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Open Access Short report

Reducing ligation bias of small RNAs in libraries for next generation sequencing

Karim Sorefan1, Helio Pais2, Adam E Hall1, Ana Kozomara3, Sam Griffiths-Jones3, Vincent Moulton2 and Tamas Dalmay1*

Author Affiliations

1 School of Biological Sciences, University of East Anglia, Norwich, NR4 7TJ, UK

2 School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK

3 Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK

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Silence 2012, 3:4  doi:10.1186/1758-907X-3-4

Published: 30 May 2012

Abstract

Background

The use of nucleic acid-modifying enzymes has driven the rapid advancement in molecular biology. Understanding their function is important for modifying or improving their activity. However, functional analysis usually relies upon low-throughput experiments. Here we present a method for functional analysis of nucleic acid-modifying enzymes using next generation sequencing.

Findings

We demonstrate that sequencing data of libraries generated by RNA ligases can reveal novel secondary structure preferences of these enzymes, which are used in small RNA cloning and library preparation for NGS. Using this knowledge we demonstrate that the cloning bias in small RNA libraries is RNA ligase-dependent. We developed a high definition (HD) protocol that reduces the RNA ligase-dependent cloning bias. The HD protocol doubled read coverage, is quantitative and found previously unidentified microRNAs. In addition, we show that microRNAs in miRBase are those preferred by the adapters of the main sequencing platform.

Conclusions

Sequencing bias of small RNAs partially influenced which microRNAs have been studied in depth; therefore most previous small RNA profiling experiments should be re-evaluated. New microRNAs are likely to be found, which were selected against by existing adapters. Preference of currently used adapters towards known microRNAs suggests that the annotation of all existing small RNAs, including miRNAs, siRNAs and piRNAs, has been biased.

Keywords:
Next generation sequencing; MicroRNA; Small RNA; MiRBase; Expression profile; Deep sequencing; T4 RNA ligase