Scavenger

Scavenger recovers unaligned RNA-seq reads by identifying potential alignment locations through sequence similarity to already aligned reads, improving the sensitivity of downstream RNA-seq analyses.

Key Features:

• Recovery of Unaligned Reads: Identifies potential alignment locations for previously unmapped reads by comparing them to aligned reads and proposing new alignments based on sequence similarity.
• Python Implementation: Implemented in Python.
• Applicability to RNA-seq Types: Evaluated on simulated and real datasets, including single-cell RNA-seq, demonstrating applicability across diverse RNA-seq data types.
• Detection of Genetic Variants: Recovered reads often exhibit more genetic variants relative to the reference genome, implicating personal genomic variation in alignment failures.
• Impact on Downstream Analyses: Integration of recovered reads alters gene expression estimation and differential expression results, notably affecting lowly expressed genes such as pseudogenes.

Scientific Applications:

• Improving Read Alignment Sensitivity: Addresses false-negative non-alignment to increase the comprehensiveness of RNA-seq alignments.
• Gene Expression Profiling and Differential Expression: Enhances estimation of gene expression levels and differential expression analysis by incorporating recovered reads.
• Variant Detection and Personalized Genomics: Reveals additional genetic variants relative to the reference genome that may be missed by initial alignments.

Methodology:

Identifies potential alignment locations for unaligned reads by detecting sequence similarity with aligned reads and integrates the recovered reads into downstream analyses.