Particles were aligned and classified using iterative rounds of reference-free alignment, removing non-ribosomal particles between each iteration.
Because of the sequence diversity among the viruses analyzed, we undertook an additional analysis on a subgroup of strains whereby we gap-stripped the alignment, removing all gaps/highly divergent regions and re-estimated the phylogeny (data not shown).
Next, we manually edited each multiple sequence alignment, removing regions of low sequence similarity where alignment errors are more likely.
Primers sequences of all ten loci used in our multilocus analysis are presented in Table 5. a Sites with indels or ambiguous alignment removed before the analyses.
Considering a probability-weighted distribution of alignments removes sensitivity to alignment errors, properly accommodates regions of alignment uncertainty, and increases the accuracy of functional element prediction.
Alignments were filtered with probcalc to eliminate weak matches (Pchance > 0.05), and short (<35 bp aligned, or < 32 matching bp) or ambiguous alignments removed with custom Perl scripts.
Pairwise alignments were performed in MEGACC (Kumar et al. 2012) with low-quality, gap-rich alignments removed manually.
We then processed the multiple alignments, removing the poly-A tails and picking the one with the greatest number of bases matching to the reference.
It is able to convert from other alignment formats, sort and merge alignments, remove PCR duplicates, generate per-position information in the pileup format (Fig. 1c), call SNPs and short indel variants, and show alignments in a text-based viewer.
It provides several core functions for in-depth analysis of evolutionary selection: retrieving the orthologous groups, generating codon-delimited and un-gapped alignments, removing recombination, building phylogenetic trees, and estimating ω under different models used by PAML/FitModel.
