An extension to: Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing has emerged as the favoured method to study microRNA (miRNA) expression, but biases introduced during library preparation have been reported. To assist researchers choose the most appropriate library preparation kit, we recently compared the performance of six commercially-available kits on synthetic miRNAs and human RNA, where library preparation was performed by the vendors. We hereby supplement this study with data from two further commonly used kits (NEBNext, NEXTflex) whose manufacturers initially declined to participate. As before, performance was assessed with respect to sensitivity, reliability, titration response and differential expression. Despite NEXTflex employing partially-randomised adapter sequences to minimise bias, we reaffirm that biases in miRNA abundance are kit-specific, complicating the comparison of miRNA datasets generated using different kits.

[1]  S. Luo,et al.  RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries. , 2011, RNA.

[2]  Cole Trapnell,et al.  Ultrafast and memory-efficient alignment of short DNA sequences to the human genome , 2009, Genome Biology.

[3]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[4]  Haifan Lin,et al.  MicroRNAs: key regulators of stem cells , 2009, Nature Reviews Molecular Cell Biology.

[5]  Ryan T Fuchs,et al.  Bias in Ligation-Based Small RNA Sequencing Library Construction Is Determined by Adaptor and RNA Structure , 2015, PloS one.

[6]  C. Croce,et al.  Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[7]  H. zur Hausen The role of microRNAs in human cancer , 2007 .

[8]  Ana Kozomara,et al.  Reducing ligation bias of small RNAs in libraries for next generation sequencing , 2012, Silence.

[9]  S. Abramson,et al.  The role of microRNA in rheumatoid arthritis and other autoimmune diseases. , 2010, Clinical immunology.

[10]  Klaus Rajewsky,et al.  MicroRNA Control in the Immune System: Basic Principles , 2009, Cell.

[11]  E. Cuppen,et al.  Limitations and possibilities of small RNA digital gene expression profiling , 2009, Nature Methods.

[12]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[13]  Arvind Y. M. Sundaram,et al.  Systematic assessment of commercially available low-input miRNA library preparation kits , 2019, bioRxiv.

[14]  D. Bartel,et al.  MicroRNAs Modulate Hematopoietic Lineage Differentiation , 2004, Science.

[15]  Renee Rubio,et al.  Comprehensive multi-center assessment of accuracy, reproducibility and bias of small RNA-seq methods for quantitative miRNA profiling , 2018 .

[16]  Ryan T Fuchs,et al.  Structural bias in T4 RNA ligase-mediated 3′-adapter ligation , 2012, Nucleic acids research.

[17]  Marcel Martin Cutadapt removes adapter sequences from high-throughput sequencing reads , 2011 .

[18]  V. Ambros,et al.  Expression profiling of mammalian microRNAs uncovers a subset of brain-expressed microRNAs with possible roles in murine and human neuronal differentiation , 2004, Genome Biology.

[19]  Leming Shi,et al.  Using RNA sample titrations to assess microarray platform performance and normalization techniques , 2006, Nature Biotechnology.

[20]  R. Sachidanandam,et al.  Identification and remediation of biases in the activity of RNA ligases in small-RNA deep sequencing , 2011, Nucleic acids research.