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. 2022 Apr 28;13:2326. doi: 10.1038/s41467-022-29843-y

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© The Author(s) 2022

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a generate must-link constraints by breaking up contigs artificially and cannot-link constraints based on contig taxonomic annotations (i.e. GTDB reference genomes). b calculate abundance estimates (average and variance of the number of reads per base) and k-mer frequency of every contig. c train siamese neural network using the cannot-link and must-link constraints as inputs (k-mer frequencies and abundance features). The learned embedding will be the features from the output layer of the neural network and be used in step e for binning. d Based on the assumption that the number of reads per base obeys a normal distribution, calculate the Kullback-Leibler divergence of the normal distributions of two contigs. SemiBin uses this value as the abundance similarity when the number of samples used is smaller than 5. e generate a sparse graph with the embedded distance and abundance similarity between contigs and uses the Infomap algorithm to obtain the preliminary bins. f SemiBin uses weighted k-means to recluster contigs in the bins whose mean number of single-copy genes is greater than one to get the final bins.