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Field-testing a molecular biotechnology+machine-learning approach for predicting reef coral resilience

ABSTRACT: Please see the appended files for a detailed treatise on the project. Briefly, reef corals were sampled seasonally, with the biopsies analyzed via proteomics (iTRAQ+nano-liquid chromatography/MS/MS). The proteomic data were then input into machine-learning models that made predictions about coral colony bleaching susceptibility. In this way, the previously developed proteomic-based machine-learning models were effectively field-tested. With respect to file naming, there were 36 samples analyzed across six iTRAQ "batches" (A, B, C, D, E, and F). Please see Table 1 (csv) for a key as to how to link the iTRAQ labels/tags with the respective samples. For each batch, three technical replicates were analyzed by nano-liquid chromatography followed by mass spectrometry (x 2; n=18 RAW and 18 MZML files). Each MZML peak file was queried against a host coral (Orbicella faveolata) and a composite Symbiodiniaceae (endosymbiotic dinoflagellates) transcriptome, generating 36 mzTAB results files in total.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Durusdinium Sp. (ncbitaxon:2486700) Orbicella Faveolata (ncbitaxon:48498) Breviolum Sp. (ncbitaxon:2499526)

SUBMITTER: ANDERSON MAYFIELD

PROVIDER: MSV000089240 | MassIVE | Tue Apr 12 07:17:00 BST 2022

REPOSITORIES: MassIVE

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