Literature DB >> 24440557

Library preparation methods for next-generation sequencing: tone down the bias.

Erwin L van Dijk1, Yan Jaszczyszyn2, Claude Thermes3.   

Abstract

Next-generation sequencing (NGS) has caused a revolution in biology. NGS requires the preparation of libraries in which (fragments of) DNA or RNA molecules are fused with adapters followed by PCR amplification and sequencing. It is evident that robust library preparation methods that produce a representative, non-biased source of nucleic acid material from the genome under investigation are of crucial importance. Nevertheless, it has become clear that NGS libraries for all types of applications contain biases that compromise the quality of NGS datasets and can lead to their erroneous interpretation. A detailed knowledge of the nature of these biases will be essential for a careful interpretation of NGS data on the one hand and will help to find ways to improve library quality or to develop bioinformatics tools to compensate for the bias on the other hand. In this review we discuss the literature on bias in the most common NGS library preparation protocols, both for DNA sequencing (DNA-seq) as well as for RNA sequencing (RNA-seq). Strikingly, almost all steps of the various protocols have been reported to introduce bias, especially in the case of RNA-seq, which is technically more challenging than DNA-seq. For each type of bias we discuss methods for improvement with a view to providing some useful advice to the researcher who wishes to convert any kind of raw nucleic acid into an NGS library.
Copyright © 2014 Elsevier Inc. All rights reserved.

Keywords:  Bias; DNA-seq; Library preparation; Next-generation sequencing; RNA-seq

Mesh:

Substances:

Year:  2014        PMID: 24440557     DOI: 10.1016/j.yexcr.2014.01.008

Source DB:  PubMed          Journal:  Exp Cell Res        ISSN: 0014-4827            Impact factor:   3.905


  136 in total

1.  Automated Gel Size Selection to Improve the Quality of Next-generation Sequencing Libraries Prepared from Environmental Water Samples.

Authors:  Miguel I Uyaguari-Diaz; Jared R Slobodan; Matthew J Nesbitt; Matthew A Croxen; Judith Isaac-Renton; Natalie A Prystajecky; Patrick Tang
Journal:  J Vis Exp       Date:  2015-04-17       Impact factor: 1.355

2.  A comparison of unamplified and massively multiplexed PCR amplification for murine antibody repertoire sequencing.

Authors:  Trisha A Rettig; Michael J Pecaut; Stephen K Chapes
Journal:  FASEB Bioadv       Date:  2018-08-30

3.  Detection and quantitative estimation of spurious double stranded DNA formation during reverse transcription in bacteria using tagRNA-seq.

Authors:  Nicolas Innocenti; Francis Repoila; Erik Aurell
Journal:  RNA Biol       Date:  2015       Impact factor: 4.652

Review 4.  Use of the nuclear walk-on methodology to determine sites of RNA polymerase II initiation and pausing and quantify nascent RNAs in cells.

Authors:  Christopher B Ball; Kyle A Nilson; David H Price
Journal:  Methods       Date:  2019-02-08       Impact factor: 3.608

5.  A Small RNA-Seq Protocol with Less Bias and Improved Capture of 2'-O-Methyl RNAs.

Authors:  Erwin L van Dijk; Claude Thermes
Journal:  Methods Mol Biol       Date:  2021

6.  Novel ribonuclease activity of cusativin from Cucumis sativus for mapping nucleoside modifications in RNA.

Authors:  Balasubrahmanyam Addepalli; Sarah Venus; Priti Thakur; Patrick A Limbach
Journal:  Anal Bioanal Chem       Date:  2017-07-20       Impact factor: 4.142

Review 7.  Reference standards for next-generation sequencing.

Authors:  Simon A Hardwick; Ira W Deveson; Tim R Mercer
Journal:  Nat Rev Genet       Date:  2017-06-19       Impact factor: 53.242

8.  Robust statistical modeling improves sensitivity of high-throughput RNA structure probing experiments.

Authors:  Alina Selega; Christel Sirocchi; Ira Iosub; Sander Granneman; Guido Sanguinetti
Journal:  Nat Methods       Date:  2016-11-07       Impact factor: 28.547

Review 9.  Detecting Rare Mutations and DNA Damage with Sequencing-Based Methods.

Authors:  Daniel B Sloan; Amanda K Broz; Joel Sharbrough; Zhiqiang Wu
Journal:  Trends Biotechnol       Date:  2018-03-14       Impact factor: 19.536

Review 10.  RNA-Seq methods for transcriptome analysis.

Authors:  Radmila Hrdlickova; Masoud Toloue; Bin Tian
Journal:  Wiley Interdiscip Rev RNA       Date:  2016-05-19       Impact factor: 9.957

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.