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Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples

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Kenneth Lopez-Perez¹, Ramon Alain Miranda-Quintana¹

¹Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32603, United States.

Biorxiv : the Preprint Server for Biology

|April 17, 2026

Summary

This summary is machine-generated.

This study optimizes BitBIRCH clustering parameters for large molecular libraries. Data-driven strategies using similarity thresholds and iterative re-clustering enhance robustness and reduce singletons, improving analysis.

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Area of Science:

Computational chemistry
Chemoinformatics
Bioinformatics

Background:

BitBIRCH is a clustering algorithm for large molecular libraries.
Its performance can be limited by excessive singletons or large clusters.

Purpose of the Study:

To develop a data-driven strategy for optimizing BitBIRCH parameters.
To mitigate limitations like singletons and large clusters in molecular library analysis.

Main Methods:

Utilized the ChEMBL34 library for case studies.
Identified optimal similarity thresholds (3-4 standard deviations above mean).
Employed iSIM and iSIM-sigma frameworks for approximation.
Investigated the impact of branching factor (up to 1024).
Introduced an iterative re-clustering procedure.

Main Results:

Similarity thresholds between 3-4 standard deviations balance cluster count and medoid similarity.
High branching factors (e.g., 1024) significantly reduce singletons.
Iterative re-clustering allows user-defined control over cluster fusion.

Conclusions:

The proposed strategy enhances BitBIRCH's robustness for large-scale molecular clustering.
Practical guidelines are provided for optimizing BitBIRCH parameters.
Improved usability of BitBIRCH for analyzing vast molecular datasets.