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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets

Huiguang Yi1,2, Yanling Lin1, Chengqi Lin2

  • 1Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.

Genome Biology
|March 17, 2021
PubMed
Summary

We developed k-mer substring space decomposition (Kssd), a faster and more accurate sketching technique for large-scale genomic data analysis. Kssd identified misidentification or contamination in over 6,000 bacterial whole genome sequencing runs.

Keywords:
Distance estimationK-merMislabeling detectionSequence comparisonSketching method

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate and efficient large-scale genomic dataset comparison is crucial for population-level analysis.
  • Current sketching methods face limitations in speed and accuracy for massive datasets.

Purpose of the Study:

  • To introduce k-mer substring space decomposition (Kssd) as a novel, superior sketching technique.
  • To enable large-scale genomic data comparisons at population resolution.
  • To identify potential misidentifications and contamination in public bacterial WGS data.

Main Methods:

  • Development of the k-mer substring space decomposition (Kssd) algorithm.
  • Application of Kssd to simulated and real genomic datasets.
  • Prioritization of references for 1,019,179 bacterial whole genome sequencing (WGS) runs from NCBI Sequence Read Archive.
  • Analysis of WGS and exome data from the 1000 Genomes Project.

Main Results:

  • Kssd demonstrated significantly improved speed and accuracy over existing sketching methods.
  • Kssd is the only method capable of large-scale dataset comparisons at population resolution.
  • Misidentification or contamination was detected in 6,164 out of 1,019,179 bacterial WGS runs.
  • Analysis of 1000 Genomes Project data was successfully performed using Kssd.

Conclusions:

  • Kssd is a highly effective tool for large-scale genomic data analysis and quality control.
  • The technique facilitates accurate population-level comparisons and identification of data anomalies.
  • This work provides a robust method for assessing the integrity of public genomic databases.