Shark

Shark assigns RNA-Seq reads to a predefined panel of genes using an alignment-free, Bloom filter-based approach to enable efficient gene-specific analyses such as differential splicing.

Key Features:

• Alignment-Free Approach: Extracts reads likely derived from a specified panel of genes without performing read alignment.
• Handling Alternative Splicing: Accounts for novel alternative splicing events to maintain accurate gene assignment in spliced transcripts.
• Performance Enhancement: Filters out irrelevant reads to accelerate downstream differential splicing analysis and reduce computational load.
• Bloom Filter Utilization: Employs a Bloom filter for fast, memory-efficient separation of reads by gene origin.
• Gene Assignment Problem: Formulates and solves the computational "gene assignment" problem to identify and extract reads originating from a predefined set of genes.

Scientific Applications:

• Targeted RNA-Seq studies: Enables analyses that focus on a limited set of genes within large RNA-Seq datasets by isolating relevant reads.
• Differential splicing analysis: Serves as a preprocessing step to reduce dataset size and preserve reads from target genes for differential splicing pipelines.
• Gene-focused RNA-Seq analyses: Supports any study requiring precise assignment of reads to specific genes in RNA-Seq samples.

Methodology:

Shark formulates the gene assignment problem as identifying and extracting reads likely originating from a predefined gene set, applies an alignment-free strategy, and uses a Bloom filter to perform fast, memory-efficient read separation.