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In our case, 9% of the reads did not align back to the transcript reference sequences.
Disagreements were resolved by referring back to the transcript to determine the context and the meaning.
Throughout the analysis, the others members (S. H. and Y. S). checked all data and stayed immersed in the original text by going back to the transcript several times to ensure trustworthiness [ 15].
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Any discrepancies in codes were resolved via consensus, referring back to the transcripts and discussing codes until there was 100% agreement on designated codes.
Large number of the reads (91%) aligned back to the transcripts as expected (Table 4).
The research team cross-examined the data in reverse from pen profiles back to the transcripts.
The individual sets of reads were then mapped back to the transcripts using BWA 0.5.8a [ 76].
As new clustering of themes emerged, the researchers checked back to the transcripts to ensure the connections worked for the primary source material.
Of the 13.6 M total reads sequenced, we were able to map the majority of reads, 85.6%, back to the transcripts using bowtie.
Additional themes were created during this process, which required continually going back to the transcripts to select relevant latent meaning in the text.
However, allele information should be inferred by mapping raw reads back to the transcripts from those assembled by Rnnotator, a topic that is worth more in-depth exploration.
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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