A Method article published in GenomeBiology discusses MetaBinner: a powerful binning method for recovering individual genomes from complex microbial communities.
] also applies SCG information for initializing the parameters of the clustering algorithm. However, in MaxBin, the bin number equals the number of the contigs containing the certain SCG. Note that the single-copy gene sets do not cover all the genomes in the microbial communities. In this way, some contigs from the genomes without the certain SCG may be assigned into the wrong bins, resulting in high contamination.
] applies a composition-based refinement to handle the highly contaminated or low-completion bins. Here, we only handle the highly contaminated bins. In our paper, if a bin has high contamination ), we split it by estimating the number of sub-bins of the bin using the same approach as in step 2 and running the k-means++ clustering. More details about the selection of the parameters are given in Additional fileStep 5: Incorporate the component binning results with an ensemble module.
To incorporate the component binning results efficiently and effectively, we completed the integration process in two stages. The quality of binning results obtained by different input matrices may be markedly different. In the first stage of the ensemble module, we separately integrated four component binning results generated by Step 4 for each input feature matrix. The quality of a bin is described by a bin score, which is computed as 100contamination).
The process of picking high-quality bins for the two stages is highly similar to UniteM’s greedy strategy. The main difference is that we only ran CheckM once for each domain to get the SCG information for all the contigs in step 4, instead of running it for all the component results as done in UniteM. For the same reason, the second stage does not need to run CheckM as many times as MetaWRAP.
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