Carlos Menor

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

The puna/páramo grasslands span across the highest altitudes of the tropical Andes, and their ecosystem dynamics are still poorly understood. In this study we examined the above-ground biomass and developed species specific and multispecies power-law allometric equations for four tussock grass species in Peruvian high altitude grasslands, considering maximum height (hmax), elliptical crown area and elliptical basal area. Although these predictors are commonly used among allometric literature, they have not previously been used for estimating puna grassland biomass. Total above-ground biomass was estimated to be of 6.7 ± 0.2 Mg ha−1 (3.35 ± 0.1 Mg C ha−1). All allometric relationships fitted to similar power-law models, with basal area and crown area as the most influential predictors, although the fit improved when tussock maximum height was included in the model. Multispecies allometries gave better fits than the other species-specific equations, but the best equation should be used depending on the species composition of the target grassland. These allometric equations provide an useful approach for measuring above-ground biomass and productivity in high-altitude Andean grasslands, where destructive sampling can be challenging and difficult because of the remoteness of the area. These equations can be also applicable for establishing above-ground reference levels before the adoption of carbon compensation mechanisms or grassland management policies, as well as for measuring the impact of land use changes in Andean ecosystems.

Abstract The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.