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As the author explains in his preface, modern readers, whether scientists, students of the humanities, or interested laypersons, often have difficulties reading Origin for a wide variety of reasons.
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I have now read Origin several times.
Presumably, this will all be over when Carson slips up and tells people he read Origin of Species, and kind of liked it.
In order to identify bulk specific contigs, BR and BS reads were assembled together but the information about the read origin was retained.
The IsoEM algorithm (see Figure 2) overcomes this difficulty by simultaneously estimating the frequencies and imputing the missing read origin within an iterative framework.
As read 2 mapped to more than one feature, we addressed this issue of read origin by defining prorating such that we considered any read with multiple alignments and features associated with that alignment.
The contrasts between accession-coverage frequencies for the two kinds of read origin were very similar for the two RAD libraries, with large numbers of ncRS sites represented in only one accession but many cRS sites represented in all of the accessions.
We compared the performance of both pipelines in labeling read origins.
In default of standards for encoding read origins, every evaluation tool has to be made explicitly compatible with the simulator used to generate reads.
In many cases, the RNA-Seq reads are first aligned to a reference genome using an alignment tool that identifies possible read origins within the genome.
Intuitively, the denominator is associated with the likelihood that the two reads come from the same haplotype and the numerator is associated with the likelihood that the reads' origins are different.
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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