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Our favored method is Figure 2A, which is to ask: As genes become more biased (uneven) in codon usage, which codon(s) for each amino acid increases in frequency?
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The disadvantage of general compensation method is figured out firstly.
A detailed diagram for the sliding window method is provided (Figure 4).
Therefore, given a fixed number of top SNPs, the more chrX-npa SNPs one can find, the more powerful a method is. Figure 6 shows that iASeq clearly increased the power for detecting allele-specificity in each dataset compared to the single-dataset based analysis.
The method is daunting: Every figure needs to be manually adjusted for each frame, of which there are 24 each second, 1440 each minute.
The method is outlined in Figure 7.
The method is illustrated in Figure 1.
The method is depicted in Figure 2.
The proposed method is illustrated in Figure 2.
The framework of our method is presented in Figure 1.
The proposed allocation method is illustrated in Figure 6.
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