ssviz

ssviz visualizes and analyzes small RNA sequencing data to support exploration and interpretation of microRNAs, piRNAs, and other noncoding small RNAs, including in organisms without prior genome annotation.

Key Features:

• Visualization and analysis: Provides visual representations and analytical summaries of small RNA sequencing results focused on microRNAs and piRNAs.
• Preprocessing compatibility: Designed to work with common small RNA preprocessing conventions including adapter trimming, contaminant removal, and collapsing identical reads while preserving original abundances.
• Readname–readcount support: Incorporates readname–readcount structures (for example, output from fastx-toolkit) directly into plots and downstream computations.
• Contaminant mapping: Maps reads to contaminant classes such as snoRNAs, snRNAs, and tRNAs for contaminant identification and filtering.
• Genome and database alignment: Integrates alignments to the genome and to small RNA databases such as miRBank and RepBase.
• Specialized miRNA/piRNA analyses: Includes analytical features tailored specifically to miRNA- and piRNA-focused studies.
• Quality assessment and annotation support: Supports quality assessment, annotation, and interpretation of small RNA profiles across datasets.

Scientific Applications:

• miRNA profiling and annotation: Analysis and visualization of microRNA abundance and annotation across samples and conditions.
• piRNA characterization: Visualization and analysis tailored to piRNA-focused studies and their population profiles.
• Contaminant identification: Identification and assessment of reads deriving from contaminant classes such as snoRNAs, snRNAs, and tRNAs.
• Small RNA-seq quality assessment: Assessment of preprocessing outcomes and mapping results to inform data quality and downstream interpretation.
• Analysis in non-model organisms: Exploration and interpretation of small RNAs in organisms lacking prior genome annotation.

Methodology:

Uses adapter trimming, removal of contaminants, collapsing identical reads while preserving original abundances, incorporation of readname–readcount structures (e.g., fastx-toolkit), mapping to contaminant classes (snoRNAs, snRNAs, tRNAs), and alignment to the genome and small RNA databases such as miRBank and RepBase, with these inputs incorporated into visualizations and downstream computations.