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Transposable elements (TEs) are ubiquitous in genomes and many remain active. TEs comprise an important fraction of the transcriptomes with potential effects on the host genome, either by generating deleterious mutations or promoting evolutionary novelties. However, their functional study is limited by the difficulty in their identification and quantification, particularly in non-model organisms.

We developed a new pipeline [explore active transposable elements (ExplorATE)] implemented in R and bash that allows the quantification of active TEs in both model and non-model organisms. ExplorATE creates TE-specific indexes and uses the Selective Alignment (SA) to filter out co-transcribed transposons within genes based on alignment scores. Moreover, our software incorporates a Wicker-like criteria to refine a set of target TEs and avoid spurious mapping. Based on simulated and real data, we show that the SA strategy adopted by ExplorATE achieved better estimates of non-co-transcribed elements than other available alignment-based or mapping-based software. ExplorATE results showed high congruence with alignment-based tools with and without a reference genome, yet ExplorATE required less execution time. Likewise, ExplorATE expands and complements most previous TE analyses by incorporating the co-transcription and multi-mapping effects during quantification, and provides a seamless integration with other downstream tools within the R environment.

Source code is available at https://github.com/FemeniasM/ExplorATEproject and https://github.com/FemeniasM/ExplorATE_shell_script. Data available on request.

Supplementary data are available at Bioinformatics online.

 

Many organisms have evolved adaptations to increase the odds of survival of their offspring. Parental care has evolved several times in animals including ectotherms. In amphibians, ~ 10% of species exhibit parental care. Among these, poison frogs (Dendrobatidae) are well-known for their extensive care, which includes egg guarding, larval transport, and specialized tadpole provisioning with trophic eggs. At least one third of dendrobatids displaying aposematism by exhibiting warning coloration that informs potential predators about the presence of defensive skin toxins. Aposematism has a central role in poison frog diversification, including diet specialization, and visual and acoustic communication; and it is thought to have impacted their reproductive biology as well. We tested the latter association using multivariate phylogenetic methods at the family level. Our results show complex relationships between aposematism and certain aspects of the reproductive biology in dendrobatids. In particular, aposematic species tend to use more specialized tadpole-deposition sites, such as phytotelmata, and ferry fewer tadpoles than non-aposematic species. We propose that aposematism may have facilitated the diversification of microhabitat use in dendrobatids in the context of reproduction. Furthermore, the use of resource-limited tadpole-deposition environments may have evolved in tandem with an optimal reproductive strategy characterized by few offspring, biparental care, and female provisioning of food in the form of unfertilized eggs. We also found that in phytotelm-breeders, the rate of transition from cryptic to aposematic phenotype is 17 to 19 times higher than vice versa. Therefore, we infer that the aposematism in dendrobatids might serve as an umbrella trait for the evolution and maintenance of their complex offspring-caring activities.

 

Abstract Given the rapid loss of biodiversity as consequence of climate change, greater knowledge of ecophysiological and natural history traits are crucial to determine which environmental factors induce stress and drive the decline of threatened species. Liolaemus montanezi (Liolaemidae), a xeric-adapted lizard occurring only in a small geographic range in west-central Argentina, constitutes an excellent model for studies on the threats of climate change on such microendemic species. We describe field data on activity patterns, use of microhabitat, behavioral thermoregulation, and physiology to produce species distribution models (SDMs) based on climate and ecophysiological data. Liolaemus montanezi inhabits a thermally harsh environment which remarkably impacts their activity and thermoregulation. The species shows a daily bimodal pattern of activity and mostly occupies shaded microenvironments. Although the individuals thermoregulate at body temperatures below their thermal preference they avoid high-temperature microenvironments probably to avoid overheating. The population currently persists because of the important role of the habitat physiognomy and not because of niche tracking, seemingly prevented by major rivers that form boundaries of their geographic range. We found evidence of habitat opportunities in the current range and adjacent areas that will likely remain suitable to the year 2070, reinforcing the relevance of the river floodplain for the species’ avoidance of extinction. 

Transcriptomic reconstructions without reference (i.e., de novo) are common for data samples derived from non-model biological systems. These assemblies involve massive parallel short read sequence reconstructions from experiments, but they usually employ ad-hoc bioinformatic workflows that exhibit limited standardization and customization. The increasing number of transcriptome assembly software continues to provide little room for standardization which is exacerbated by the lack of studies on modularity that compare the effects of assembler synergy. We developed a customizable management workflow for de novo transcriptomics that includes modular units for short read cleaning, assembly, validation, annotation, and expression analysis by connecting twenty-five individual bioinformatic tools. With our software tool, we were able to compare the assessment scores based on 129 distinct single-, bi- and tri-assembler combinations with diverse k-mer size selections. Our results demonstrate a drastic increase in the quality of transcriptome assemblies with bi- and tri- assembler combinations. We aim for our software to improve de novo transcriptome reconstructions for the ever-growing landscape of RNA-seq data derived from non-model systems. We offer guidance to ensure the most complete transcriptomic reconstructions via the inclusion of modular multi-assembly software controlled from a single master console.