Biology subjects

Rodrigues, A. V.

Publications and source records attributed to Rodrigues, A. V..

3 recordsLinked to original sources

bioRxiv2021

Accurate prediction of protein structures and interactions using a 3-track network

DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. One-Sentence SummaryAccurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function.

bioinformatics↗

bioRxiv2020

naturaList: a package to classify occurrence records in levels of confidence in species identification

O_LIThere is a big volume of occurrence records available in biodiversity databases, but researchers should guarantee its quality before use it in scientific studies. A problem that might compromise the quality of occurrence data is species misidentification. We address this issue by presenting naturaList, a R package designed to classify species occurrence data according to identification reliability. C_LIO_LInaturaList allows to classify species occurrences up to six levels of confidence in species identification, and to filter occurrence data accordingly. The highest level of confidence is assigned to records identified by a specialist, whose name must be provided by the user. The other five levels of confidence are derived from the occurrence data. We demonstrate naturaList functions using occurrences of Alsophila setosa, a tree fern species from Atlantic Forest, as example. We classified and filtered data in grid cells in order to maintain only the highest-level records in each cell. Then we selected only those records classified in the two highest levels of confidence. C_LIO_LIFrom 323 occurrences of Alsophila setosa displaying geographic coordinates, 69 (21%) were identified by a specialist. After filtering the highest-level records inside grid cells, 102 records remained. From these grid cell filtered data, 38 occurrences (37%) were classified into the highest confidence level. Three records were removed using an interactive map module, due to falling in sea sites or outside the native range size of the species. Since we selected only records classified in the two highest levels of confidence, the final dataset contained 94 occurrence records. C_LIO_LInaturaList guarantees the reproducibility of occurrence data processing and cleaning. Macroecologists, biogeographers and taxonomists might benefit from using naturaList package to evaluate the quality of species identification in occurrence data and by identify sites that need evaluation of taxonomic classification of species. C_LI

ecology↗

bioRxiv2020

Expected impacts of climate change on tree ferns distribution and diversity patterns in subtropical Atlantic Forest

Tree ferns are common elements in the Atlantic Forest domain, sometimes reaching more than half of total dominance at forest sites. Just as most groups, climate change might impact the distribution and diversity of tree ferns. To investigate the extent of these impacts in the subtropical Atlantic Rainforest, we measured the changes in species distribution, - and {beta}-diversity between current climate and future climatic scenarios for 2050. Most tree ferns species tend to lose their distribution area. Hence, species richness tends to decrease in the future, especially in the Rainforest sites. In general, {beta}-diversity tend to not change on the regional scale, but some sites can change its relative singularity in composition. Our results show that climate change can impact distribution and -diversity of tree ferns, but with no trend to cause homogenization in the tree ferns of the study area. Protected Areas (PAs) in our study region manage to withhold more -diversity than areas without PAs -- the same applies to {beta}-diversity. Our study offers a new light into the effects of climate change in tree ferns by integrating the evaluation of its impacts on distribution, - and {beta}-diversity in all study areas and inside PAs.

ecology↗