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Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs).

Warren A Cheung¹, B F Francis Ouellette, Wyeth W Wasserman

¹Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada.

BMC Bioinformatics

|September 29, 2012

Summary

We developed MeSH Over-representation Profiles (MeSHOPs) to identify key biomedical themes in literature. This method quantifies associations between entities and medical concepts, aiding in relationship discovery.

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Area of Science:

Biomedical Informatics
Computational Biology
Medical Subject Headings (MeSH)

Background:

MEDLINE®/PubMed® indexes over 20 million biomedical articles.
Medical Subject Headings (MeSH) is a controlled vocabulary for article annotation.
Identifying key biomedical themes is crucial for understanding concepts and discovering relationships.

Purpose of the Study:

Introduce a novel method for determining enriched MeSH annotations for specific topics.
Develop MeSH Over-representation Profiles (MeSHOPs) for quantitative analysis and visualization.
Enable computational analysis and visualization of biomedical literature themes.

Main Methods:

Generate MeSH Over-representation Profiles (MeSHOPs) based on hypergeometric distribution.
Statistically account for term prevalence against a specified background.
Visualize MeSHOPs using word clouds for relative importance and track changes over time using publication dates.

Main Results:

MeSHOPs provide quantitative summaries of MeSH annotations for literature sets.
MeSHOPs highlight unusually prevalent terms, offering insights into topic associations.
MeSHOPs can be compared using techniques like hierarchical clustering and validated against Gene Ontology annotations.

Conclusions:

MeSHOPs enable quantitative measurement of entity-medical concept associations from literature.
Comparison of MeSHOPs facilitates relating entities based on shared medical themes.
A web interface is available for generating and visualizing MeSHOPs.