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_28
Snippet: The iterative clustering algorithm contains four steps as shown below: Initialization: Initialize N groups by randomly assign sub-contigs to them. Updating the abundance distribution E i : For each bin i, the component sub-contigs' relative abundance profiles cs are aggregated to calculate the empirical probability density function E i . The aggregation is performed by calculating the normalized histograms for these relative abundance profiles, s.....
Document: The iterative clustering algorithm contains four steps as shown below: Initialization: Initialize N groups by randomly assign sub-contigs to them. Updating the abundance distribution E i : For each bin i, the component sub-contigs' relative abundance profiles cs are aggregated to calculate the empirical probability density function E i . The aggregation is performed by calculating the normalized histograms for these relative abundance profiles, so that the summation of histogram values will be 1. Re-assignment of the sub-contigs: Once E i is derived, the relative likelihood of c j being produced from the ith prototype distribution can be calculated as E i (c j ). The prior probability of each bin (or haplotype) is a weighted sum of the likelihoods of all the component sub-contigs. The weights are determined by the total bases in the sub-contigs. The prior probability P r(H i ) for bin i is:
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