Sentence examples for build a multiple alignment from inspiring English sources

The program T-Coffee [ 25] was used to build a multiple alignment with TbDHN1 and the hits obtained by homology searches (Table 1).

The clusters were then applied to build a multiple alignment and a phylogenetic tree, which is reconciled with the species tree.

From these pairwise alignments we build a multiple alignment and generated alternative motif candidate sets by three different motif discovery strategies.

T-Coffee was used to build a multiple alignment among the TbDHN1 fungal homologs, plant LEA proteins recently classified by Wise [ 12] in Class I, Class II and Class III and fungal sequences previously reported as LEA proteins by Pfam database (Pfam Database Accession Number PF02987) (Table 3).

Searches of promoters were conducted using two original algorithms: the first to pre-select leader regions with candidate PEP-promoters (several candidates were found per region), and the second to build a multiple alignment keeping one of the candidate promoters in each of the regions.

We built a multiple alignment at the level of single repeats to define the EPTP repeat consensus sequence (Figure 5).

We then added the best TE reference sequence corresponding to each cluster, and finally built a multiple alignment.

We also tested another alternative from the TBA package called Roast which appears to implement the method described in [23] which builds a multiple alignment based on pairwise comparisons between a reference sequence and all other sequences, thus in theory requiring time linear with respect to the number of sequences, similar to Murasaki.

For each family, we used the consensus sequence constructed by RepeatScout as a query in a BLAT search of the corresponding genome to retrieve 5 10 copies with at least 20 bp of flanking sequences, built a multiple alignment, refine the consensus sequences when necessary, and manually examined sequence features (coding and noncoding) and TSD diagnostics for TE classification.

Popular methods extract gene-specific sequences from all species under examination and build a multiple sequence alignment for each gene [ 1].

The central idea of IsoRankN is to build a multiple network alignment by local partitioning of the graph of pairwise functional similarity scores.