Literature DB >> 30529531

Exploring the effect of library preparation on RNA sequencing experiments.

Lei Wang1, Sara J Felts2, Virginia P Van Keulen3, Larry R Pease4, Yuji Zhang5.   

Abstract

RNA sequencing (RNA-seq) has become the widely preferred choice for surveying the genome-wide transcriptome complexity in many organisms. However, the broad adaptation of this methodology into the clinic still needs further evaluation of potential effect of sample preparation factors on its analytical reliability using patient samples. In this study, we examined the impact of three major sample preparation factors (i.e., cDNA library storage time, the quantity of input RNA, and cryopreservation of cell samples) on sequence biases, gene expression profiles, and enriched biological functions using RNAs isolated from primary B cell and CD4+ cell blood samples of healthy subjects. Our comprehensive comparison results suggested that different cDNA library storage time, quantity of input RNA, and cryopreservation of cell samples did not significantly alter gene transcriptional expression profiles generated by RNA-seq experiments. These findings shed new lights on the potential applications of RNA-seq technique to patient samples in a regular clinical setting. Published by Elsevier Inc.

Entities:  

Keywords:  Cryopreservation; Library storage time; Quantity of input RNA; RNA-seq; lincRNA

Year:  2018        PMID: 30529531      PMCID: PMC6551333          DOI: 10.1016/j.ygeno.2018.11.030

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  36 in total

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Authors:  Da Wei Huang; Brad T Sherman; Richard A Lempicki
Journal:  Nat Protoc       Date:  2009       Impact factor: 13.491

2.  Differential expression in RNA-seq: a matter of depth.

Authors:  Sonia Tarazona; Fernando García-Alcalde; Joaquín Dopazo; Alberto Ferrer; Ana Conesa
Journal:  Genome Res       Date:  2011-09-08       Impact factor: 9.043

3.  How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes?

Authors:  Brian J Haas; Melissa Chin; Chad Nusbaum; Bruce W Birren; Jonathan Livny
Journal:  BMC Genomics       Date:  2012-12-27       Impact factor: 3.969

4.  Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data.

Authors:  Konstantin Okonechnikov; Ana Conesa; Fernando García-Alcalde
Journal:  Bioinformatics       Date:  2015-10-01       Impact factor: 6.937

5.  How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets.

Authors:  Lucia Peixoto; Davide Risso; Shane G Poplawski; Mathieu E Wimmer; Terence P Speed; Marcelo A Wood; Ted Abel
Journal:  Nucleic Acids Res       Date:  2015-07-21       Impact factor: 16.971

6.  Erratum to: A survey of best practices for RNA-seq data analysis.

Authors:  Ana Conesa; Pedro Madrigal; Sonia Tarazona; David Gomez-Cabrero; Alejandra Cervera; Andrew McPherson; Michal Wojciech Szcześniak; Daniel J Gaffney; Laura L Elo; Xuegong Zhang; Ali Mortazavi
Journal:  Genome Biol       Date:  2016-08-26       Impact factor: 13.583

7.  Single-cell transcriptome conservation in cryopreserved cells and tissues.

Authors:  Amy Guillaumet-Adkins; Gustavo Rodríguez-Esteban; Elisabetta Mereu; Maria Mendez-Lago; Diego A Jaitin; Alberto Villanueva; August Vidal; Alex Martinez-Marti; Enriqueta Felip; Ana Vivancos; Hadas Keren-Shaul; Simon Heath; Marta Gut; Ido Amit; Ivo Gut; Holger Heyn
Journal:  Genome Biol       Date:  2017-03-01       Impact factor: 13.583

8.  Identification and removal of sequencing artifacts produced by mispriming during reverse transcription in multiple RNA-seq technologies.

Authors:  Haridha Shivram; Vishwanath R Iyer
Journal:  RNA       Date:  2018-06-27       Impact factor: 4.942

9.  Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences.

Authors:  Anqi Zhu; Joseph G Ibrahim; Michael I Love
Journal:  Bioinformatics       Date:  2019-06-01       Impact factor: 6.937

Review 10.  Translating RNA sequencing into clinical diagnostics: opportunities and challenges.

Authors:  Sara A Byron; Kendall R Van Keuren-Jensen; David M Engelthaler; John D Carpten; David W Craig
Journal:  Nat Rev Genet       Date:  2016-03-21       Impact factor: 53.242

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  1 in total

1.  Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries.

Authors:  Samuel Hunter; Rutendo F Sigauke; Jacob T Stanley; Mary A Allen; Robin D Dowell
Journal:  BMC Genomics       Date:  2022-03-07       Impact factor: 3.969

  1 in total

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