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Related Concept Videos

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

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Related Experiment Video

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A Web Tool for Generating High Quality Machine-readable Biological Pathways
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Published on: February 8, 2017

Teaching bioinformatics and neuroinformatics by using free web-based tools.

William Grisham1, Natalie A Schottler, Joanne Valli-Marill

  • 1Department of Psychology, University of California, Los Angeles, 90095, USA. grisham@lifesci.ucla.edu <grisham@lifesci.ucla.edu>

CBE Life Sciences Education
|June 3, 2010
PubMed
Summary

This module introduces bioinformatics tools for research. Students learn to connect phenotypic traits to specific genes using integrated online resources for genetic analysis.

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Area of Science:

  • Bioinformatics
  • Genetics
  • Neuroscience

Background:

  • Bioinformatics tools are crucial for modern biological research.
  • Integrating diverse data sources is essential for understanding complex biological questions.
  • Educational modules can effectively teach complex bioinformatics concepts.

Purpose of the Study:

  • To introduce students to essential bioinformatics resources and tools.
  • To demonstrate how to use integrated bioinformatic tools to answer research questions.
  • To provide a framework for teaching genetics from phenotype to molecular level.

Main Methods:

  • A computer-based module integrating multiple bioinformatics resources.
  • Utilizing online databases for anatomy (Mouse Brain Library), QTL analysis (WebQTL), gene expression (UCSC Genome Browser, NCBI Entrez Gene, Allen Brain Atlas), and literature (PubMed).
  • Guiding students through discovery of chromosomal regions, candidate genes, gene expression patterns, nucleotide sequences, and relevant literature.

Main Results:

  • Students can identify chromosomal regions linked to phenotypic traits.
  • Students can generate a list of candidate genes narrowed by expression data.
  • Students can analyze gene expression patterns and retrieve gene sequences and associated literature.

Conclusions:

  • This module effectively teaches bioinformatics resource utilization for genetic research.
  • The integrated approach allows for a comprehensive understanding of genetics from phenotypic to molecular levels.
  • Accessible teaching materials are available to support instructors and students.