Many of the SEA-ORCHID data indicators may be recommended for such purpose.
CLC, CLW, TMS and RJW carried out the sequence analysis, and CCC provided the unpublished orchid data.
Accumulation of additional orchid sequence data is needed to clarify this point.
Nevertheless, according to our experimental results for the Orchid EST data (Table 2), a smaller d T / greater d N decreased the number of non-redundant qualified siRNAs and limited the reduction of the RNAi experiments.
Considerable internal transcribed spacer (ITS) variations in Thelephoraceae (also very frequent, 30.0% of the sequence found on Aphyllorchis spp. and 63.2% on C. exigua) forbade phylogenetic analysis, but sequences were not more similar within than between orchid species (data not shown).
For example, for the orchid ESTs data, when the candidate-genes included all of the mRNA sequences and the TF sequences and no genes were excluded, our heuristic algorithm identified 3,092,859 qualified siRNAs with the parameters L = 21, d T = 4 and d N = 5.
However, due to a few of genomic data of orchids, the role of TE involved in orchid diversity and evolution has not been accessed yet.
It will be optimistic for uncovering the mystery of orchid genome evolution after more orchid genomic and transcriptomic data obtained.
Due to a lack of comprehensive sequence data for orchid bees, the antennal transcriptomes had to be assembled de novo.
This finding is consistent with BLAST data on orchid floral bud ESTs, which yielded top matches to V. vinifera followed by O. sativa [ 12, 13].
