Sentence examples for alignment that include from inspiring English sources
Algorithms for sequence alignment that include the possibility of inversions have been proposed [ 55- 57], and they have the potential advantage of incorporating explicit penalties for inversion events.
Nevertheless, aligning non-coding regions (or a subset of them) across divergent taxa can be difficult and may introduce error into phylogenetic analyses when alignments are not accurate, and we have attempted to minimize the possibility of this type of error by excluding from analysis all positions in the alignment that include a gap in one or more taxon.
In order to fill this gap, we propose in this work a new type of alignment, called syntenic global alignment, that includes three types of blocks: highly conserved blocks: blocks where regions with a high degree of similarity are aligned; conserved blocks: blocks where regions with a low degree of similarity are aligned; unconserved blocks: blocks where regions with no similarity are aligned.
Thus far, manual curation of a megabase-scale whole-genome multiple alignment that includes rearrangement and lateral gene transfer has proven too time-consuming and difficult.
Moreover, analysis of an alignment that included plant and stramenopile sequences indicated that new and more restrictive consensuses (GDNX7RX5P and EGDX8NX5(S/P)) could be defined for Imp1 and Imp2, respectively.
We used a multiple sequence alignment that includes both the homeodomain and the additional conserved domains (Additional file 2).
Any alignment that includes sequence from the query outside of the embedded domain includes shuffled sequence that is not homologous to the library sequence.
However, this may not be significant, given that the domain was defined on the basis of an alignment that included both archaeal and bacterial sequences.
A multiple sequence alignment that included all 12 nearly complete modern P. infestans mtDNA sequences plus all 13 herbaria samples was used as input.
2) Then for each gene, a profile hidden Markov model (HMM) [ 50] was built from a peptide sequence alignment that includes all orthologues found in the previous step.
