UMR DIADE

DIversity - Adaptation - plant DEvelopment

The DIADE Research Unit aims to understand the diversification of tropical plants, one of the main original reservoirs of biodiversity, and for which conservation, management and exploitation are an important issue for Sustainable Development.

The nine teams forming the Unit belong to IRD, University of Montpellier, CIRAD, and CNRS.

CERES Team

DYNADIV Team

PANEEC Team

EVOGEC Team

ADvENS Team

AgroLD

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11 to 20 of 1,043 Results

liftoff_o157h7_to_s88.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 2.9 MB - MD5: 16c7a8c67ec05111e9d9c98e4e4b906a annotation transferred by Liftoff
S88_fa4fe08.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 1.8 MB - MD5: 7ecf8459a73585ee129b054d09b00c20 annotation transferred by GrAnnoT
AzucenaRS1_filter50.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 116.7 MB - MD5: 22cae9b869fc700acb22361127cac2eb Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 50%
AzucenaRS1_filter60.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 116.2 MB - MD5: dfb66d40a2ac500739173c81e744ccc7 Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 60%
AzucenaRS1_filter70.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 115.7 MB - MD5: 59e6f3c7baa18dfa31c65f4fa218c338 Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 70%
AzucenaRS1_filter80.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 115.0 MB - MD5: 56eceffb04dda9e7fbe0e48972befc9d Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 80%
AzucenaRS1_filter90.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 113.7 MB - MD5: 849a84800d8a3cea65e146e8ecf4eda5 Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 90%
AzucenaRS1_filter95.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 111.8 MB - MD5: 7224ffc601ff9b6dfd015b5bd77eb1e9 Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 95%
Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset. Sep 17, 2025 - DYNADIV Team Kaczmarek, Thomas; Faye, Adama; Ilmi Ahmed, Aicha; Veltman, Margaretha; Thuillet, Anne-Celine; Scarcelli, Nora; Sultan, Benjamin; Vigouroux, Yves; Cubry, Philippe, 2025, "Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.", https://doi.org/10.23708/M9YGDC, DataSuds, V1, UNF:6:EdX+/x3auhCXDY0Q7OgZ4g== [fileUNF] The ability of species and populations to adapt to their environment faces increasing challenges as climate change accelerates. Recent methods based on genomic offset (GO) statistics aim to quantify the risk of non-adaptation of populations to future climates. While several studi...
climateMetrics_EWEMBI_liebmann_west_africa_1979-2013.nc Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset. Network Common Data Form - 123.6 MB - MD5: 69a3987dbf4cc68d86230068ffeed8cc Données A NetCDF file with climate crop-specific metrics stored for each pixel (resolution 0.5°) located within a defined area covering West Africa.

liftoff_o157h7_to_s88.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 2.9 MB -

annotation transferred by Liftoff

S88_fa4fe08.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 1.8 MB -

annotation transferred by GrAnnoT

AzucenaRS1_filter50.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 116.7 MB -

Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 50%

AzucenaRS1_filter60.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 116.2 MB -

Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 60%

AzucenaRS1_filter70.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 115.7 MB -

Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 70%

AzucenaRS1_filter80.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 115.0 MB -

Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 80%

AzucenaRS1_filter90.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 113.7 MB -

Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 90%

AzucenaRS1_filter95.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 111.8 MB -

Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 95%

Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.

Sep 17, 2025 - DYNADIV Team

Kaczmarek, Thomas; Faye, Adama; Ilmi Ahmed, Aicha; Veltman, Margaretha; Thuillet, Anne-Celine; Scarcelli, Nora; Sultan, Benjamin; Vigouroux, Yves; Cubry, Philippe, 2025, "Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.", https://doi.org/10.23708/M9YGDC, DataSuds, V1, UNF:6:EdX+/x3auhCXDY0Q7OgZ4g== [fileUNF]

The ability of species and populations to adapt to their environment faces increasing challenges as climate change accelerates. Recent methods based on genomic offset (GO) statistics aim to quantify the risk of non-adaptation of populations to future climates. While several studi...

climateMetrics_EWEMBI_liebmann_west_africa_1979-2013.nc

Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.

Network Common Data Form - 123.6 MB -

Données

A NetCDF file with climate crop-specific metrics stored for each pixel (resolution 0.5°) located within a defined area covering West Africa.

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