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Two distinct sets of sequence data were collected.
We also note that Nuray and Savas [9] defined the sets of sequence spaces such as strongly σ-summable to zero, strongly σ-summable and strongly σ-bounded with respect to the modulus function, see [10].
It can be summarized that GO terms offer a good starting point for analyzing large sets of sequence data, but that the further analysis process should be performed critically in order to eliminate potential noise disturbing the biological relevance of the data.
In order to assess functional divergence, we generated two control data sets of sequence pairs.
In this article, we apply our algorithm on simulated sets of sequence reads of various lengths.
We used the aforementioned three sets of sequence features, and trained three SVM classification models.
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But the two sets of sequences differed in two positions.
Two sets of sequences have been used in our tests.
Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations.
These two results show that the set of rejected sequences is effective measure zero: each test is a sequence of effective open sets of sequences.
For SNP discovery, two sets of sequences were considered.
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