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Scalable CNN-based classification of selective sweeps using derived allele frequencies
Sjoerd van den Belt1, Hanqing Zhao1, Nikolaos Alachiotis1
1Department of Computer Science, Faculty of EEMCS, University of Twente, 7522NB Enschede, The Netherlands.
Bioinformatics (Oxford, England)
|September 4, 2024
Summary
FAST-NN, a novel deep learning model, efficiently detects selective sweeps in genomic data without preprocessing. This machine learning approach offers a scalable and rapid solution for analyzing large datasets, outperforming existing methods.
Area of Science:
- Genetics
- Computational Biology
- Machine Learning
Background:
- Selective sweeps are crucial for understanding adaptation but are challenging to detect due to confounding factors like population bottlenecks.
- Existing methods using single nucleotide polymorphisms (SNPs) are sensitive to these factors and computationally intensive.
- Machine learning, particularly convolutional neural networks (CNNs), offers potential for robust detection but faces scalability issues with large genomic datasets.
Purpose of the Study:
- To develop a computationally efficient and scalable deep learning model for accurate selective sweep detection.
- To create a model robust to confounding evolutionary factors without requiring data preprocessing.
- To enable practical application of CNNs for large-scale genomic data analysis.
Main Methods:
- Proposed a 1D CNN-based model named FAST-NN.
- Utilized derived allele frequencies, avoiding summary statistics or raw SNP data.
- Performed neural architecture search based on the SweepNet architecture and evaluated data fusion approaches.
Main Results:
- FAST-NN achieved up to 12% higher inference accuracy than the reference architecture across challenging scenarios.
- The model demonstrates sample-size invariance and scalability.
- FAST-NN is significantly faster than existing methods: 30x-259x on CPU and 2.0x-6.2x on GPU for 128-1000 samples.
Conclusions:
- FAST-NN provides a highly accurate, computationally efficient, and scalable solution for selective sweep detection.
- The model's robustness to confounding factors and lack of preprocessing requirement make it practical for large-scale genomic studies.
- This work facilitates the widespread adoption of deep learning in population genetics research.
