Advanced Applications of RNA Sequencing and Challenges

Han, Yingbo; Gao, Shaojian; Muegge, Kathrin; Zhang, Wei; Zhou, Bing

doi:10.4137/bbi.s28991

Cited by 169 publications

(172 citation statements)

References 209 publications

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“…RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al, 2009;Han et al, 2015;Conesa et al, 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al, 2013;Li et al, 2013a;Lonsdale et al, 2013;Fadista et al, 2014).…”

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Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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ORCID IDs: 0000-0002-6182-800X (J.H.); 0000-0001-6612-3570 (S.V.); 0000-0002-9564-8146 (K.M.M.).With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.Maize (Zea mays) is the most widely produced crop in United States, and U.S. agriculture accounted for 36% of world maize production in 2015 (USDA, 2016). Maize has also been in the center of genetics research for more than 100 years, including McClintock's pioneering work with transposable elements (reviewed by McClintock, 1983;Fedoroff, 2012). Due to recent technological advances in nucleic acid sequencing and the availability of the maize genome sequence (Schnable et al., 2009), maize genomics research has been greatly expedited.RNA-sequencing (RNA-Seq) has become the favored technique for detecting genomewide expression patterns. RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al., 2009;Han et al., 2015;Conesa et al., 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al., 2013;Li et al., 2013a;Lonsdale et al., 2013;Fadista et al., 2014). Because of its widespread adaptability, greater than 5000 Illumina platform Maize RNA-Seq libraries (Fig. 1A) are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Leinonen et al., 2010), adding to the body of data that can be used to study the maize genome.The maize genome is large and heterogeneous, and the genome annotation is still far from complete (Cigan et al., 2005;Ficklin and Feltus, 2011). Although recent work has...

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Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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“…Next-generation RNA sequencing has increased ourability to identify specific proteins involved in many diseases [17,18]. In the present study, this technique was used to identify putative differences in pterygia samples when compared with normal conjunctival samples.…”

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Overexpression of CFH gene in pterygiumv patients

Bang¹,

Rho²,

Kang³

et al. 2017

Trop. J. Pharm Res

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“…The mechanisms regulating biological reactions, as well as the identification of genes implicated in these responses, are of great interest to the research community. These techniques could serve to assign functions to previously unannotated genes and to allocate gene groups to functional pathways (DeRisi et al, 1996;Han et al, 2015). Additionally, these techniques contribute to our understanding of biological mechanisms and responses to environmental stimuli (Miller and Tang, 2009).…”

Section: Rna-seqmentioning

confidence: 99%