedicated to TE detection using LR sequencing data. The rationale of these tools is similar to the one used with short-read sequencing data: they use LR data mapped to a reference sequence, through a BAM file for TLDR (25) or directly from the mapping tool output for TELR.…”

“…We mapped this simulated reads dataset to the unmasked G0-F100 genome using TELR (24), TLDR (25) and the TrEMOLO OUTSIDER module (here called “TrEMOLO_NA”, see Methods) with minimap2 (30). We also launched the whole TrEMOLO pipeline (both OUTSIDER and INSIDER modules) with the G0-F100 genome as reference and as the assembled genome, we used the G0-F100 genome with the 100 highly frequent TE insertions.…”

“…The same parameters were used for TrEMOLO and TrEMOLO_NA. For TELR (v1 - August 2021; (24)), the settings were --minimap2_family --assembler flye -x ont --aligner minimap2.…”

“…However, the longer read length is counterbalanced by a lower sequencing quality (still improving) compared with short-read sequencing and fewer dedicated tools for TE detection. The current methods to detect TE variations using LR sequencing data (and also most of the short-read sequencing data-based tools) generally rely on the presence/absence of TE insertions annotated in a reference genome ( e.g ., LorTE (23)), or on the detection of abnormally mapped reads upon the reference genome that can be linked to a TE ( e.g ., TELR (24)). However, some tools also allow the partial detection, assembly and annotation of non-reference TE insertions (e.g.…”

Reproducible evaluation of transposable element detectors with McClintock 2 guides accurate inference of Ty insertion patterns in yeast

“…The original McClintock system automated installation of these six “component” TE detection methods, provided a common interface to run all components, reduced the number of shared input files, and generated a standard set of output files [15]. Since its initial development, the McClintock system has been used to support detection of TE insertions and enable biological discoveries in a variety of organisms and biological contexts [15, 16, 17, 18, 19, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44] and to facilitate comparative evaluation of multiple TE detectors [15, 16, 45].…”