Comprehensive assessment of multiple biases in small RNA sequencing reveals significant differences in the performance of widely used methods
Carrie Wright; Anandita Rajpurohit; Emily E. Burke; Courtney Williams; Leonardo Collado‐Torres; Martha Kimos; Nicholas J. Brandon; A.J. Cross · 2019 · BMC Genomics
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: RNA sequencing offers advantages over other quantification methods for microRNA (miRNA), yet numerous biases make reliable quantification challenging. Previous evaluations of these biases have focused on adapter ligation bias with limited evaluation of reverse transcription bias or amplification bias. Furthermore, evaluations of the quantification of isomiRs (miRNA isoforms) or the influence of starting amount on performance have been very limited. No study had yet evaluated the quantification of isomiRs of altered length or compared the consistency of results derived from multiple
Abstract by Carrie Wright; Anandita Rajpurohit; Emily E. Burke; Courtney Williams; Leonardo Collado‐Torres; Martha Kimos; Nicholas J. Brandon; A.J. Cross, BMC Genomics (2019) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1186/s12864-019-5870-3