Biology subjects

Siberchicot, A.

Publications and source records attributed to Siberchicot, A..

5 recordsLinked to original sources

bioRxiv2023

DRomics, a workflow to model and make sense of dose-response (multi-)omics data in (eco)toxicology

Omics technologies has opened new possibilities to assess environmental risks and to understand the mode(s) of action of pollutants. Coupled to dose-response experimental designs, they allow a non-targeted assessment of organism responses at the molecular level along an exposure gradient. However, describing the dose-response relationships on such high-throughput data is no easy task. In a first part, we review the software available for this purpose, and their main features. We set out arguments on some statistical and modeling choices we have made while developing the R package DRomics and its positioning compared to others tools. The DRomics main analysis workflow is made available through a web interface, namely a shiny app named DRomics-shiny. Next, we present the new functionalities recently implemented. DRomics has been augmented especially to be able to handle varied omics data considering the nature of the measured signal (e.g. counts of reads in RNAseq) and the way data were collected (e.g. batch effect, situation with no experimental replicates). Another important upgrade is the development of tools to ease the biological interpretation of results. Various functions are proposed to visualize, summarize and compare the responses, for different biological groups (defined from biological annotation), optionally at different experimental levels (e.g. measurements at several omics level or in different experimental conditions). A new shiny app named DRomicsInterpreter-shiny is dedicated to the biological interpretation of results. The institutional web page https://lbbe.univ-lyon1.fr/fr/dromics gathers links to all resources related to DRomics, including the two shiny applications.

bioinformatics↗

bioRxiv2023

PhylteR: efficient identification of outlier sequences in phylogenomic datasets

In phylogenomics, incongruences between gene trees, resulting from both artifactual and biological reasons, can decrease the signal-to-noise ratio and complicate species tree inference. The amount of data handled today in classical phylogenomic analyses precludes manual error detection and removal. However, a simple and efficient way to automate the identification of outliers from a collection of gene trees is still missing. Here, we present PhylteR, a method that allows a rapid and accurate detection of outlier sequences in phylogenomic datasets, i.e. species from individual gene trees that do not follow the general trend. PhylteR relies on DISTATIS, an extension of multidimensional scaling to 3 dimensions to compare multiple distance matrices at once. In PhylteR, these distance matrices extracted from individual gene phylogenies represent evolutionary distances between species according to each gene. On simulated datasets, we show that PhylteR identifies outliers with more sensitivity and precision than a comparable existing method. We also show that PhylteR is not sensitive to ILS-induced incongruences, which is a desirable feature. On a biological dataset of 14,463 genes for 53 species previously assembled for Carnivora phylogenomics, we show (i) that PhylteR identifies as outliers sequences that can be considered as such by other means, and (ii) that the removal of these sequences improves the concordance between the gene trees and the species tree. Thanks to the generation of numerous graphical outputs, PhylteR also allows for the rapid and easy visual characterisation of the dataset at hand, thus aiding in the precise identification of errors. PhylteR is distributed as an R package on CRAN and as containerized versions (docker and singularity).

evolutionary biology↗

bioRxiv2022

Larval density in the invasive Drosophila suzukii: immediate and delayed effects on life-history traits

The immediate and delayed effects of density are key in determining population dynamics, since they can positively or negatively affect the fitness of individuals. These effects have great relevance for polyphagous insects for which immature stages develop within a single site of finite feeding resources. Drosophila suzukii is a crop pest that induces severe economic losses for agricultural production, however little is known about the effects of density on its life-history traits. In the present study, we (i) investigated the egg distribution resulting from females egg-laying strategy and (ii) tested the immediate and delayed effects of larval density on emergence rate, development time, sex ratio of offspring, fecundity and adult size (a range of 1 to 50 larvae was used). We showed that most of fruits contain several eggs and aggregate of eggs of high density can be found in some fruits. This high density has no immediate effects on the emergence rate, but has effect on larval developmental time. This trait was involved in a trade-off with adult life-history traits: the larval development was reduced as larval density increased, but smaller and less fertile adults were produced. Our results should help to better understand the population dynamics of this species and to develop more successful control programs.

ecology↗

bioRxiv2021

rbioacc: an R-package to analyse toxicokinetic data

O_LI rbioacc is an R-package dedicated to the analysis of experimental data collected from bioaccumulation tests during which organisms are exposed to a chemical (exposure phase) and then put into a clean media (depuration phase). Internal concentrations are regularly measured over time all along the experiment. C_LIO_LI rbioacc provides ready-to-use functions to visualize and fully analyze such data. Under a Bayesian framework, this package fits a generic one-compartment toxicokinetic (TK) model automatically built from the data. It provides TK parameter estimates (appropriate uptake and elimination rates) and bioaccumulation metrics (e.g., BCF, BSAF, BMF). All parameter estimates, bioaccumulation metrics as well as predictions of internal concentrations into organisms are delivered with their uncertainty. C_LIO_LIBioaccumulation metrics are primarily provided in support of environmental risk assessment, in full compliance with regulatory requirements required to approve marketing applications of chemical substances. C_LIO_LIThis paper gives brief worked examples of the use of rbioacc from data collected through standard bioaccumulation tests, and publicly available within the scientific literature. These examples constitute step-by-step user-guides to analyze any new data set, uploaded in the right format. C_LI

ecology↗

bioRxiv2021

Taking full advantage of modelling to better assess environmental risk due to xenobiotics

In the European Union, more than 100,000 man-made chemical substances are awaiting an environmental risk assessment (ERA). Simultaneously, ERA of chemicals has now entered a new era. Indeed, recent recommendations from regulatory bodies underline a crucial need for the use of mechanistic effect models, allowing assessments that are not only ecologically relevant, but also more integrative, consistent and efficient. At the individual level, toxicokinetic-toxicodynamic (TKTD) models are particularly encouraged for the regulatory assessment of pesticide-related risks on aquatic organisms. In this paper, we first propose a brief review of classical dose-response models to put into light the on-line MOSAIC tool offering all necessary services in a turnkey web platform whatever the type of data to analyze. Then, we focus on the necessity to account for the time-dimension of the exposure by illustrating how MOSAIC can support a robust calculation of bioaccumulation factors. At last, we show how MOSAIC can be of valuable help to fully complete the EFSA workflow regarding the use of TKTD models, especially with GUTS models, providing a user-friendly interface for calibrating, validating and predicting survival over time under any time-variable exposure scenario of interest. Our conclusion proposes a few lines of thought for an even easier use of modelling in ERA. Graphical art O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/436474v3_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@d98a72org.highwire.dtl.DTLVardef@1066b4org.highwire.dtl.DTLVardef@c6eef3org.highwire.dtl.DTLVardef@71c528_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗