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A structured motif allows variable length gaps between several components, where each component is a simple motif, which allows either no gaps or only fixed length gaps.
The algorithm searches for motifs expressed as simplified 'regular expressions' (consensus sequences allowing wildcards but not variable length gaps).
We look at the problem of mining structured motifs, which allow variable length gaps between simple motif components.
For each alignment, identity was calculated as the number of matches over the selected length (gaps do not count).
Rows and columns are ordered by motif similarity measured using all possible 6 bp sequences with variable length gaps in the middle.
The alignment summary presents targeted sequences accession no., species name, target length, gaps, matches and identity between query and targeted sequences.
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It should be noted that the full length GAP-43 protein localizes to presynaptic growth cones in vivo.
The use of a variable length gap between blocks allowed us to detect a significant motif that contained one of the recognition elements of the soxbox in all three clusters.
Bio.SeqIO interprets multiple sequence alignment file formats as collections of equal length (gapped) sequences.
The set S/ P stands for all the substrings of S that match pattern P. The notation x(n, m), 0 ≤ n < m, is used for a wildcard region with minimum length gap of n and maximum length gap of m, and x(n) stands for a rigid-length n gap.
Sequence similarity was calculated across the full length gapped pairwise alignment from the BLAST output, where a gap opening or base substitution counted as a mismatch.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com