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goloco: a web application to create genome scale information from surprisingly small experiments.

Sajid M Hossain^1,2, Yiyun Rao³, Jahid O Hossain⁴

¹Department of Biomedical Engineering, The Pennsylvania State University, 211 Wartik Lab, University Park, PA, 16802, USA.

BMC Bioinformatics

|February 24, 2025

Summary

This study introduces CRISPR lossy compression to simplify large-scale CRISPR screens by focusing on essential genes. A new tool, goloco, makes genome-wide functional genomics research more accessible with reduced experimental effort.

Keywords:

CRISPR screens Functional genomics Genomic inference Machine learning Web application

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Area of Science:

Genomics
Molecular Biology

Background:

CRISPR technology enables functional genomics by observing cellular changes upon gene disruption.
Comprehensive CRISPR screens are resource-intensive, limiting scalability due to millions of cells and thousands of guide RNAs.
Current methods face challenges in conducting large-scale genome-wide functional studies.

Purpose of the Study:

To develop a novel approach, "CRISPR lossy compression," for reducing CRISPR screen complexity.
To enable genome-wide screens by focusing on key genetic nodes that can infer phenotypes.
To make large-scale functional genomics studies more tractable and accessible.

Main Methods:

Proposed "CRISPR lossy compression" by selecting condensed gene sets (100-1,000 genes).
Developed goloco, an interactive web application for exploring functional genomics data.
Integrated analyses including volcano plots, regression, and network analyses within goloco.

Main Results:

Demonstrated that condensed gene sets can infer genome-wide phenotypes.
The goloco web application allows exploration of genome-scale loss-of-function phenotypes from minimal pooled measurements (as few as 100).
The tool provides diverse analytical capabilities for data interpretation.

Conclusions:

Goloco empowers researchers to conduct genome-scale functional studies efficiently.
The approach significantly reduces experimental overhead for functional genomics.
Broadens accessibility to large-scale functional genomics research for the scientific community.