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Updated: May 24, 2025

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Protocol for sequence clustering with PaSiMap in Jalview.

Thomas Morell1, James Procter2, Geoffrey J Barton2

  • 1Department of Biology, University of Konstanz, 78457 Konstanz, Germany.

STAR Protocols
|February 28, 2025
PubMed
Summary
This summary is machine-generated.

Pairwise similarity mapping (PaSiMap) in Jalview offers a novel method for protein sequence analysis without multiple sequence alignments. This protocol details PaSiMap for effective sequence clustering and visualization.

Keywords:
bioinformaticscomputer sciencessequence analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Bioinformatics

Background:

  • Principal Component Analysis (PCA) is commonly used for analyzing protein sequence relationships.
  • PCA often requires pre-computed multiple sequence alignments, which can be a limitation.
  • Distinguishing systematic from random differences in sequence data is crucial for accurate analysis.

Purpose of the Study:

  • To present a detailed protocol for sequence clustering using Pairwise Similarity Mapping (PaSiMap) in Jalview.
  • To provide a user-friendly method for analyzing protein sequence relationships.
  • To offer an alternative to PCA that does not require multiple sequence alignments.

Main Methods:

  • Implementation of PaSiMap within the Jalview software platform.
  • Step-by-step guide covering installation, sequence data import, and PaSiMap analysis execution.
  • Utilizing RStudio for downstream data visualization and interpretation of PaSiMap results.

Main Results:

  • PaSiMap effectively distinguishes between systematic and random variations in protein sequence datasets.
  • The protocol enables streamlined visualization and analysis of protein sequence relationships.
  • Successful application of PaSiMap for sequence clustering and relationship mapping.

Conclusions:

  • PaSiMap in Jalview provides a powerful and accessible tool for protein sequence analysis.
  • This protocol facilitates the effective clustering and interpretation of protein sequence data.
  • The method offers a valuable alternative for researchers studying protein sequence evolution and function.