MassIVE MSV000086530

Complete Public PXD022796

Towards predicting the fate of reef corals

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Description

An iTRAQ approach was used to characterize the proteomes of reef corals (Orbicella faveolata) and their dinoflagellate endosymbionts (family Symbiodiniaceae) from a short-term (one month) thermal stress experiment. Please note that the same protein sample was labeled with the 113 tag and run in each batch (A, B, and C in the file names) to serve as an internal control aimed at limiting bias associated with batch-to-batch variation. As such, although 24 samples were analyzed (8-plex iTRAQ was used), only 21 of these reflect actual experimental coral samples. Please see the attached Word and Excel files to match the batch and tag with the experimental samples. Note that one sample (B5-7; iTRAQ batch A-label 114; a control coral sampled after five treatment days) spilled in the speed-vac during preparation and so was not analyzed, resulting in a final sample size of 20. [doi:10.25345/C55Z0Q] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: coral reefs ; climate change ; dinoflagellate ; proteomics ; predictive modeling

Contact

Principal Investigators:
(in alphabetical order) 		Anderson Mayfield, University of Miami, United States
Submitting User: abmayfield

Publications

Anderson B. Mayfield.
Machine-learning-based proteomic predictive modeling with thermally- challenged Caribbean reef corals.
Mayfield AB (2022) Machine-learning-based proteomic predictive modeling with thermally- challenged Caribbean reef corals. Diversity 14, 33.

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Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

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Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

Distinct replicate labels are counted across all files submitted in the "Metadata" category having a "BioReplicate" or "Replicate" column in this dataset.

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Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.