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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Microarray Analysis for Saccharomyces cerevisiae
13:17

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Published on: April 7, 2011

Self-directed student research through analysis of microarray datasets: a computer-based functional genomics

Laura J Grenville-Briggs1, Ian Stansfield

  • 1School of Medical Sciences, Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK.

Biochemistry and Molecular Biology Education : a Bimonthly Publication of the International Union of Biochemistry and Molecular Biology
|November 15, 2011
PubMed
Summary
This summary is machine-generated.

Masters students effectively learned bioinformatics and functional genomics by analyzing complex yeast DNA damage response microarray data. This hands-on approach fostered hypothesis generation and research skills, mirroring real-world scientific inquiry.

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Area of Science:

  • Bioinformatics
  • Functional Genomics
  • Molecular Biology

Background:

  • Masters-level computer practical workshops are essential for advanced scientific training.
  • Functional genomics investigations require analysis of complex biological datasets.
  • Understanding DNA damage responses is crucial in human medical contexts.

Purpose of the Study:

  • To describe a series of Masters-level computer practical workshops focused on functional genomics.
  • To enable students to analyze complex transcriptomics datasets from yeast DNA damage response studies.
  • To stimulate active learning by simulating a realistic research environment for bioinformatics and functional genomics.

Main Methods:

  • Students engaged in advanced functional genomics by analyzing a microarray dataset.
  • Participants proposed and tested research hypotheses using transcriptomics data.
  • The practical workshops mimicked current research methods in bioinformatics.

Main Results:

  • The complexity of microarray data allowed students to explore unique hypotheses.
  • Students successfully placed their findings in a human medical context related to DNA damage.
  • 79% of students rated the workshops as highly effective (4 or 5 out of 5) for learning.

Conclusions:

  • The practical workshops effectively enhanced student learning in bioinformatics and functional genomics.
  • Using microarray data as a 'student research playground' is a valuable pedagogical approach.
  • The hands-on, hypothesis-driven methodology successfully engaged students with research methods.