bioRxivBioinformaticsPreprint
Integrated genotyping, annotation and epigenetic characterization of transposable elements with GraffiTE
Clément GoubertiD, Rose Laflamme, Robert Eveleigh, Ioannis Ragoussis, Travis WheeleriD, Guillaume LettreiD, Guillaume BourqueiD, Cristian GrozaiD
Abstract
Genotyping, annotating and characterizing TE variation leaped forward with long-read sequencing (LRS) and de novo assembly. LRS techniques also reveal CpG methylation, which enables measuring the impact of TEs on epigenomes and sheds light on the role of TEs as regulatory sequences. Yet, TE genotyping tools discard the valuable allele-specific epigenetic information. We introduce GraffiTE v1.1, the first Nextflow pipeline that genotypes SVs, annotates polymorphic mobile elements (MEI), and maps epigenetic data to TEs using pangenome graphs in one command. GraffiTE v1.1 is designed to map any epigenetic information that is based on LRS stencils, including Fiber-seq. We demonstrate the capabilities of GraffiTE v1.1 by analyzing genomes and methylomes obtained from 20 Quebec residents of French-Canadian descent. In these genomes, TE annotation yields 4,908 Alu, 704 LINE1, 112 SVA and 21 HERVK (HML-2) distinct polymorphic mobile elements (MEIs). We also identify 10 fixed HERVK insertions that vary between solo LTR and provirus, or in proviral copy number. We determine that these MEIs contribute 144,748 additional CpGs not present in the reference. From this, we identify 480 Alus, 67 LINE1s, 12 SVAs and 2 HERVKs in hypomethylated states. We note that Alus and SVAs are more methylated than other LINE1 and HERVK. Lastly, MEIs dating reported a mean of age 1.08 Mya (median: 640 kya) and beta regression identified a moderate demethylating effect of age on MEIs (effect: -0.069 log-odds per log10 years, P < 6.08 x 10-19). Our results show that the methylation level of a LINE1 MEI is expected to decrease from 86.4% to 80.8% in the first Mya. In conclusion, GraffiTE v1.1 genotypes SVs, annotates MEI, and characterizes their epigenetic state at the scale of populations by leveraging the full potential of LRS. While we showcase its application in human data, the pipeline can be deployed in any model for which a TE library is available.
- organism: human
- assay: long-read sequencing
- research focus: cell type annotation
- research focus: chromatin & epigenetic regulation
- approach: deep learning
- approach: statistical model
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