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Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Mapping Mammalian 3D Genome Interactions with Micro-C-XL

Published on: November 3, 2023

cudaMap: a GPU accelerated program for gene expression connectivity mapping.

Darragh G McArt¹, Peter Bankhead, Philip D Dunne

¹Centre for Cancer Research and Cell Biology (CCRCB), Queen's University Belfast (QUB), Belfast, Northern Ireland, UK. s.zhang@qub.ac.uk.

BMC Bioinformatics

|October 12, 2013

Summary

This summary is machine-generated.

cudaMap software significantly accelerates cancer research by using Graphics Processing Units (GPUs) for faster connectivity mapping and drug discovery. This computational tool drastically reduces analysis time, enabling high-throughput identification of potential therapeutics.

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Area of Science:

Bioinformatics and computational biology
Genomics and transcriptomics
Cancer research and drug discovery

Background:

Modern cancer research generates large datasets requiring significant computational power and posing data accessibility challenges.
Connectivity mapping, a computational technique for drug repurposing and therapeutics discovery, is time-intensive on standard Central Processing Units (CPUs).
Traditional connectivity mapping using sscMap on CPUs can take over two hours per gene signature.

Purpose of the Study:

To introduce cudaMap, a novel software utilizing NVIDIA Graphics Processing Units (GPUs) to accelerate connectivity mapping.
To significantly reduce the computational time for analyzing large-scale gene expression data in cancer research.
To enhance the efficiency of identifying candidate therapeutics through high-throughput analysis.

Main Methods:

Implementation of cudaMap using CUDA C/C++ to leverage the parallel processing capabilities of NVIDIA GPUs.
Comparative analysis of computational performance between GPU-accelerated (cudaMap) and CPU-based (sscMap) methods.
Application of cudaMap to analyze gene signatures for therapeutics discovery.

Main Results:

cudaMap reduced connectivity mapping time to approximately 30 seconds per gene signature using an NVIDIA Tesla C2050 GPU.
Analysis of multiple gene signatures, previously taking days, was completed in as little as 10 minutes.
Demonstrated significant speed improvements with increasing computational load and accuracy requirements for statistical significance.

Conclusions:

cudaMap offers a substantial acceleration for computationally intensive connectivity mapping tasks crucial in modern cancer research.
The adoption of GPU multicore functionality, as exemplified by cudaMap, is a key strategy for accelerated local computing in biomedical research.
cudaMap is open-source, facilitating its use in accelerating the discovery of candidate therapeutics and drug repurposing.