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S-leaping: an efficient downsampling method for large high-throughput sequencing data.

Hiroyuki Kuwahara¹, Xin Gao¹

¹Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

Bioinformatics (Oxford, England)

|June 24, 2023

Summary

This summary is machine-generated.

Downsampling large omics datasets is computationally challenging. We developed s-leaping, an efficient method, and fadso, a tool for FASTQ files, improving speed and reducing memory usage for omics data analysis.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Sequencing coverage is crucial for omics study design.
Downsampling is used to estimate cost-effective sequencing coverage.
Large datasets make traditional downsampling computationally intensive.

Purpose of the Study:

To develop an efficient and accurate downsampling method for large omics data.
To create a user-friendly tool for applying the method to FASTQ files.

Main Methods:

Developed an approximate downsampling algorithm named s-leaping.
Created a C-based tool called fadso for processing FASTQ data.
Compared s-leaping and fadso against existing downsampling methods.

Main Results:

s-leaping demonstrated up to 39% faster performance than existing methods with comparable accuracy.
fadso showed up to 12% increased speed and 21% lower memory usage on large datasets.
fadso achieved up to 40% higher throughput in parallel computing settings.

Conclusions:

s-leaping offers an efficient and accurate solution for downsampling large omics datasets.
fadso provides a practical and high-throughput tool for FASTQ file downsampling.
The developed methods facilitate cost-effective design and analysis of omics studies.