After the notion of non-continuous seed was presented, the spaced seed has been studied by many researchers in aspects of computational complexity [ 8- 12] as well as adapting the seeds for more specific biological sequences [ 13, 14].
Meanwhile, for human, a number of statistical analyses have already revealed that non-coding sequences (randomly selected sequences) are hierarchically organized and convey more biological information than protein-coding sequences [ 73, 74].
To potentially glean more biological information from the sequence data, we examined the GO biological process annotations for ESTs represented within B. rapa nectaries, along with 8,771 B. rapa whole flower ESTs, 8,265 B. rapa root ESTs, and 12,448 tobacco nectary ESTs currently available in dbEST.
On the one hand, biological sequences had more ATAA than non-biological sequences (1065 ATAA motifs in 30 random biological sequences versus 613 in 30 random non-biological sequences).
The latter, structural comparison, is particularly interesting since protein structures are known to be more evolutionarily conserved than the biological sequences which encode them.
In biological sequences, substitutions are more frequent than insertions and deletions.
Furthermore, our analysis suggests that it is useful to combine microarray gene expression profiles with other data sources such as ChIP-chip data or promoter sequences to extract more biological information.
Taking a more familiar example of distances between biological sequences, we know that those can be roughly estimated even without an explicit model of sequence evolution, but it is also known that, as sequences diverge, the error of the estimate becomes more and more significant.
In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information.
As the high throughput sequencing efforts generate more biological information, scientists from different disciplines are interpreting the polymorphisms that make us unique.
