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PR2S2Clust: Patched RNA-seq read segments' structure-oriented clustering.

Ashis Kumer Biswas¹, Jean X Gao¹

¹1 Department of Computer Science and Engineering, The University of Texas at Arlington, Texas 76019, USA.

Journal of Bioinformatics and Computational Biology

|July 27, 2016

Summary

This summary is machine-generated.

PR2S2Clust analyzes RNA-seq data to cluster non-coding RNA (ncRNA) segments based on their structural profiles. This method enhances ncRNA identification and classification by examining RNA secondary structures.

Keywords:

Patched RNA-seq read segments ensemble clustering hierarchical clustering partitional clustering

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

RNA sequencing (RNA-seq) allows deep exploration of transcriptomes, including identification and quantification of non-coding RNAs (ncRNAs).
The function of ncRNAs is intrinsically linked to their secondary structures.
Clustering RNA read segments based on structural profiles is crucial for understanding ncRNA function.

Purpose of the Study:

To introduce PR2S2Clust, an analysis platform for extracting and preparing secondary structure profiles of RNA-seq read segments.
To develop a strategy for annotating RNA segments into ncRNA classes using these structural profiles and clustering techniques.

Main Methods:

PR2S2Clust extracts features to generate secondary structure profiles for RNA-seq read segments.
The platform employs seven pairwise structural distance metrics, considering short-read mappings ('patched structures'), for clustering.
Both classical and ensemble clustering variations (partitional and hierarchical) are utilized.

Main Results:

The proposed PR2S2Clust system effectively extracts structural profiles for RNA-seq read segments.
The platform successfully annotates segments into ncRNA classes using various clustering strategies.
Experiments on real-world RNA-seq datasets demonstrate the system's effectiveness and superiority over competitive methods.

Conclusions:

PR2S2Clust provides a robust platform for structure-oriented clustering of RNA-seq read segments.
The system enhances the identification and classification of ncRNAs by leveraging their secondary structures.
The availability of source codes and datasets facilitates further research in ncRNA analysis.