TY - JOUR AB - © 2018 The Author(s). Background: NGS data contains many machine-induced errors. The most advanced methods for the error correction heavily depend on the selection of solid k-mers. A solid k-mer is a k-mer frequently occurring in NGS reads. The other k-mers are called weak k-mers. A solid k-mer does not likely contain errors, while a weak k-mer most likely contains errors. An intensively investigated problem is to find a good frequency cutoff f 0 to balance the numbers of solid and weak k-mers. Once the cutoff is determined, a more challenging but less-studied problem is to: (i) remove a small subset of solid k-mers that are likely to contain errors, and (ii) add a small subset of weak k-mers, that are likely to contain no errors, into the remaining set of solid k-mers. Identification of these two subsets of k-mers can improve the correction performance. Results: We propose to use a Gamma distribution to model the frequencies of erroneous k-mers and a mixture of Gaussian distributions to model correct k-mers, and combine them to determine f 0 . To identify the two special subsets of k-mers, we use the z-score of k-mers which measures the number of standard deviations a k-mer's frequency is from the mean. Then these statistically-solid k-mers are used to construct a Bloom filter for error correction. Our method is markedly superior to the state-of-art methods, tested on both real and synthetic NGS data sets. Conclusion: The z-score is adequate to distinguish solid k-mers from weak k-mers, particularly useful for pinpointing out solid k-mers having very low frequency. Applying z-score on k-mer can markedly improve the error correction accuracy. AU - Zhao, L AU - Xie, J AU - Bai, L AU - Chen, W AU - Wang, M AU - Zhang, Z AU - Wang, Y AU - Zhao, Z AU - Li, J DA - 2018/12/31 DO - 10.1186/s12864-018-5272-y JO - BMC Genomics PY - 2018/12/31 TI - Mining statistically-solid k-mers for accurate NGS error correction VL - 19 Y1 - 2018/12/31 Y2 - 2024/03/28 ER -