seqcluster

seqcluster identifies and clusters small non-coding RNAs (sRNAs) from high-throughput sequencing to quantify annotated and novel non-miRNA sRNAs and characterize expression patterns across genomic regions such as long non-coding RNAs, repeated elements, transcription start sites, and splicing site regions.

Key Features:

• Non-redundant quantification: Produces non-redundant counts of sRNA sequences to quantify all types of sRNAs including non-miRNA species.
• Multi-mapping handling: Handles sequences that map to multiple genomic locations to account for repeated elements and multi-mapping hits.
• Detection of novel non-canonical sRNAs: Identifies sRNAs derived from long non-coding RNAs, repeated elements, transcription start sites, and splicing site regions that are not annotated or predicted as microRNAs (miRNAs).
• Pattern extraction and expression profiling: Extracts expression patterns within biologically defined groups to profile sRNA expression across conditions.
• Clustering and classification of co-expressed sRNAs: Identifies and classifies co-expressed sRNA clusters to distinguish control and disease states based on putative functional importance.
• Extension of SeqBuster framework: Extends the SeqBuster framework to broaden analysis beyond annotated miRNAs to additional sRNA classes.
• Empirical application to Parkinson's disease (PD): Has been applied to post-mortem brain samples to reveal sRNA alterations associated with early pathogenic perturbations in PD.

Scientific Applications:

• Comprehensive sRNA discovery: Detection and quantification of known and novel small non-coding RNAs across diverse genomic regions.
• Expression profiling in disease: Characterization of sRNA expression changes in neurodegenerative disease research, including Parkinson's disease (PD).
• Biomarker identification: Identification of sRNA expression patterns and co-expressed clusters that may serve as early biomarkers of disease.
• Comparative and group-based analyses: Extraction of expression patterns within biologically defined groups to compare control versus diseased states.
• Genomic studies of non-canonical sRNAs: Investigation of sRNAs derived from lncRNAs, repeated elements, transcription start sites, and splice site regions in genomic research.

Methodology:

Non-redundant quantification of sRNAs; handling of multi-mapping reads; extraction of expression patterns within biological groups; clustering and classification of co-expressed sRNAs; extension of the SeqBuster framework to detect unannotated/non-miRNA sRNAs.