bcSeq

bcSeq maps high-throughput sequencing reads from CRISPR-Cas9 and shRNA screens by modeling Phred score-based sequencing errors and resolving ambiguous barcode mappings to support gene-function and genetic-interaction studies.

Key Features:

• R-based implementation: Implemented as an R package for integration with R-based bioinformatics workflows.
• Error tolerance: Incorporates an algorithm to manage sequencing errors inherent in high-throughput data to improve read mapping accuracy.
• Statistical error modeling (Phred scores): Employs a statistical model based on Phred scores to evaluate and manage sequencing-quality-related errors.
• Ambiguous mapping resolution (Trie): Uses a Trie data structure to resolve ambiguous mappings among similar sequences in barcode libraries.
• Computational efficiency (Trie): Leverages the Trie structure to enable rapid and efficient sequence lookup and mapping in large datasets.
• Parallelization: Supports parallelized computation to process multiple reads simultaneously for higher throughput.

Scientific Applications:

• CRISPR-Cas9 and shRNA screening: Mapping barcode and guide RNA sequencing reads from high-throughput CRISPR-Cas9 and shRNA screens.
• Gene-function and genetic-interaction studies: Providing accurate read mappings that support downstream analysis of gene function and genetic interactions.

Methodology:

bcSeq applies a Trie data structure for sequence mapping and ambiguous-resolution, a Phred score-based statistical error model to assess sequencing quality, an error-tolerant mapping algorithm, and parallelized processing.