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[Preprint]. 2023 Mar 10:2023.03.07.531597.
doi: 10.1101/2023.03.07.531597.

Torch-eCpG: A fast and scalable eQTM mapper for thousands of molecular phenotypes with graphical processing units

Kord M Kober  1   2   3 , Liam Berger  1 , Ritu Roy  1   2 , Adam Olshen  2   3
Affiliations

Torch-eCpG: A fast and scalable eQTM mapper for thousands of molecular phenotypes with graphical processing units

Kord M Kober et al. bioRxiv. .

Update in

Abstract

Background: Gene expression may be regulated by the DNA methylation of regulatory elements in cis, distal, and trans regions. One method to evaluate the relationship between DNA methylation and gene expression is the mapping of expression quantitative trait methylation (eQTM) loci (also called expression associated CpG loci, eCpG). However, no open-source tools are available to provide eQTM mapping. In addition, eQTM mapping can involve a large number of comparisons which may prevent the analyses due to limitations of computational resources. Here, we describe Torch-eCpG, an open-source tool to perform eQTM mapping that includes an optimized implementation that can use the graphical processing unit (GPU) to reduce runtime.

Results: We demonstrate the analyses using the tool are reproducible, up to 18x faster using the GPU, and scale linearly with increasing methylation loci.

Conclusions: Torch-eCpG is a fast, reliable, and scalable tool to perform eQTM mapping. Source code for Torch-eCpG is available at https://github.com/kordk/torch-ecpg.

Keywords: DNA methylation; GPU; eCpG; eQTM; expression quantitative trait methylation; gene expression; tensor; transcriptional regulation.

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Conflict of interest statement

Competing interests: The authors have no competing interests.

Figures

Figure 1.
Figure 1.
Comparisons of the first 1000 CpG-transcript pair linear regression analyses results between tecpg and lm() function in the stats package in R for a simulated dataset generated by sampling with replacement (n=1000 samples). Simulated patient data was generated from real patient data in the Grady Trauma Project.
Figure 2.
Figure 2.
Performance of GPU implementations for eQTM mapping. Comparison of runtimes for tecpg analyses on CPU and GPU for (A) cis-eCpG, (B) distal-eCpG, and (C) trans-eCpG. The analyses evaluated 340 patients from the Grady Trauma Project dataset and included 422,442 methylation loci and 17,653 genes.
Figure 3.
Figure 3.
CPU runtimes for tecpg using 1, 2, 4, 8, 16, and 24 CPU cores. The analyses evaluated 340 patients from the Grady Trauma Project dataset and included 422,442 methylation loci and 17,653 genes.
Figure 4.
Figure 4.
GPU runtime of tecpg for 1000 simulated patient samples for 20,000 genes and 20×103, 100 ×103, 250 ×103, 450 ×103 and 850 ×103 CpG loci. Simulated patient data was generated from real patient data in the Grady Trauma Project.
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