Sentence examples for define an alignment from inspiring English sources

Most alignment algorithms first define an alignment graph where each node represents a set of orthologous proteins.

Note that since we are considering RNA-to-DNA alignments from an RNA contig-centric perspective (i.e. we define an alignment as a single mapping for each position in a contig sequence), we are not concerned with deletions, i.e. unaligned genomic positions flanked by aligned genomic positions, which would typically represent introns in RNA-to-DNA alignments.

We define an alignment-specific parameter ρ C that operates as a scale factor applied to all of the branch lengths in the predefined tree.

We start by defining an alignment and its annotation more formally (see Figure 4).

The constrained alignment problem is that of finding a subset of constraints, that is, a subset of edges from the bipartite similarity graph, such that the subset of edges define a legal alignment and the number of conserved edges induced by the alignment is maximum.

We define a true alignment as a situation when a read is aligned back into the same position from which it was generated.

We define a global alignment of T1, T2 as follows (Jiang et al., 1995): is a tree where nodes are labeled with pairs from (V1∪)×(V1∪).

First, we define a seed alignment of the amino acid sequences of PyrD (EC 1.3.98.1), PreA (EC 1.3.1.1) and PydA (EC 1.3.1.2) that have been structurally characterized.

We define any alignment with P value less than or equal to 0.05 as being "above noise".

We can now define a new alignment-free statistic as : \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $${ D_{2}^{q}}= \sum_{w \in \Sigma^{k}} {X_{w}^{q}} {Y_{w}^{q}}.

The features one is interested in and the way in which these features are described ultimately define the correct alignment, and in theory, given a set of sequences, each feature type may define a distinct optimal alignment.