abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis
Omer Ali1, Amna Farooq1, Mingyi Yang2,3
1Department of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Oslo, Norway.
BMC Bioinformatics
|March 4, 2022
Summary
A new Python package, abc4pwm, efficiently clusters transcription factor (TF) binding motifs represented as position weight matrices (PWMs). It also assesses cluster quality and aids in DNA sequence analysis for various high-throughput sequencing data.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Transcription factor (TF) binding motifs are crucial for understanding protein-DNA interactions, often represented as position weight matrices (PWMs).
- Increasing numbers of TF motifs from diverse sources lead to redundancy and challenges in management and analysis.
- Existing tools lack efficient methods for clustering PWMs and assessing the quality of these clusters.
Purpose of the Study:
- To develop an efficient tool for clustering transcription factor (TF) binding motifs (PWMs).
- To enable automatic assessment of PWM cluster quality and filtering of erroneous clusters.
- To facilitate TF motif searching, discovery, and integration with high-throughput sequencing data analysis.
Main Methods:
- Development of a Python package named Affinity Based Clustering for Position Weight Matrices (abc4pwm).
- Implementation of clustering algorithms for PWMs, with optional integration of DNA-Binding Domain (DBD) information.
- Development of methods for automatic cluster quality evaluation, representative motif generation, and PWM filtering.
- Incorporation of an ensemble learning approach for TF motif searching and discovery.
Main Results:
- The abc4pwm package efficiently clusters PWMs from multiple sources, generating representative motifs for each cluster.
- It automatically evaluates clustering quality and filters out incorrectly clustered PWMs.
- The tool successfully updated a human DBD family database, classified human TF PWMs, and performed motif searching and discovery.
Conclusions:
- The developed abc4pwm tool is effective for TF motif searching, clustering, and quality assessment.
- It demonstrates utility in DNA sequence analysis across various high-throughput sequencing data types, including RNA-seq, ChIP-seq, and ATAC-seq.
- abc4pwm aids in recovering known TF motifs and identifying true TF binding targets from experimental data.
Keywords:
Clustering quality assessmentDNA sequence analysisDNA-binding domainMotif searchingPosition weight matricesTranscription factorMore Related Videos
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