Sentence examples for alignment assuming from inspiring English sources

Either the Needleman-Wunsch [ 21] or the Smith-Waterman [ 22] algorithms used to be applied to search for the optimal alignment assuming an affine gap penalty function.

An ε-proteobacteria-specific covariance model was built based on a tRNASec sequence alignment assuming the 7/5 structure (supplementary fig. S2, Supplementary Material online).

When there is only one protein family member of both interacting partners in each species, one is guaranteed to get the correct alignment, assuming that it is a real complex in all organisms.

Since only 48% of the base pairs in the pig genome are expected to have been hit, we would only expect to hit at most 48% of the human-mouse alignment, assuming perfect conservation.

One potential solution would be to ensure that a given sequence has as its best blast hit a member of the same species or genus prior to alignment, assuming that named genera represent monophyletic groups and that such data are available.

This raw dataset holds 1,908,396 Rmaps (1 Rmap = 1 single molecule restriction map) ≥ 300 Kb, with an average size of 397.49 Kb (300 2,515.20 Kb) and a total mass of 758,574.97 Mb (~270 X coverage, before alignment, assuming a ~2.8 Gb genome).

We first analysed the ZPC interspecies alignments assuming the M0 model of no variation in ω among codons.

Then, we used this approach to predict the burying of selected positions within the multiple alignments, assuming that secondary structure predictions in these positions are accurate.

To confirm that the accuracies of the alignments by the two approaches are indistinguishable from each other and from that generated by the serial version, the BAliBASE benchmark scores were also calculated, where the differences from the reference alignments (assumed to be correct) were evaluated as the SP and TC scores (Thompson et al., 1999).

Since the true alignment A T is not known, we will consider the expected gain E A T [ G (A, A T ) | X, Y ] of alignment A, assuming that sequences X and Y were generated by our model E A T [ G (A | A T ) | X, Y ] = ∑ A T G (A, A T ) Pr (A T | X, Y ).

We defined a new log-likelihood ratio score Ψ for each alignment by assuming that Λ normally distributes with mean and variance as differing functions of n for coding and non-coding regions: where 𝒩(x|μ,σ) is the normal density and the six parameters μ C, A C, B C, μ N, A N and B N are estimated from the training dataset by maximum likelihood (see Section 3).