Sentence examples for multiple guide trees from inspiring English sources

The use of multiple guide trees is a particularly important feature in microbial genomes, which are subject to lateral gene transfer.

We show how this approach greatly reduces computation time and memory requirements for clustering large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for multiple alignments.

In this paper we look at the use of variations on SeedMap specifically for making guide trees for multiple alignment.

For our purposes, we were mainly interested in a fast way of generating guide trees for multiple alignment, especially for future versions of the ClustalW package.

When applying UPGMA to the outputs from SparseMap embeddings and using these clusterings as guide trees for multiple alignments we found (results not shown) considerable differences between runs, and these differences increase as more divergent sequences are included.

We show how this approach greatly reduces computation time and memory requirements for clustering large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for multiple alignment.

This could crucially improve the runtime of multiple-alignment algorithms, as calculating guide trees becomes the most time-consuming step in progressive alignment if the number of sequences grows.

Alignment-free methods are also used to construct guide trees for progressive multiple alignment (Blackshields et al., 2010; Edgar, 2004; Katoh et al., 2002).

This paper examines the impact of the tradeoff of accuracy for speed in the construction of the guide trees in protein progressive multiple sequence alignment.

We then try to measure the success of the overall procedure by (i) tree comparison and (ii) comparing the multiple sequence alignments that are generated using guide trees from embedded distance matrices with those generated from full sequence distance matrices.

The use of guide trees was a significant advance for multiple sequence alignment in sequence bioinformatics, and methods such as CLUSTAL-W 34) CLUSTAL-W 34 widespread popularity in nucleic acid and protein sequence alignment.