Sentence examples for algorithmic alignment from inspiring English sources

The algorithmic alignment of sequences with consoles is shown in Figure 2b, and its fragment whose quality is to be assessed is given in Figure 2c.

Therefore when calculating the number of comparisons in the algorithmic alignment, we took only columns in which at least one symbol would belong to a homologous region.

Number of columns without deletions in the reference alignment: G = 9 ; Number of columns without deletions in the algorithmic alignment: A = 1 3 ; Number of common columns without deletions: I = 6 ; Accuracy: A c c u r a c y = I ∕ G = 6 ∕ 9 ; Confidence: C o n f i d e n c e = I ∕ A = 6 ∕ 1 3.

As opposed to the works mentioned above, our evaluation of algorithmic alignment methods was based not on the assessment of the alignment results for a few selected positions, but on the comparison of algorithmic alignments with the GS alignment as a whole over the total length of the sequences (see [ 14- 16]).

However, all the works cited above had a common fault: algorithmic alignments were compared not with the true evolutionary alignments (which were unknown!), but with their approximations.

Values of the measures of similarity (2.4) of algorithmic alignments via the reference alignments derived from "native" matrix and the PAM250 matrix, are practically the same (difference less than 1%, see the table in additional file 1, sheet PAM250).

However, it is important to know how closely algorithmic alignments produced through optimization of any chosen target function reflect an evolution-based alignment of the appropriate amino acid sequences, e.g. the one, which juxtaposes the positions in the compared proteins originating from the same position in their common predecessor.

However, due to the algorithmic, parameterization, alignment mode differences between the HMMER variants, the two derived false-positive rates may not be necessarily comparable for overlapping HMMER2 and HMMER3 sequence-to-domain alignments.

Using standard tools like BLAST is not adequate as its local alignment algorithmic base means it will not distinguish between isoforms which differ by the inclusion of additional exons in one isoform (see Fig 1).

At the same time, attempts to align low homologous proteins, using a matrix designed for shorter distances, significantly deteriorate the results, signifying mismatch of algorithmic and reference alignments.

Many (though not all) GNA heuristic algorithms typically achieve an alignment via two algorithmic components: node cost function (NCF) and alignment strategy (AS) [ 5– 7, 25, 26, 30, 34– 34].