Abstract: MicroRNAs play an important role in regulation and disease progression, and thus are increasingly being characterized by next-generation sequencing (NGS). One of the challenges in deep sequencing of the small RNAs (small RNASeq) is in the sample preparation workflow. This workflow involves the ligation of fixed sequences, called adapters, onto the 5' and 3' ends of the starting RNA library. These adapters are prone to undesired joining to one another without a segment of the library in between, resulting in adapter dimer formation. Most library workflows use affinity capture to remove these adapter dimers, which is inefficient due to the close size similarity between adapter dimers and adapter-tagged small RNA libraries. Alternatively, a gel purification step can be used, which in consequence, can deplete low abundance sequences from the starting library. Furthermore, adapter dimers can predominate when input library concentrations are low, thus suppressing formation of the adapter-tagged library. We present a novel approach to small RNA library sample preparation using chemically-modified adapters to disfavor adapter-adapter ligation while allowing for efficient joining of adapters onto the 5' and 3' ends of the library. Using this technology, we demonstrate improvements in specificity and yield for small RNA library preparation.Summary: We present a novel approach to improve the specificity of small RNA library sample preparation. This approach uses chemically-modified adapters to disfavor adapter-adapter ligation while allowing for efficient joining of adapters onto the 5' and 3' ends of the library.
Ask the author a question about this poster.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
CD Genomics: The Strategies and Case Studies of Gut Microbiome Sequencing
Lentiviral Integration Site Sequencing – Eliminate the Uncertainty
Insight into Tumor Heterogeneity through Next-Generation Sequencing
Genomics Transforms the Future of Agriculture
How to explore cancer-related mutations using NGS