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Synonymous and non-synonymous differences between sequences were estimated using the DnDscan method [21] and presented and graphed using EXCEL.

Nonsynonymous (dN) and synonymous (dS) distances and their standard errors from between sequences were estimated using the modified Nei Gojobori method (Zhang et al. 1998) implemented in MEGA5 (Tamura et al. 2011).

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The genetic distance between sequences was estimated by PROTDIST under JTT amino acid substitution model.

The extent of nucleotide and amino acid divergence between sequences was estimated using uncorrected differences (p-distance) as this distance is known to give better results than more complicated methods when the number of sequences is large and the number of positions used is relatively small, because of its smaller variance [ 60].

The ω ratios between pairwise sequences were estimated following the method of Yang and Nielsen [ 24].

Genetic distances between aligned sequences were estimated according to a specific substitution model using Tree-Puzzle [ 23].

For each alignment, the non-synonymous substitution rate (K a ) and the synonymous substitution rate (K s ) between the human and macaque cDNA sequences were estimated.

Genetic distances between A. lyrata and A. thaliana intergenic sequences were estimated using the Kimura 2-parameter (K2P) model implemented in Mega 2.0.

The DNA sequence similarity among MITE01 and BmHel-2 sequences were estimated by alignment between O. nubilalis from GenBank accessions AB597304.1 (positions 1429 to 1179) and EF396411.1 (685 to 943), and B. mori accessions AP009003.1 (89798 to 90133) and AP009031.1 (54931 to 54604) using the ClustalW algorithm (Gap penalty = 10; Gap Extension - 0.2).

The nucleotide and amino acid sequence differences between the PPRV lineages for 12 complete genome sequences were estimated by using BioEdit software v7.2.0.

Nucleotide diversity, the average number of nucleotide differences per site between two sequences was estimated by using DNASP Version 4.10 [ 55].

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