Sentence examples for bases in the input from inspiring English sources

The alignment score for each species' eBLAT alignments shows the total number of aligning bases in the input reference DNA.

The positions of the first and last aligning bases in the input reference DNA are also noted, along with the number of sequencing gaps detected in the aligning regions of the test genomes and the total number of "Ns" (the presumed number of missing bases as indicated in the database).

For bacteria, a third readout, the color-coded EvoUnique print, highlights those bases in the input reference DNA that are unique (that do not align with any of the other genomes included in the analysis) and those bases that align with only a single other or two other genomes included in the analysis (data not shown).

For example, in Figure 4, estimating the probability of nucleotide C at "Ancestor 2" having evolved from nucleotide A at "Ancestor 1", should reflect the nucleotide equilibrium of D. melanogaster and D. simulans, given the o previous bases in the input alignment for the two species.

To assess the efficacy of eBLAT alignments in comparison to the original BLAT, we compared the pairwise alignment scores (the total number of aligning bases in the input DNA) of eBLAT to those obtained with BLAT, using 10 different intergenic regions from the Drosophila melanogaster genome.

The matching score between X and Y is computed using the following equations: M (X, Y ) = ∑ i σ (x i, y i )   and σ (a, b ) = { 1 + log ⁡ 4 (0.25 / p a ) if     a = b log ⁡ 4 (0.25 / p a p b ) otherwise where p a is the background frequency for base a in the input sequences or in the whole genome.

Within any modeling study, it is critical to test the robustness of the results based on uncertainty in the input parameters.

We show here that, together with basic information on the size, sequence and GU base pair variation in the input alignment, these features are sufficient to determine the reading direction of an RNA motif with an evolutionary conserved secondary structure.

In this study, the RNN_WL model was developed to enable coastal engineers to predict long-term water levels in a coastal inlet, based on the input of data in a remote NOAA station in the region.

The DHP is a difference of two visualizations and contains nodes and edges in the difference or intersection of the two networks based on positional similarity in the input HPs.

We introduce a novel fuzzing method based on Lévy flights in the input space in order to maximize coverage of execution paths.