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Related Experiment Video

Updated: May 28, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

KABOOM! A new suffix array based algorithm for clustering expression data.

Scott Hazelhurst¹, Zsuzsanna Lipták

¹Wits Bioinformatics, School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, Private Bag 3, 2050 Wits, South Africa. scott.hazelhurst@wits.ac.za

Bioinformatics (Oxford, England)

|October 11, 2011

Summary

This summary is machine-generated.

A new string similarity filter efficiently clusters large expression datasets by identifying distant, exact matches, outperforming existing tools in speed and quality for bioinformatics research.

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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

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Last Updated: May 28, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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Published on: October 19, 2021

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Second-generation sequencing generates large expression datasets, posing challenges for data clustering.
Existing clustering algorithms struggle with scalability due to all-versus-all comparisons.

Purpose of the Study:

To develop a novel, efficient string similarity filter for large-scale expression data clustering.
To introduce a scalable alternative to all-versus-all comparisons in sequence analysis.

Main Methods:

A new filter based on multiple, long, and distant exact string matches.
Efficient implementation using modified suffix arrays.
Development of the wcd-express clustering tool.

Main Results:

The new filter effectively eliminates the need for all-versus-all comparisons.
The wcd-express tool demonstrates competitive performance in quality and runtime compared to existing methods.
The heuristic proves efficient for large expression datasets.

Conclusions:

The proposed string similarity filter offers a scalable and efficient solution for expression data clustering.
wcd-express provides a valuable new tool for bioinformatics research using next-generation sequencing data.