Ai Feedback
Exact(7)
Following sequencing, all failed sequence reads, low quality sequence ends and tags were removed.
This failure mode analysis provides information on distinguishing between process and template-related attributes that may lead to downstream failed sequence.
Following sequencing, all failed sequence reads, low quality sequence ends (Avg Q25), short reads < 150 bp (final mean length 412 bp) and tags and primers were removed.
Quality criteria taken were the following: (1) failed sequence reads, (2) sequences with low quality tags, and (3) sequences that are shorter than half the expected amplicon length or 250 bp, whichever the shortest.
"Poly A tail" was the single largest cause of failed sequence in both the template-related failure category as well as the entire data set, yet the reads make up the majority in the higher failed read length bins.
These recorded observations facilitate downstream systematic analysis of failed sequence data.
Similar(53)
All failed sequences were attempted at least twice.
Amplicons that failed sequencing over 50% of the times also performed poorly during HRM analysis.
Sequencing of these amplicons failed in 341 out of 397 reactions, which accounted for 86% of all failed sequencing.
Extraneous sequences resulting from >1 template molecule per picotitre well were removed from the datasets (Table 1) as they include exact duplicates and failed sequences that are replete with uncharacterized nucleotides.
Of the designed primer pairs, only a few amplicons failed sequencing.
Write better and faster with AI suggestions while staying true to your unique style.
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