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By comparing the results of a perfect assembly process to those of a widely used assembler on a number of simulated datasets, we comprehensively evaluated a host of quality metrics for de novo transcriptome assemblies.
So that said, when possible, if your genome is amenable and you've got the resources, it's going to be really valuable for you to sequence and assemble the genome, even if it's not a perfect assembly.
Hypothetically, a perfect assembly result produces nothing but subsquences of the reference sequences.
Because we simulate sequencing, we know the source location of each read and can simulate a "perfect" assembly that correctly detects all overlaps and can correct all sequencing errors.
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All reads were sequenced with a 1.5% uniform error rate (that does not affect any perfect assemblies; see Methods).
For a given dataset, the perfect assembly will be of higher quality than the non-perfect assembly.
Recently, a similar simulated sequencing and perfect assembly methodology was used by Mundry et al. to evaluate four assemblers on a single dataset [ 18].
In our example, Assembly 1.1 approximated the "perfect" assembly the best.
Zero indicated that the scaffold was impossible to be connected to any chromosome; one indicated the scaffold could be connected with no orientation; more than one indicated potential perfect assembly with an orientation.
Series of STM images (2 V tip bias 10 pA current) of three protein molecules, they form a perfect linear assembly and stabilize.
Unless otherwise specified, we simulated sequencing and perfect assembly by randomly selecting subsequences of haplotypes of a specific length.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com