Clément Goubert @clementgoubert@qoto.org

dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. I...

Author summary Transposable elements (TEs) are engines of evolution over short and long evolutionary time scales and have played crucial roles in pathogen evolution. The impacts of TEs are multifaceted, ranging from creating adaptive sequence variants, gene disruptions, chromosomal rearrangements or even triggers of genome expansions. As a defense, pathogen genomes have evolved sophisticated mechanisms to silence or mutate TEs. Pathogens have also benefited from TEs thanks to altered virulence genes and increased antifungal resistance. How TEs cope with genomic defenses and expand in genomes (i.e., cause TE bursts) remains poorly understood though. We analyzed over a dozen high-quality genomes of a fungal wheat pathogen species, which has recently experienced TE reactivations. We reconstructed the evolutionary history of many TEs by building phylogenetic trees. Using this approach, we identified "invasion routes", i.e., tracking TE copies that constitute the most likely ancestors of renewed activity of TEs (i.e. a bursts). Our work showed that specific features, in particular the proximity to genes, were likely important drivers leading the reactivation of TEs.

Author summary Functional enrichment analysis is a commonly used technique to identify trends in large scale biological datasets. In biomedicine, functional enrichment analysis of gene expression data is frequently applied to identify disease and drug mechanisms. While enrichment tests were once primarily conducted with complicated computer scripts, web-based tools are becoming more widely used. Users can paste a list of genes into a website and receive enrichment results in a matter of seconds. Despite the popularity of these tools, there are concerns that statistical problems and incomplete reporting are compromising research quality. In this article, we conducted a systematic examination of published enrichment analyses and assessed whether (i) any statistical flaws were present and (ii) sufficient methodological detail is provided such that the study could be replicated. We found that lack of methodological detail and errors in statistical analysis were widespread, which undermines the reliability and reproducibility of these research articles. A set of best practices is urgently needed to raise the quality of published work.

I want to talk today about a methodological issue in #genomics research that has been around a long time but is still a major problem. The reason is that today I reviewed another manuscript that has this exact problem.

Background: Many computational methods have been developed to detect non-reference transposable element (TE) insertions using short-read whole genome sequencing data. The diversity and complexity of such methods often present challenges to new users seeking to reproducibly install, execute or evaluate multiple TE insertion detectors. Results: We previously developed the McClintock meta-pipeline to facilitate the installation, execution, and evaluation of six first-generation short-read TE detectors. Here, we report a completely re-implemented version of McClintock written in Python using Snakemake and Conda that improves its installation, error handling, speed, stability, and extensibility. McClintock 2 now includes 12 short-read TE detectors, auxiliary pre-processing and analysis modules, interactive HTML reports, and a simulation framework to reproducibly evaluate the accuracy of component TE detectors. When applied to the model microbial eukaryote Saccharomyces cerevisiae, we find substantial variation in the ability of McClintock 2 components to identify the precise locations of non-reference TE insertions, with RelocaTE2 showing the highest recall and precision in simulated data. We find that RelocaTE2, TEMP, TEMP2 and TEBreak provide a consistent and biologically meaningful view of non-reference TE insertions in a species-wide panel of ∼1000 yeast genomes, as evaluated by coverage-based abundance estimates and expected patterns of tRNA promoter targeting. Finally, we show that best-in-class predictors for yeast have sufficient resolution to reveal a dyad pattern of integration in nucleosome-bound regions upstream of yeast tRNA genes for Ty1, Ty2, and Ty4, allowing us to extend knowledge about fine-scale target preferences first revealed experimentally for Ty1 to natural insertions and related copia-superfamily retrotransposons in yeast. Conclusion: McClintock (https://github.com/bergmanlab/mcclintock/) provides a user-friendly pipeline for the identification of TEs in short-read WGS data using multiple TE detectors, which should benefit researchers studying TE insertion variation in a wide range of different organisms. Application of the improved McClintock system to simulated and empirical yeast genome data reveals best-in-class methods and novel biological insights for one of the most widely-studied model eukaryotes and provides a paradigm for evaluating and selecting non-reference TE detectors for other species. ### Competing Interest Statement The authors have declared no competing interest.

“https://t.co/B2L9m3ebrz #computationalbiology #summerinternship2023 #c3genomics #C3GMTL #bioinformatics #genomics #summerinternship”