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Related Concept Videos

Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Related Experiment Video

Updated: Jul 5, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

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Published on: June 23, 2012

Constructing and refining multiple sequence alignments with PileUp, SeqLab, and the GCG suite.

Steven M Thompson1

  • 1Florida State University, Tallahassee, Florida, USA.

Current Protocols in Bioinformatics
|April 23, 2008
PubMed
Summary

This study demonstrates using the Accelrys GCG Wisconsin Package SeqLab for biological sequence analysis, focusing on identifying common elements within datasets. It details the application of various bioinformatics tools for sequence alignment, annotation, and data export.

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Last Updated: Jul 5, 2026

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biological sequence data analysis is crucial for understanding genetic information.
  • Efficient tools are needed for aligning, annotating, and analyzing multiple sequence datasets.
  • Identifying common patterns and elements within biological sequences aids in functional and evolutionary studies.

Purpose of the Study:

  • To illustrate the utility of the Accelrys GCG Wisconsin Package SeqLab graphical user interface for comprehensive biological sequence data management.
  • To highlight methods for discovering and recognizing common elements across multiple biological sequences.
  • To provide a practical guide for utilizing various bioinformatics tools within the SeqLab environment.

Main Methods:

  • Utilized the SeqLab graphical user interface (GUI) for data handling.
  • Employed GCG programs including LookUp, PileUp, PlotSimilarity, FASTA, Motifs, MEME/MotifSearch, Profile Package, HMMER Package, PAUPSearch, and ToFastA.
  • Integrated the non-GCG public domain program ReadSeq for enhanced functionality.

Main Results:

  • Demonstrated the capability of SeqLab to perform alignment, annotation, analysis, and export of biological sequences.
  • Successfully identified common elements and patterns within diverse biological sequence datasets.
  • Showcased the combined power of GCG and non-GCG tools for robust sequence analysis.

Conclusions:

  • The Accelrys GCG Wisconsin Package SeqLab is an effective platform for in-depth biological sequence analysis.
  • The integration of multiple bioinformatics tools facilitates the discovery of significant patterns in sequence data.
  • SeqLab provides a user-friendly approach to complex sequence data management and interpretation.