Protocol for sequence clustering with PaSiMap in Jalview

Thomas Morell; James Procter; Geoffrey J. Barton; Kay Diederichs; Olga Mayans; Jennifer R. Fleming

doi:10.1016/j.xpro.2025.103603

Protocol for sequence clustering with PaSiMap in Jalview

Thomas Morell, James Procter, Geoffrey J. Barton, Kay Diederichs, Olga Mayans, Jennifer R. Fleming (Lead / Corresponding author)

Computational Biology

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Abstract

Pairwise similarity mapping (PaSiMap), implemented in Jalview, serves as an alternative to principal component analysis (PCA) for analyzing protein sequence relationships without requiring pre-computed multiple sequence alignments. It excels at distinguishing between systematic and random differences in datasets. Here, we present a protocol for sequence clustering with PaSiMap in Jalview. We describe steps for installation, sequence importing, and running PaSiMap analyses. We also detail procedures for plotting and data interpretation in RStudio, offering a streamlined approach for visualizing and analyzing protein sequence relationships effectively.

Original language	English
Article number	103603
Number of pages	15
Journal	STAR Protocols
Volume	6
Issue number	1
Early online date	27 Mar 2025
DOIs	https://doi.org/10.1016/j.xpro.2025.103603
Publication status	Published - 27 Mar 2025

Keywords

bioinformatics
computer sciences
sequence analysis

ASJC Scopus subject areas

General Neuroscience
General Biochemistry,Genetics and Molecular Biology
General Immunology and Microbiology

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10.1016/j.xpro.2025.103603Licence: CC BY-NC

1-s2.0-S2666166725000097-main
STAR Protocols 6, 103603, March 21, 2025 ª 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/)
Final published version, 4.53 MBLicence: CC BY-NC

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AU - Mayans, Olga

AU - Fleming, Jennifer R.

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Protocol for sequence clustering with PaSiMap in Jalview

Abstract

Keywords

ASJC Scopus subject areas

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