Literature DB >> 33606266

Biological Perspectives of RNA-Sequencing Experimental Design.

Metsada Pasmanik-Chor1.   

Abstract

The development of high-throughput technologies has changed the conduct of biological experiments in the last decade. From single gene studies, research has shifted to measuring gene signatures at the transcriptome level. The dramatic decrease in the financial expenses of next generation sequencing techniques has enabled their routine implementation. However, very often, economic constraints restrict the number of samples and sequence quality. Careful planning and design may overcome this limitation, and attain the maximum information from a given experiment.Among the factors that affect the quality and quantity of data resulting from next generation sequencing experiments are sample size and the number of replicates, sequence depth and coverage, randomization, and batches. Here, we discuss the design of high-throughput experiments, while focusing on RNA-sequencing experiments. We suggest critical rules of thumb, from biological, statistical, and bioinformatics points of view, aimed to obtain a successful experiment, beyond the economic constraints.

Keywords:  Batch; Differentially expressed genes (DGEs); Experiment design; High-throughput (HT); Next generation sequencing (NGS); RNA-Seq; Sample size; Sample variability; Sequence coverage; Sequence depth

Mesh:

Substances:

Year:  2021        PMID: 33606266     DOI: 10.1007/978-1-0716-1103-6_17

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  17 in total

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Journal:  EMBO J       Date:  2015-09-21       Impact factor: 11.598

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Authors:  Alicia Poplawski; Harald Binder
Journal:  Brief Bioinform       Date:  2018-07-20       Impact factor: 11.622

6.  Comparison of the transcriptional landscapes between human and mouse tissues.

Authors:  Shin Lin; Yiing Lin; Joseph R Nery; Mark A Urich; Alessandra Breschi; Carrie A Davis; Alexander Dobin; Christopher Zaleski; Michael A Beer; William C Chapman; Thomas R Gingeras; Joseph R Ecker; Michael P Snyder
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7.  Properties of permuted-block randomization in clinical trials.

Authors:  J P Matts; J M Lachin
Journal:  Control Clin Trials       Date:  1988-12

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Authors:  Anto P Rajkumar; Per Qvist; Ross Lazarus; Francesco Lescai; Jia Ju; Mette Nyegaard; Ole Mors; Anders D Børglum; Qibin Li; Jane H Christensen
Journal:  BMC Genomics       Date:  2015-07-25       Impact factor: 3.969

9.  A reanalysis of mouse ENCODE comparative gene expression data.

Authors:  Yoav Gilad; Orna Mizrahi-Man
Journal:  F1000Res       Date:  2015-05-19

10.  Selection of Control, Randomization, Blinding, and Allocation Concealment.

Authors:  Amrita Sil; Piyush Kumar; Rajesh Kumar; Nilay Kanti Das
Journal:  Indian Dermatol Online J       Date:  2019-08-28
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