L’UMR CBGP – Centre de Biologie pour la Gestion des Populations – a pour vocation de comprendre les mécanismes qui régissent l’évolution de populations d'organismes importants pour l’agronomie, les forêts, la santé humaine ou la conservation de la biodiversité. Les recherches portent sur des modèles biologiques et sont développées selon six axes : origine et caractérisation de la biodiversité ; adaptation des phytophages, de leurs ennemis naturels et de leurs symbiontes ; écologie et évolution des zoonoses ; biologie, écologie et évolution des espèces envahissantes ; génomique statistique et évolutive des populations ; approches moléculaires et bioinformatiques haut débit. Nos tutelles sont l'INRAE, le CIRAD, l'IRD et l'Institut Agro-Montpellier.

UMR CBGP – Centre de Biologie pour la Gestion des Populations – aims to understand the mechanisms that govern populations of organisms that are important to agriculture, forest, human health and biodiversity conservation. Studies concern biological models and follow six lines of research: origin and characterization of biodiversity; adaptation of plant eaters, their natural enemies and symbionts; ecology and evolution of zoonoses; biology, ecology and evolution of invasive species; statistical and evolutionary population genomics; and high-throughput molecular and bioinformatics techniques. Our supervising bodies are INRAE, CIRAD, IRD and Institut Agro-Montpellier.
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

71 to 80 of 148 Results
uniqOcc_clcpro_df_recession_res50.RData
23 avr. 2024 - Multi-scale analyses of past climate change and management impacts on desert locust gregarization risk.
R Data - 469,2 KB - MD5: 1b290cb8cab33e72451425611b8c19eb
Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"
Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"
1 mars 2024
Chapuis, Marie-Pierre; Benoit, Laure; Galan, Maxime, 2023, "Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"", https://doi.org/10.18167/DVN1/D31UAV, CIRAD Dataverse, V2, UNF:6:3cpS5HiYLk+POaq90NQuug== [fileUNF]
Shell script for preprocessing of raw sequences ; R scripts for data filtering, reduction and analysis ; abundance table data.
fastqCombinePairedEnd.py
1 mars 2024 - Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"
Python Source Code - 4,1 KB - MD5: a8615acfbf0737600ce065ffb8c8b83f
Executable file required for using the Shell script "Preprocess_1step_forFrogs.sh"
Preprocess_1step_forFrogs.sh
1 mars 2024 - Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"
Shell Script - 13,2 KB - MD5: 930c4ed891ebdbdcccfab0b01dd893b5
Shell script for preprocessing of raw sequences (before running Frogs) : From demultiplexed fastq.gz files produced from an Illumina MiSeq amplicon library, this script cuts reads from 3' primers (with cutadapt), trims on length longer reads (with fastx_trimmer), then merges read...
remove_void.awk
1 mars 2024 - Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"
application/x-awk - 329 B - MD5: 3e02ca2e3e65e7294e2e88d9033006eb
Executable file required for using the Shell script "Preprocess_1step_forFrogs.sh"
Multi-agent model SANDMAN: source code
Multi-agent model SANDMAN: source code
2 oct. 2023
SOREL, Maeva; GAY, Pierre-Emmanuel; PIOU, Cyril, 2023, "Multi-agent model SANDMAN: source code", https://doi.org/10.18167/DVN1/1UIN2P, CIRAD Dataverse, V2
Multi-agent model SANDMAN source code for NetLogo (v. 6.3.0). [Swarm migrAtion uNder winD, teMperature and vegetAtion iNfluence]
SANDMAN7.3.nlogo
2 oct. 2023 - Multi-agent model SANDMAN: source code
Unknown - 75,6 KB - MD5: 48f806a08ad63521fa3927f7ad8a1f53
Code
V7.3. Source code suitable for Netlogo 6.3.0
Filter1_sequencing-run.R
2 mai 2023 - Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"
R Syntax - 8,3 KB - MD5: 14dd967c52750f012e387f2c3c786a35
First R script for data filtering. This script filter data for index contamination following correction 2 from Galan et al. 2016 (Tfa) and works on an abundance text file from Frogs and a metadata file that specifies sample names, biological units, technical replicates and contro...
Filtering_Functions.R
2 mai 2023 - Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"
R Syntax - 377 B - MD5: 7cc002154f0e34a37b6b53f1ad649fb4
Loading packages and functions required for R scripts for data filtering.
Nextfilters_and_reduction.R
2 mai 2023 - Original data for " Chapuis MP et al. (2023). Evaluation of 96-well high-throughput DNA extraction methods for 16S rRNA gene metabarcoding. Molecular Ecology Resources, 23, 1509–1525"
R Syntax - 20,2 KB - MD5: 2ea6d5266bd97ee5195e4feff7a93b3a
Second R script for data filtering. This script keeps positive data only if congruent between replicates (duplicates or triplicates) and filters data for extraction & pcr contamination following correction 1 from Galan et al. 2016 (Tcc) and using negative controls. It works on an...
Add Data

Log in to create a dataverse or add a dataset.

Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.