ProFED

ProFED performs profile filtering of count-based expression data to identify reproducible hits from pooled shRNA RNAi screens and comparative or synthetic‑lethal studies using NGS deconvolution.

Key Features:

• Profile Filtering Approach: Employs an aim-oriented profile filtering methodology that enhances descriptive data analysis and hit calling in heterogeneous count-based datasets, including outputs from NGS deconvolution.
• Versatile Design: Handles various count-based datasets beyond shRNA libraries, enabling application across different high-throughput screening setups.
• Resource Efficiency: Demonstrates that a screen depth of 100-fold average shRNA representation can yield reproducible target hits, supporting resource-saving screening strategies.

Scientific Applications:

• RNA interference (RNAi) screening: Analysis of pooled shRNA library count data to identify therapeutic targets from high-throughput RNAi screens.
• Comparative and synthetic lethal studies: Identification of genetic dependencies in contexts such as the A673 Ewing sarcoma cell line model.

Methodology:

Integration of count data from high-throughput RNAi screens using pooled shRNA libraries and NGS deconvolution; application of profile filtering techniques to manage dataset heterogeneity and improve hit calling; and statistical considerations addressing analysis of heterogeneous datasets.