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Let H S A m (T, S) denote the optimal alignment cost of trees T and S in alignment mode m, where m is one of the following modes: Rooted-Ordered, Rooted-Unordered, Unrooted-Ordered or Unrooted-Unordered.
Denote by HSA T, S) the minimum alignment cost of an HSA between T and S, and call an HSA A optimal with respect to T and S if w(T, S, A) = HSA T, S).
Similar(58)
We used a relative score formula described by Höchsmann et al. [ 48] to assess the similarity of two trees, normalizing the alignment cost by the average of the self-alignment costs of the compared trees.
To compute the temporal variability of the measurements, different values as the length of the strokes, the variability of the mean and standard deviation of the measures, alignment costs referenced to the signature of the first day, etc. are obtained.
Compute all of the minimum pairwise alignment costs c ij using dynamic programming.
Sequence alignment costs and base-pair alignment costs were set using the RIBOSUM85-60 scoring matrix [ 49].
These parameters were selected to maximize the probability of finding the correct alignment at the cost of increased runtime, which is especially important for the B73 genome given its high repetitive content.
The resulting reciprocal map is identical to the one derived from the mouse-human alignments at the cost of losing around 10% sequence coverage on both species [ 29].
A recent benchmark test highlighted that filtering using BWA generated mapping qualities with a stringent threshold removed more than 80% of the false alignments at the cost of 1% loss in sensitivity compared with the unique mapping strategy [ 80, 82].
We evaluated the alignment accuracy and computational cost of nine different multiple sequence alignment programs with the BAliBASE dataset and discuss the relevance of some implementations embedded in the programs algorithms.
The computational cost of alignment can be reduced by pre-aligning shapes, as is done with the Volumetric-Aligned Molecular Shapes (VAMS) method.
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