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Since it contains mainly small globular proteins with one stereotypical large binding site it can be seen as a rather "easy" dataset.
Exome sequencing offers definite advantages over whole genome sequencing: it is significantly less expensive, more easily understood for functional interpretation, significantly faster to analyze, and an easy dataset to manage.
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SNNC performs well on the easy datasets (mathcal {D}_{1}) and (mathcal {D}_{2}), but its performance on (mathcal {D}_{3}-mathcal {D}_{5}) is yet far from satisfactory.
Even using the SPNs of 512×256 pixels, (mathcal {F}) and (mathcal {A}) can be as high as 0.72 on the two easy datasets.
Using breakpoint and inversion median solvers (including MGR) again give very bad results, even for easy datasets of r = 2.
From these figures, we find that the median genomes returned by GRAPPA-TP are the closest to the true ancestors, except for the easy datasets with 100 genes and r = 4, where the DCJ median actually performs better.
Results: Our studies show that on average DACTAL yields more accurate trees than the two-phase methods we studied on very large datasets that are difficult to align, and has approximately the same accuracy on the easier datasets.
Based on these considerations, we set up the following four different datasets: (mathcal {D}_{1}): easy symmetric dataset, which consists of 1000 images taken by 25 cameras of different models (each accounting for 40 images).
(mathcal {D}_{2}): easy asymmetric dataset.
(mathcal {D}_{1}): easy symmetric dataset, which consists of 1000 images taken by 25 cameras of different models (each accounting for 40 images).
Since longer reads can only make the assembly process easier, these datasets still represent some of the hardest instances of the sequence assembly problem.
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