Peron, G. | Search bioRxiv

bioRxiv2020

CitrusKB: A Comprehensive Knowledge Base for Transcriptome and Interactome of Citrus spp. Infected by Xanthomonas citri subsp. citri at Different Infection Stages

Citrus canker type A is a serious disease caused by Xanthomonas citri subsp. citri (X. citri), which is responsible for severe losses to growers and to the citrus industry worldwide. To date, no canker-resistant citrus genotypes are available, and there is limited information regarding the molecular and genetic mechanisms involved in the early stages of the citrus canker development. Here, we present the knowledge base for transcriptome of in vivo citrus interactome, the CitrusKB. This is the first in vivo interactome database for different citrus cultivars, and it was produced to provide a valuable source of information on citrus and their interaction with the citrus canker bacterium X. citri. The database provides tools for a user-friendly web interface to search and analyze a large amount of information regarding eight citrus cultivars with distinct levels of susceptibility to the disease and their interaction, at different stages of infection, with the citrus canker bacterium X. citri. Currently, CitrusKB comprises a reference citrus genome and its transcriptome, expressed transcripts, pseudogenes and predicted genomic variations (SNPs and SSRs). The updating process will continue by incorporating annotations and analysis tools. We expect that CitrusKB may substantially contribute to the area of citrus genomics. CitrusKB is accessible at http://bioinfo.deinfo.uepg.br/citrus. Users can download all the generated raw sequences and generated datasets by this study from the CitrusKB website.

genomics↗

bioRxiv2019

The challenges of estimating the distribution of flight heights from telemetry or altimetry data

BackgroundGlobal positioning systems (GPS) and altimeters are increasingly used to monitor vertical space use by aerial species, a key aspect of their niche that we need to know to understand their ecology and conservation needs, and to manage our own use of the airspace. However, there are various sources of error in flight height data (\"height\" above ground, as opposed to \"altitude\" above a reference like the sea level): vertical error from the devices themselves, error in the ground elevation below the tracked animals, and error in the horizontal position of the animals and thus the predicted ground elevation below them.\n\nMethodsWe used controlled field trials, simulations, and the reanalysis of raptor case studies with state-space models to illustrate the effect of improper error management.\n\nResultsErrors of a magnitude of 20 meters appear in benign conditions (expected to be larger in more challenging context). These errors distort the shape of the distribution of flight heights, inflate the variance in flight height, bias behavioural state assignments, correlations with environmental covariates, and airspace management recommendations. Improper data filters such as removing all negative recorded flight height records introduce several biases in the remaining dataset, and preclude the opportunity to leverage unambiguous errors to help with model fitting. Analyses that ignore the variance around the mean flight height, e.g., those based on linear models of flight height, and those that ignore the variance inflation caused by telemetry errors, lead to incorrect inferences.\n\nConclusionThe state-space modelling framework, now in widespread use by ecologists and increasingly often automatically implemented within on-board GPS data processing algorithms, makes it possible to fit flight models directly to raw flight height records, with minimal data pre-selection, and to analyse the full distribution of flight heights, not just the mean. In addition to basic research about aerial niches, behaviour quantification, and environmental interactions, we highlight the applied relevance of our recommendations for airspace management and the conservation of aerial wildlife.

ecology↗