clusterizebyslidingwindows

clusterizebyslidingwindows clusters transcripts using a sliding-window approach to identify highest-peak regions and export annotated features in GFF3 for analysis of transcriptomic and genomic features from next-generation sequencing (NGS) data.

Key Features:

• Sliding Window Clustering: Segments transcriptomic data into windows defined by specific size parameters and overlap criteria and clusters transcripts within those windows.
• Peak Selection: Identifies and retains the highest peaks within each clustered region, highlighting features such as transcription factor binding sites and other regulatory elements.
• GFF3 Output Format: Exports clustered regions and peak annotations in GFF3 format for compatibility with downstream genomic analyses.

Scientific Applications:

• High-throughput NGS transcriptomic analysis: Enables analysis of large-scale next-generation sequencing transcriptomic datasets by summarizing clustered features and peaks.
• Regulatory element identification: Supports detection of putative transcription factor binding sites and other regulatory elements via highest-peak selection.
• Gene expression and epigenetic studies: Facilitates characterization of genomic regions relevant to gene expression regulation and epigenetic modifications.

Methodology:

Partition the dataset using a sliding window defined by size and overlap parameters; cluster transcripts within each window; identify and retain the highest peaks per clustered region; output results in GFF3 format.