The Impact of Normalization Methods on RNA-Seq Data Analysis

Zyprych-Walczak, Joanna; Szabelska, Alicja; Handschuh, Luiza; Górczak, Katarzyna; Klamecka, Kamila; Figlerowicz, Marek; Siatkowski, Idzi

doi:10.1155/2015/621690

Cited by 94 publications

(88 citation statements)

References 28 publications

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“…Several benchmark studies focusing on differential expression analysis proposed that RPKM performed poorly and should be avoided (Maza et al, 2013;Dillies et al, 2013;Zyprych-Walczak et al, 2015). This was not observed for the maize GCN testing.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…However, many factors may affect the callings of alternative splicing, such as sequencing depth, the analysis pipeline and parameters [56][57][58]. We further analyzed the RNA-seq datasets [19,24,50,51] used in the Ke et al study [55] to detect the changed alternative splicing events by rMATS [34], a popular software used in alternative splicing analysis with FDR < 0.05.…”

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confidence: 99%

Mettl3-mediated m6A regulates spermatogonial differentiation and meiosis initiation

et al. 2017

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METTL3 catalyzes the formation of N6 -methyl-adenosine (m 6 A) which has important roles in regulating various biological processes. However, the in vivo function of Mettl3 remains largely unknown in mammals. Here we generated germ cell-specific Mettl3 knockout mice and demonstrated that Mettl3 was essential for male fertility and spermatogenesis. The ablation of Mettl3 in germ cells severely inhibited spermatogonial differentiation and blocked the initiation of meiosis. Transcriptome and m 6 A profiling analysis revealed that genes functioning in spermatogenesis had altered profiles of expression and alternative splicing. Our findings provide novel insights into the function and regulatory mechanisms of Mettl3-mediated m 6 A modification in spermatogenesis and reproduction in mammals.

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“…However, the normalisation method of trimmed mean of M-values included in edgeR package and the DESeq normalisation method seem to perform better than others [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 90%

DEgenes Hunter - A Flexible R Pipeline for Automated RNA-seq Studies in Organisms without Reference Genome

Gayte¹,

Moreno²,

Zonjic³

et al. 2017

Genomics Comput Biol

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Differential gene expression based on RNA-seq is widely used.Bioinformatics skills are required since no algorithm is appropriate for all experimental designs.Moreover, when working with organisms without reference genome, functional analysis is less than straightforward in most situations. DEgenes Hunter, an attempt to automate the process, is based on two independent scripts, one for differential expression and one for functional interpretation. Based on replicates, the R script decides which of the edgeR, DEseq2, NOISeq and limma algorithms are appropriate. It performs quality control calculations and provides the prevalent, most reliable, set of differentially expressed genes, and lists all other possible candidates for further functional interpretation. It also provides a combined P-value that allows differentially expressed genes ranking. It has been tested with synthetic and real-world datasets, showing in both cases ease of use and reliable results. With real data, DEgenes Hunter offers straightforward functional interpretation.

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The Impact of Normalization Methods on RNA-Seq Data Analysis

Cited by 94 publications

References 28 publications

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

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

Mettl3-mediated m6A regulates spermatogonial differentiation and meiosis initiation

DEgenes Hunter - A Flexible R Pipeline for Automated RNA-seq Studies in Organisms without Reference Genome

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