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A Real-world What-Where-When Memory Test
Published on: May 16, 2017
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kmcEx: memory-frugal and retrieval-efficient encoding of counted k-mers
Peng Jiang1, Jie Luo1, Yiqi Wang1
1Precision Medicine Research Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Bioinformatics (Oxford, England)
|May 1, 2019
Summary
This study introduces a novel Bloom filter-like method to encode k-mers and their frequencies, significantly reducing memory usage for bioinformatics tasks. The approach achieves substantial memory savings and efficient retrieval, overcoming limitations of traditional k-mer counters.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- K-mers and their frequencies are fundamental in bioinformatics for tasks like genome assembly and error correction.
- Traditional k-mer counting methods produce large outputs that often exceed available memory, limiting their practical application.
Purpose of the Study:
- To develop a memory-efficient method for encoding k-mers and their frequencies.
- To improve the usability of k-mer counting by reducing memory footprint and enhancing retrieval efficiency.
Main Methods:
- A novel Bloom filter-like data structure utilizing coupled-bit arrays for k-mer representation and frequency encoding.
- Implementation and testing on five real biological datasets.
Main Results:
- Achieved an average memory-saving ratio of 13.81% for 31-mers compared to raw input, using 7 hash functions.
- Demonstrated well-controlled retrieval time complexity (effectively constant).
- Reduced the false-positive rate by two orders of magnitude.
Conclusions:
- The proposed encoding method offers significant memory savings and efficient retrieval for k-mer counting.
- This approach enhances the scalability and applicability of k-mer analysis in large-scale genomics studies.
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