Sentence examples for by suffix tree analysis from inspiring English sources

The P. aeruginosa PA7 genome was compared to other P. aeruginosa genomes at the nucleotide level by suffix tree analysis using MUMmer [41], and the predicted PA7 CDSs were compared with the gene sets from the other sequenced P. aeruginosa genomes by BLAST using an E value cutoff of 1×10−5 and by HMM paralogous family searches using appropriate cutoffs established for each specific HMM.

The B. ovis ATCC25840 genome was compared to the genomes of B. suis 1330, B. abortus 2308, B. abortus 9-941, and B. melitensis 16M (PATRIC), at the nucleotide level by suffix tree analysis using MUMmer [81], and the predicted B. ovis CDSs were compared with the gene sets from the other sequenced Brucella genomes by BLAST and by HMM paralogous family searches, as previously described [82].

Phylogenetic tree was constructed by MEGA5.2 phylogenetic tree analysis software.

MOST and MOST+ find each word's occurrences by utilizing a suffix tree.

In the first step each simple motif is searched separately by building a suffix tree for the sequence.

But as suffix trees are memory expensive, this method would largely defeat the whole point of making suffix arrays a practical replacement for suffix trees).. (Linear time can be achieved by first building a suffix tree and traversing it to compute a suffix array, but suffix trees are memory expensive).

In section Bird's-eye view, we briefly outline the first three linear-time algorithms for direct suffix array construction: Kim et al. [ 19], Kärkkäinen and Sanders [ 20] and Ko and Aluru [ 21] (Theoretically, linear time can be achieved by first building a suffix tree and traversing it to compute a suffix array.

In principle, this complexity could be achieved by using suffix trees (Weiner, 1973) as the underlying data structure.

Various VMM approaches like context tree weighting (CTW), prediction by partial match (PPM), probabilistic suffix tree (PST) and Lempel Ziv (LZ78) algorithms for prediction of sequences based on partial sequences are well explored [10].

STEME [ 10] speeds up MEME by indexing sequences with a suffix tree.

Then, similar strings are found by traversing nodes of the corresponding suffix tree.