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A simple linear time approach will find the maximum scoring subsequence [ 15, 16].
The maximum scoring subsequence is roughly the same as Smith-Waterman except that it does not consider gaps.
Before we describe MaxSSmap we provide background on the maximum scoring subsequence and GPUs, CUDA, and OpenCL.
We use the maximum scoring subsequence to identify candidate genome fragments for final alignment instead of hash-tables and Burrows-Wheeler transform.
At high divergence and with gaps we expect Smith-Waterman to fare better in accuracy and error than our maximum scoring subsequence heuristic.
The maximum scoring subsequence between the two DNA sequences can now be obtained through this sequence of substitution scores [ 15, 16].
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Finally, the maximum scoring subsequences of a genome, which can then be considered as a candidate ncRNA gene, is identified.
The positions of the sites are obtained by scanning each sequence (plus strand first) until the highest scoring subsequence is found.
Intuitively, one can think about finding the optimal (maximum) score as a procedure of trying all possible sequence splitting into two subsequences (5′ end and 3′ end) and optimally aligning those subsequences.
The maximum score on the MMSE is 30.
Maximum score estimators.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com