Transposable elements (TEs) are major components of eukaryotic genomes. However, the extent of their impact on genome evolution, function, and disease remain a matter of intense interrogation. The rise of genomics and large-scale functional assays has shed new light on the multi-faceted activities of TEs and implies that they should no longer be marginalized. Here, we introduce the fundamental properties of TEs and their complex interactions with their cellular environment, which are crucial to understanding their impact and manifold consequences for organismal biology. While we draw examples primarily from mammalian systems, the core concepts outlined here are relevant to a broad range of organisms.

Motivation: Most RNA-seq data analysis software packages are not designed to handle the complexities involved in properly apportioning short sequencing reads to highly repetitive regions of the genome. These regions are often occupied by transposable elements (TEs), which make up between 20 and 80% of eukaryotic genomes. They can contribute a substantial portion of transcriptomic and genomic sequence reads, but are typically ignored in most analyses. Results: Here, we present a method and software package for including both gene- and TE-associated ambiguously mapped reads in differential expression analysis. Our method shows improved recovery of TE transcripts over other published expression analysis methods, in both synthetic data and qPCR/NanoString-validated published datasets. Availability and implementation: The source code, associated GTF files for TE annotation, and testing data are freely available at http://hammelllab.labsites.cshl.edu/software. Contact:mhammell@cshl.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons. While several pathogenic mutations have been identified, the vast majority of ALS cases have no family history of disease. Thus, for most ALS cases, the disease may be a product of multiple pathways contributing to varying degrees in each patient. Using machine learning algorithms, we stratify the transcriptomes of 148 ALS postmortem cortex samples into three distinct molecular subtypes. The largest cluster, identified in 61% of patient samples, displays hallmarks of oxidative and proteotoxic stress. Another 19% of the samples shows predominant signatures of glial activation. Finally, a third group (20%) exhibits high levels of retrotransposon expression and signatures of TARDBP/TDP-43 dysfunction. We further demonstrate that TDP-43 (1) directly binds a subset of retrotransposon transcripts and contributes to their silencing in vitro, and (2) pathological TDP-43 aggregation correlates with retrotransposon de-silencing in vivo.

Peer reviewed: NoNRC publication: Ye