DSAP

DSAP processes deep-sequencing small RNA datasets from next-generation sequencing technologies, including Solexa platform data, to identify and quantify microRNAs and other transcribed non-coding RNAs.

Key Features:

• Input Format: Accepts tab-delimited files containing unique sequence reads (tags) with corresponding copy numbers from deep-sequencing experiments.
• Cleanup: Removes adaptors and poly-nucleotide sequences (A/T/C/G/N) from raw reads.
• Clustering: Groups cleaned sequence tags into unique clusters to represent distinct RNA species using clustering algorithms.
• Non-coding RNA Matching: Maps sequence homologies to the Rfam database to identify transcribed non-coding RNAs.
• Known miRNA Matching: Detects known microRNAs by sequence-similarity comparisons against miRBase.
• Expression Visualization: Summarizes ncRNA and miRNA expression using multi-color bar charts and a log(2)-scaled color matrix.
• Cross-Species Comparison: Compares identified miRNAs across species entries cataloged in miRBase to assess conservation and divergence.

Scientific Applications:

• Gene Regulation Studies: Identification and quantification of miRNAs and ncRNAs to support analyses of gene expression regulation.
• Comparative Genomics: Cross-species miRNA comparison to explore evolutionary conservation and divergence of small RNAs.
• Functional Annotation: Functional annotation of novel RNA sequences through homology mapping against Rfam and miRBase.

Methodology:

Processing steps explicitly include adaptor and poly-nucleotide (A/T/C/G/N) removal, clustering of cleaned sequence tags using clustering algorithms, and sequence homology mapping to Rfam and miRBase for ncRNA and known miRNA identification, with expression summarized in log(2)-scaled matrices and bar charts.