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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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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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Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Tools for managing and analyzing microarray data.

André Koschmieder1, Karin Zimmermann, Silke Trissl

  • 1Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, 12489 Berlin, Germany. koschmie@informatik.hu-berlin.de

Briefings in Bioinformatics
|March 23, 2011
PubMed
Summary
This summary is machine-generated.

Selecting microarray data management software is challenging due to numerous options. This survey guides researchers by evaluating tool functionality, usability, and maintenance, highlighting gaps in integrated analysis capabilities.

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Area of Science:

  • Biomedical research
  • Bioinformatics
  • Genomics

Background:

  • Microarray gene expression analysis generates vast, diverse datasets.
  • Hundreds of software tools exist for managing this data, complicating selection.
  • Choosing appropriate microarray data management systems is difficult for researchers.

Purpose of the Study:

  • To survey and evaluate existing microarray data management systems.
  • To provide a guideline for selecting suitable software tools.
  • To identify strengths and weaknesses of current systems, particularly in integrated analysis.

Main Methods:

  • Surveyed 78 microarray data management tools.
  • Inspected 22 tools in detail.
  • Performed hands-on testing of 7 selected tools.

Main Results:

  • Evaluated tools based on functionality, ease-of-use, installation, and maintenance.
  • Identified mature tools covering specific tasks.
  • Found existing systems inadequately meet requirements for integrated analysis of diverse experimental data.

Conclusions:

  • The survey offers a valuable guideline for selecting microarray data management systems.
  • Current software landscape has gaps, especially in comprehensive integrated data analysis.
  • Further development is needed for systems supporting complex, multi-data-type analyses.