Sentence examples for most alignment programs from inspiring English sources

Quality scores were converted to Sanger scale, which is compatible with most alignment programs.

FASTA is the most widely used format for sequence alignments and is output by most alignment programs.

Most alignment programs use k-tuple scores [ 4, 5] to measure the similarity of two sequences, or related word-based measures.

Most alignment reconstruction programs use a progressive approach in which most similar sequences are aligned first, following a guide-tree.

Some of the most popular alignment programs used for RNA-seq include Bowtie/TopHat [ 25], BWA [ 28], and STAR [ 29].

Thus, most structure alignment programs (e.g. DYNALIGN [ 2], FOLDALIGN [ 3], PMCOMP [ 4], or STEMLOC [ 5]) implement lightweight variants of Sankoff's algorithm, but are still computationally demanding.

Most sequence alignment programs perform equally well on RNA sequence sets with high sequence identity, that is with an average pairwise sequence identity (APSI) above 75%%.

This program is different from most other alignment programs in that it uses an evolutionary model to place insertions and deletions, with the result of minimizing the overalignment of nonhomologous regions.

Most multiple sequence alignment programs use heuristic methods rather than global optimization because identifying the optimal alignment between more than a few sequences of moderate length is prohibitively computationally expensive.

Most multi-alignment programs are using heuristic optimisation algorithms, i.e. they are, in general, not able to find the mathematically optimal alignment with respect to the objective function.

We examine this tradeoff and find that, because of a loss of information in the early steps of the approach, the alignments generated by the most common multiple sequence alignment programs are inherently unstable, and simply reversing the order of the sequences in the input file will cause a different alignment to be generated.