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‘Multi-omic’ data analysis using O-miner

Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or a...

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Autores principales: Sangaralingam, Ajanthah, Dayem Ullah, Abu Z, Marzec, Jacek, Gadaleta, Emanuela, Nagano, Ai, Ross-Adams, Helen, Wang, Jun, Lemoine, Nicholas R, Chelala, Claude
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357557/
https://www.ncbi.nlm.nih.gov/pubmed/28981577
http://dx.doi.org/10.1093/bib/bbx080
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author Sangaralingam, Ajanthah
Dayem Ullah, Abu Z
Marzec, Jacek
Gadaleta, Emanuela
Nagano, Ai
Ross-Adams, Helen
Wang, Jun
Lemoine, Nicholas R
Chelala, Claude
author_facet Sangaralingam, Ajanthah
Dayem Ullah, Abu Z
Marzec, Jacek
Gadaleta, Emanuela
Nagano, Ai
Ross-Adams, Helen
Wang, Jun
Lemoine, Nicholas R
Chelala, Claude
author_sort Sangaralingam, Ajanthah
collection PubMed
description Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from ‘-omics’ technologies. Created from a biologist’s perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.
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spelling pubmed-63575572019-02-08 ‘Multi-omic’ data analysis using O-miner Sangaralingam, Ajanthah Dayem Ullah, Abu Z Marzec, Jacek Gadaleta, Emanuela Nagano, Ai Ross-Adams, Helen Wang, Jun Lemoine, Nicholas R Chelala, Claude Brief Bioinform Paper Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from ‘-omics’ technologies. Created from a biologist’s perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org. Oxford University Press 2017-08-04 /pmc/articles/PMC6357557/ /pubmed/28981577 http://dx.doi.org/10.1093/bib/bbx080 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Paper
Sangaralingam, Ajanthah
Dayem Ullah, Abu Z
Marzec, Jacek
Gadaleta, Emanuela
Nagano, Ai
Ross-Adams, Helen
Wang, Jun
Lemoine, Nicholas R
Chelala, Claude
‘Multi-omic’ data analysis using O-miner
title ‘Multi-omic’ data analysis using O-miner
title_full ‘Multi-omic’ data analysis using O-miner
title_fullStr ‘Multi-omic’ data analysis using O-miner
title_full_unstemmed ‘Multi-omic’ data analysis using O-miner
title_short ‘Multi-omic’ data analysis using O-miner
title_sort ‘multi-omic’ data analysis using o-miner
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6357557/
https://www.ncbi.nlm.nih.gov/pubmed/28981577
http://dx.doi.org/10.1093/bib/bbx080
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