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Updated: May 23, 2026

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
Published on: February 27, 2020
A new algorithm for quantifying binding site pattern similarity with applications for Next Generation Sequencing
Paul W Bible1, Rasiah Loganantharaj
1University of Louisiana, Lafayette, LA 70503, USA. pwb4552@louisana.edu
We developed PfmSim, a new algorithm for comparing transcription factor binding patterns represented as Position Frequency Matrices (PFMs). PfmSim improves the accuracy of similarity detection and classification of regulatory sequences.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Transcription factor ChIP-seq experiments provide regulatory data.
- Motif analysis of this data offers insights into biological function.
- Position Frequency Matrices (PFMs) represent transcription factor binding patterns.
Purpose of the Study:
- To develop a novel algorithm for gapped alignment of PFMs.
- To compare the developed algorithm with existing measures for similarity and classification tasks.
Main Methods:
- Developed a novel gapped alignment algorithm for PFMs, named PfmSim.
- Compared PfmSim against the Sandelin and Wasserman standard measure.
- Evaluated performance on similarity and classification tasks.
Main Results:
- PfmSim provides improved similarity values compared to the standard measure.
- The algorithm demonstrates enhanced performance in classification tasks.
Conclusions:
- PfmSim offers a more accurate method for comparing transcription factor binding patterns.
- This advancement facilitates more sophisticated detection and classification of regulatory sequences.
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