Biology subjects

Mestre-Tomas, J.

Publications and source records attributed to Mestre-Tomas, J..

5 recordsLinked to original sources

bioRxiv2026

Stop guessing thinning distances: a data-driven framework for spatial thinning parametrization for ecological modelling

Spatially heterogeneous sampling and uneven publication effort in biodiversity repositories limit ecological niche characterization and habitat suitability modelling, affecting downstream inferences at regional and global scales. Spatial thinning offers a widely used strategy to mitigate these biases due to its simplicity and scalability. However, researchers typically select thinning parameters using heuristic or arbitrary criteria, often independent of species-specific characteristics, which introduces an additional, largely unquantified source of potential error. Here, we propose a data-driven framework for spatial thinning parameterization that replaces heuristic decision-making with principled, reproducible criteria. We define optimality as the stability of thermal niche estimates derived from empirical data, rather than as an agreement with a known true distribution. The framework integrates two complementary approaches. The Last Stable Thinning Parameterization (LS-TP) identifies the largest thinning intensity that preserves the stability of species-specific niche descriptors. Biogeographic-size Dependent Thinning Parameterization (BD-TP) links thinning intensity to species' geographic range size and scales spatial filtering across taxa with different distributional properties. Using seagrasses as a model system, we evaluated the framework across a global compilation of more than 100,000 occurrence records. Our results showed that optimal thinning parameters varied systematically with species range size and that uniform thinning approaches led to either excessive information loss or reduction of spatial clustering. Furthermore, the framework identified species for which available data did not support stable niche characterization, as these species failed to meet stability criteria even at the smallest thinning parameters. We recommend explicitly acknowledging this limitation in future studies and prioritizing increased sampling effort for such undersampled taxa. Our approaches provide a first step toward explicit, data-driven spatial thinning parameterization, with direct implications for ecological niche modelling, biodiversity assessment, and climate-driven distribution analyses.

ecology↗

bioRxiv2025

To join or not to join: handling biological replicates in long-read RNA sequencing data

Long-read RNA sequencing (lrRNA-seq) has revolutionized transcriptomics facilitating the study of alternative splicing and resulting in identification of thousands of novel transcripts. While isoform identification has received significant attention, the handling of biologically replicated lrRNA-seq datasets remains less explored. However, how multiple samples are combined in a lrRNA-seq study may strongly impact transcript identification. This study defines and evaluates two strategies for obtaining consensus transcriptomes from multi-sample lrRNA-seq data: "Join & Call", where reads from all samples are combined before transcript identification, and "Call & Join", where transcript identification is performed on individual samples before combining the resulting annotations. We applied these strategies to a highly replicated dataset of mouse brain and kidney tissues, using both PacBio and ONT technologies, across six widely used transcript reconstruction tools. Our results indicate that the optimal strategy depends on the chosen computational tool and research objective. We found that Join & Call is generally more suitable for discovering rarely occurring, novel isoforms, as pooling evidence increases confidence in calling lowly-expressed transcripts. Conversely, Call & Join is computationally more efficient and often preferable for highly replicated datasets when the investigation of rare novel transcripts is not the primary objective. Our findings provide a conceptual and practical framework for multi-sample transcriptome reconstruction, guiding best practices in the context of increasingly large-scale lrRNA-seq studies.

bioinformatics↗

bioRxiv2024

Pelagic ecosystem responses to changes in seawater conditions during the Middle Pleistocene Transition in the Eastern Mediterranean

AbstractMesopelagic fishes play a crucial role in the global carbon cycle through their diel vertical migration (DVM), but the impacts of neither natural nor anthropogenic climate change on DVM patterns are currently known. Studying the geological past can elucidate changes in DVM patterns under swelling climate pressure and allow estimating the impacts of the current climate crisis. We present a multi-proxy, ecosystem-level assessment of paleoenvironmental changes in the Eastern Mediterranean during the Middle Pleistocene (marine isotope stages MIS 23-18; 923-756 ka B.P.) and use the carbon and oxygen isotopic composition of fossil fish otoliths to assess the impacts of these changes on DVM and their possible implications on the biological pump. Temperature was the primary driver of ecosystem change during MIS 21 interglacial, whereas productivity became a dominant factor in MIS 19 interglacial. Responses of organisms throughout the water column varied. Our results indicate increased productivity across trophic levels during MIS 19, which affected foraminiferal biomasses, but did not inhibit fish DVM. In contrast, the early MIS 21 warming led to a reduction in DVM by mesopelagic fishes and consequently a drop in biological pump efficiency.

systems biology↗

bioRxiv2023

SQANTI-SIM: a simulator of controlled transcript novelty for lrRNA-seq benchmark

Long-read RNA-seq has emerged as a powerful tool for transcript discovery, even in well-annotated organisms. However, assessing the accuracy of different methods in identifying annotated and novel transcripts remains a challenge. Here, we present SQANTI-SIM, a versatile utility that wraps around popular long-read simulators to allow precise management of transcript novelty based on the structural categories defined by SQANTI3. By selectively excluding specific transcripts from the reference dataset, SQANTI-SIM effectively emulates scenarios involving unannotated transcripts. Furthermore, the tool provides customizable features and supports the simulation of additional types of data, representing the first multi-omics simulation tool for the lrRNA-seq field. We demonstrate the effectiveness of SQANTI-SIM by benchmarking five transcriptome reconstruction pipelines using the simulated data.

bioinformatics↗

bioRxiv2023

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms

The emergence of long-read RNA sequencing (lrRNA-seq) has provided an unprecedented opportunity to analyze transcriptomes at isoform resolution. However, the technology is not free from biases, and transcript models inferred from these data require quality control and curation. In this study, we introduce SQANTI3, a tool specifically designed to perform quality analysis on transcriptomes constructed using lrRNA-seq data. SQANTI3 provides an extensive naming framework to describe transcript model diversity in comparison to the reference transcriptome. Additionally, the tool incorporates a wide range of metrics to characterize various structural properties of transcript models, such as transcription start and end sites, splice junctions, and other structural features. These metrics can be utilized to filter out potential artifacts. Moreover, SQANTI3 includes a Rescue module that prevents the loss of known genes and transcripts exhibiting evidence of expression but displaying low-quality features. Lastly, SQANTI3 incorporates IsoAnnotLite, which enables functional annotation at the isoform level and facilitates functional iso-transcriptomics analyses. We demonstrate the versatility of SQANTI3 in analyzing different data types, isoform reconstruction pipelines, and sequencing platforms, and how it provides novel biological insights into isoform biology. The SQANTI3 software is available at https://github.com/ConesaLab/SQANTI3.

bioinformatics↗