Traversing the k-mer Landscape of NGS Read Datasets for Quality Score Sparsification
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Author(s)
Berger Leighton, Bonnie
Yu, Yun William
Yorukoglu, Deniz
Date Issued
April 2014
Journal
Research in Computational Molecular Biology
Publisher
Springer Nature
Citation
Yu, Y. William, et al. “Traversing the K-Mer Landscape of NGS Read Datasets for Quality Score Sparsification.” Research in Computational Molecular Biology, edited by Roded Sharan, vol. 8394, Springer International Publishing, 2014, pp. 385–99.
Version
Author's final manuscript
Abstract
It is becoming increasingly impractical to indefinitely store raw sequencing data for later processing in an uncompressed state. In this paper, we describe a scalable compressive framework, Read-Quality-Sparsifier (RQS), which substantially outperforms the compression ratio and speed of other de novo quality score compression methods while maintaining SNP-calling accuracy. Surprisingly, RQS also improves the SNP-calling accuracy on a gold-standard, real-life sequencing dataset (NA12878) using a k-mer density profile constructed from 77 other individuals from the 1000 Genomes Project. This improvement in downstream accuracy emerges from the observation that quality score values within NGS datasets are inherently encoded in the k-mer landscape of the genomic sequences. To our knowledge, RQS is the first scalable sequence-based quality compression method that can efficiently compress quality scores of terabyte-sized and larger sequencing datasets. Availability: An implementation of our method, RQS, is available for download at: http://rqs.csail.mit.edu/. © 2014 Springer International Publishing Switzerland. Keywords: RQS; quality score; sparsification; compression; accuracy; variant calling
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-05269-4_31
