Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Fishes

ABSTRACT: Phylogenomics and Historical Biogeography of Seahorses, Dragonets, Goatfishes, and Allies (Teleostei: Syngnatharia): Assessing Factors Driving Uncertainty in Biogeographic Inferences

PROVIDER: PRJNA725267 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR14414700_1.fastq.gz	Fastqsanger.gz
	SRR14414700_2.fastq.gz	Fastqsanger.gz
	SRR14414701_1.fastq.gz	Fastqsanger.gz
	SRR14414701_2.fastq.gz	Fastqsanger.gz
	SRR14414702_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 144

Similar Datasets

Project description:Plastome phylogenomics and historical biogeography of aquatic plant genus Hydrocharis (Hydrocharitaceae)

| PRJNA771243 | ENA

Project description:Biogeographic inferences across spatial and evolutionary scales

| PRJNA914787 | ENA

Project description:Disentangling Historical Relationships Within Poeciliidae (Teleostei: Cyprinodontiformes) Using Ultraconserved Elements

| PRJNA992473 | ENA

Cristaria Atacama Desert

Project description:Historical Biogeography of the Atacama plant genus Cristaria

| PRJEB48173 | ENA

Project description:Historical biogeography and plastome evolution of Commelinaceae Mirb.

| PRJNA1003278 | ENA

Project description:Phylogenomics and biogeography of seven cacti genera

| PRJNA934337 | ENA

Project description:Phylogenomics, biogeography, and adaptive radiation of grapes

| PRJNA490319 | ENA

Project description:Phylogenomics of bumblebee catfishes of the family Pseudopimelodidae using ultraconserved elements (Teleostei: Siluriformes)

| PRJNA724721 | ENA

Metabolomic evidence for biogeographic segregation in Espeletiinae (Asteraceae): A new perspective on an Andean adaptive radiation in sky islands (Intraspecific chemical variability assay)

Project description:The biogeography of tropical Andean plants and the effects of altitudinal barriers as evolutionary constraints have been the focus of great debate. Although genetic markers have provided evidence for the geographic isolation of Andean-endemic groups, even highly variable molecular markers commonly used to assess biogeography do not have the resolving power needed for a fine geographic discrimination in some Andean taxa. We present a metabolomics approach based on ultrahigh-performance liquid chromatography-mass spectrometry combined with multivariate statistical methods and machine learning algorithms of multiple species and populations of Espeletiinae (Asteraceae) to provide metabolomic evidence for biogeographic segregation in this Andean-endemic subtribe. Our approach allows the discrimination of Espeletiinae taxa at different geographic scales with characteristic metabolic fingerprints related to their country of origin on a global scale, to their páramo massifs on a regional scale and to their páramo complexes on a local scale, revealing inter- and intraspecific metabolic variations. Our results, together with previous studies, suggest that Andean geography and Pleistocene glacial cycles not only shaped the evolutionary history of Espeletiinae but also its metabolic fingerprints, demonstrating the potential of metabolomics for understanding biogeographic patterns in recently diversified plant groups where genetic divergence is still at an early stage. Intraspecific chemical variability assay protocols and data are reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS943' rel='noopener noreferrer' target='_blank'>MTBLS943</a>.Interspecific chemical variability assay protocols and data are reported in study <a href='https://www.ebi.ac.uk/metabolights/MTBLS944' rel='noopener noreferrer' target='_blank'>MTBLS944</a>.

2020-03-24 | MTBLS943 | MetaboLights

Metabolomic evidence for biogeographic segregation in Espeletiinae (Asteraceae): A new perspective on an Andean adaptive radiation in sky islands (Interspecific chemical variability assay)

Project description:The biogeography of tropical Andean plants and the effects of altitudinal barriers as evolutionary constraints have been the focus of great debate. Although genetic markers have provided evidence for the geographic isolation of Andean-endemic groups, even highly variable molecular markers commonly used to assess biogeography do not have the resolving power needed for a fine geographic discrimination in some Andean taxa. We present a metabolomics approach based on ultrahigh-performance liquid chromatography-mass spectrometry combined with multivariate statistical methods and machine learning algorithms of multiple species and populations of Espeletiinae (Asteraceae) to provide metabolomic evidence for biogeographic segregation in this Andean-endemic subtribe. Our approach allows the discrimination of Espeletiinae taxa at different geographic scales with characteristic metabolic fingerprints related to their country of origin on a global scale, to their páramo massifs on a regional scale and to their páramo complexes on a local scale, revealing inter- and intraspecific metabolic variations. Our results, together with previous studies, suggest that Andean geography and Pleistocene glacial cycles not only shaped the evolutionary history of Espeletiinae but also its metabolic fingerprints, demonstrating the potential of metabolomics for understanding biogeographic patterns in recently diversified plant groups where genetic divergence is still at an early stage. Interspecific chemical variability assay protocols and data are reported in the current study <a href='https://www.ebi.ac.uk/metabolights/MTBLS944' rel='noopener noreferrer' target='_blank'>MTBLS944</a>.Intraspecific chemical variability assay protocols and data are reported in study <a href='https://www.ebi.ac.uk/metabolights/MTBLS943' rel='noopener noreferrer' target='_blank'>MTBLS943</a>.

2020-03-24 | MTBLS944 | MetaboLights

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data