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. 2021 Apr 28;11:9134. doi: 10.1038/s41598-021-88708-4

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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K-mer sequence decomposition and reconstruction. (a) Sequences can be decomposed into all possible masked 3-mers (i.e. X₁X₂X₃ separated into X₁X₂_ and X₁_X₃) as shown for PTX7. Each masked 3-mer is counted generating an 882-dimension vector (non-zero elements shown). Vectors are normalized (divided by $\sqrt{15}$ in the case of PTX7) and multiplied by their enrichment score. Principal component analysis (PCA) identifies enriched k-mers, which allow a sequence to be reconstructed. An arbitrary cut-off (0.1) can be used to minimize noise and facilitate assembly. (b) The sequence reconstruction of the first PCA component calculated from the second library selection.