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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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'Multi-omic' data analysis using O-miner.

Ajanthah Sangaralingam1, Abu Z Dayem Ullah1, Jacek Marzec1

  • 1Barts Cancer Institute, Queen Mary University of London.

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|October 6, 2017
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Summary
This summary is machine-generated.

The O-miner suite now integrates transcriptomic, genomic, and methylomic data for automated analysis. This bioinformatics tool helps scientists identify biomarkers and pathways from complex -omics data, maximizing research output.

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Area of Science:

  • Biomedical Research
  • Bioinformatics
  • Genomics
  • Transcriptomics
  • Methylomics

Background:

  • High-throughput -omics technologies generate vast datasets, posing integration and analysis challenges for researchers lacking bioinformatics expertise.
  • A need exists for user-friendly tools to analyze complex -omics data alongside biological and clinical information.

Purpose of the Study:

  • To present an updated O-miner analytical suite designed for automated, biologist-centric analysis of multi-omics data.
  • To enable identification of significantly altered pathways and prioritization of novel biomarkers/targets.

Main Methods:

  • The O-miner web tool was updated to incorporate new features and data types for analyzing transcriptomic, genomic, and methylomic data.
  • Pipelines allow for the integration of in-house and public -omics data, including meta-analyses.

Main Results:

  • The updated O-miner suite provides an efficient, easy-to-use platform for automated analysis of large-scale -omics datasets.
  • Demonstrated utility in identifying significantly altered pathways and prioritizing potential biomarkers/targets for validation.

Conclusions:

  • O-miner serves as a valuable resource for basic and clinical scientists to overcome bioinformatics challenges in -omics data analysis.
  • The tool facilitates the maximization of research output by enabling comprehensive analysis and interpretation of multi-omics data.