Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

ACCESS DATA

Json Xml

Similar Datasets

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (tubulointerstitium)

Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.

2013-08-06 | E-GEOD-47184 | biostudies-arrayexpress

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (glomeruli)

2013-08-06 | E-GEOD-47183 | biostudies-arrayexpress

Small RNA-seq of undiseased human brain

Project description:The surprising observation that virtually the entire human genome is transcribed means we know very little about the function of many emerging classes of RNAs, except their astounding diversity. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their ability to classify classes of non-coding RNAs (ncRNAs). To address this, we developed CoRAL, a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length, cleavage specificity, and antisense transcription to distinguish between different ncRNA classes. We evaluated CoRAL using genome-wide small RNA sequencing (smRNA-seq) datasets from two human tissue types (brain and skin [GSE31037]), and were able to classify six different types of RNA transcripts with 79~80% accuracy in cross-validation experiments, and with 71~73% accuracy when CoRAL uses one tissue type for training and the other as validation. Analysis by CoRAL revealed that long intergenic ncRNAs, small cytoplasmic RNAs, and small nuclear RNAs show more tissue specificity, while microRNAs, small nucleolar, and transposon-derived RNAs are highly discernible and consistent across the two tissue types. The ability to consistently annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using smRNA-seq data in less characterized organisms. Four samples were sequenced, each one coming from frozen brain tissue (frontal cortex) of a deceased female human patient with no remarkable pathology.

2013-07-07 | E-GEOD-43335 | biostudies-arrayexpress

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (tubulointerstitium)

2013-08-06 | GSE47184 | GEO

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (glomeruli)

2013-08-06 | GSE47183 | GEO

Small RNA-seq of undiseased human brain

2013-07-07 | GSE43335 | GEO

Project description:GBE machine learning data

| PRJNA729223 | ENA

Cell-free DNA 5-hydroxymethylcytosine as a marker for common cancer detection

Project description:We used a highly sensitive nano-5hmC-Seal method and profiled the genome-wide distribution of 5-hydroxymethylcytosine (5hmC) in plasma cell-free DNA (cfDNA) from 384 patients with bladder, breast, colorectal, kidney, lung, or prostate cancer and 221 controls. We used machine learning and developed plasma cfDNA 5hmC signatures that are highly sensitive for cancer detection and cancer origin determination. We also identified genes and signaling pathways with aberrant DNA hydroxymethylation in six cancers.

2022-09-23 | GSE202988 | GEO

Towards predicting the fate of reef corals

Project 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.

2020-11-27 | MSV000086530 | MassIVE

Native molecule sequencing by nano-ID reveals synthesis and stability of RNA isoforms

Project description:Eukaryotic genes often generate a variety of RNA isoforms that can lead to functionally distinct protein variants. The synthesis and stability of RNA isoforms is however poorly characterized. The reason for this is that current methods to quantify RNA metabolism use short-read sequencing that cannot detect RNA isoforms. Here we present nanopore sequencing-based Isoform Dynamics (nano-ID), a method that detects newly synthesized RNA isoforms and monitors isoform metabolism. nano-ID combines metabolic RNA labeling, long-read nanopore sequencing of native RNA molecules and machine learning. nano-ID derived RNA stability estimates enable a distinctive evaluation of stability determining factors such as sequence, poly(A)-tail length, RNA secondary structure, translation efficiency and RNA binding proteins. Application of nano-ID to the heat shock response in human cells reveals that many RNA isoforms change their stability. nano-ID also shows that the metabolism of individual RNA isoforms differs strongly from that estimated for the combined RNA signal at a specific gene locus. nano-ID enables studies of RNA metabolism on the level of single RNA molecules and isoforms in different cell states and conditions.

2020-05-06 | GSE127890 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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