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Figure 10 shows the performance of our alignment method presented in Section 2 on the Watertight dataset.
which can again be converted into a standard least squares minimization problem as done in Equation 7. We summarize the method with the simple schematic shown in Figure 3. Figure 3 The general workflow of our alignment approach.
When we sat down with Earvin to talk about workforce diversity, it was a real affirmation of our alignment in what we are working towards (in addition to being a bit of a "pinch-me" moment).
Details of our alignment processing and alternative splicing detection are found in Additional file 1.
Invalid sequence (or indeed polymorphism) would affect the accuracy of our alignment calls and could have led to the inclusion of mismatched probes.
The anchoring approach that we implemented can help to find out which component of our alignment program is to blame if automatically produced alignments are biologically incorrect.
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The results of our alignments revealed one validated SNP that was conserved among highly divergent taxa.
This filtering stage reduced the average length of our alignments to 214 sites.
Very similar, well-aligned structures generally have RMS deviations <1.0 Å, indicating that all four of our alignments are precise.
One of the striking features of our alignments is that this site and downstream sequences appear to be conserved in these proteins (Fig. 5).
Because many of our alignments are relatively short, we could not confidently estimate all the parameters of Zhang et al.'s (2005) model along all lineages simultaneously.
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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