Author: Jiao Chen; Jiayu Shang; Jianrong Wang; Yanni Sun
Title: A binning tool to reconstruct viral haplotypes from assembled contigs Document date: 2019_7_16
ID: 2basllfv_36
Snippet: For each experiment, we evaluate the performance of VirBin from three aspects: haplotype number estimation, clustering performance, and the computed haplotype abundance. When the originating haplotypes of the input contigs are known, we can evaluate both the recall and precision for the clustering step. First, we map the clusters to haplotypes based on the consensus haplotype label of the component contigs. If there is no consensus haplotype memb.....
Document: For each experiment, we evaluate the performance of VirBin from three aspects: haplotype number estimation, clustering performance, and the computed haplotype abundance. When the originating haplotypes of the input contigs are known, we can evaluate both the recall and precision for the clustering step. First, we map the clusters to haplotypes based on the consensus haplotype label of the component contigs. If there is no consensus haplotype membership (e.g. a tie), we map the clusters to haplotypes based on the ranking of the abundance. Let a cluster be B and its paired haplotype be H. As the input to our program is a set of contigs, let the contig set originating from H be C H . Define B ∩ C H as the common regions between the two contig sets. Following other contig binning tools (Wu et al., 2014; Lu et al., 2017) , the base-level recall for H is thus
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