Statistical Design and Analysis of RNA Sequencing Data

Auer, Paul L.; Doerge, R. W.

doi:10.1534/genetics.110.114983

Cited by 351 publications

(313 citation statements)

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“…Exhaustive enumeration of imprinted genes will require a large community- wide effort, including multiple replicates from multiple lines, with samples of different tissues and developmental time points. If the results are to be interpreted with confidence on the basis of RNA-seq data alone, a blocked and replicated design is essential (Auer and Doerge 2010). Our intention here was to apply RNA-seq in a simple, unreplicated design to serve as a means of nominating candidates for subsequent validation.…”

Section: Discussionmentioning

confidence: 99%

“…It is also important to examine biological replicates, ideally from individuals from different strains to test the possibility of strain-specific effects. A much larger study, with a well-replicated and blocked design of multiple RNA-seq runs (Auer and Doerge 2010) would be needed to generate a definitive count of the number of imprinted genes. From our data, 4.5% (251) of the 5527 genes, having sufficient data to perform the test, exhibit significant imprinting in the placenta.…”

Section: Discussionmentioning

confidence: 99%

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A Survey for Novel Imprinted Genes in the Mouse Placenta by mRNA-seq

2011

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Many questions about the regulation, functional specialization, computational prediction, and evolution of genomic imprinting would be better addressed by having an exhaustive genome-wide catalog of genes that display parent-of-origin differential expression. As a first-pass scan for novel imprinted genes, we performed mRNA-seq experiments on embryonic day 17.5 (E17.5) mouse placenta cDNA samples from reciprocal cross F 1 progeny of AKR and PWD mouse strains and quantified the allele-specific expression and the degree of parent-of-origin allelic imbalance. We confirmed the imprinting status of 23 known imprinted genes in the placenta and found that 12 genes reported previously to be imprinted in other tissues are also imprinted in mouse placenta. Through a wellreplicated design using an orthogonal allelic-expression technology, we verified 5 novel imprinted genes that were not previously known to be imprinted in mouse (Pde10, Phf17, Phactr2, Zfp64, and Htra3). Our data suggest that most of the strongly imprinted genes have already been identified, at least in the placenta, and that evidence supports perhaps 100 additional weakly imprinted genes. Despite previous appearance that the placenta tends to display an excess of maternally expressed imprinted genes, with the addition of our validated set of placenta-imprinted genes, this maternal bias has disappeared.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Survey for Novel Imprinted Genes in the Mouse Placenta by mRNA-seq

2011

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“…Because of the still significant cost of sequencing, and the enormous amount of data generated, observational studies with no biological replication are common, but are clearly prone to misinterpretation. The reader is referred to [33] for a discussion of the best experimental designs for meaningful comparisons of RNAseq datasets; the principles are fundamentally similar to those described above for field and greenhouse plot design.…”

Section: Guidelines For Biochemical and Molecular Biological Experimentsmentioning

confidence: 99%

Replication Concepts for Bioenergy Research Experiments

2015

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While there are some large and fundamental differences among disciplines related to the conversion of biomass to bioenergy, all scientific endeavors involve the use of biological feedstocks. As such, nearly every scientific experiment conducted in this area, regardless of the specific discipline, is subject to random variation, some of which is unpredictable and unidentifiable (i.e., pure random variation such as variation among plots in an experiment, individuals within a plot, or laboratory samples within an experimental unit) while some is predictable and identifiable (repeatable variation, such as spatial or temporal patterns within an experimental field, a glasshouse or growth chamber, or among laboratory containers). Identifying the scale and sources of this variation relative to the specific hypotheses of interest is a critical component of designing good experiments that generate meaningful and believable hypothesis tests and inference statements. Many bioenergy feedstock experiments are replicated at an incorrect scale, typically by sampling feedstocks to estimate laboratory error or by completely ignoring the errors associated with growing feedstocks in an agricultural area at a field or farmland (micro-or macro-region) scale. As such, actual random errors inherent in experimental materials are frequently underestimated, with unrealistically low standard errors of statistical parameters (e.g., means), leading to improper inferences. The examples and guidelines set forth in this paper and many of the references cited are intended to form the general policy and guidelines for replication of bioenergy feedstock experiments to be published in BioEnergy Research.

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“…Several R packages have been developed for statistical testing for DE using RNA-Seq data [38] . Those include edgeR [39] , DESeq [40] , DEGSeq [41] , baySeq [42] , BBSeq [43] , TSMP [44] , NBPSeq [45] and PoissonSeq [46] . Additionally, databases like SEQC (SEquencing Quality Control) have been established to assess the performance of the NGS technologies.…”

Section: Advantages and Challengesmentioning

confidence: 99%

Statistical modeling for differential transcriptome analysis using RNA-Seq technology

Miecznikowski

Liu

Ren

2012

JST

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RNA-Seq is a recently developed technology for transcriptome profiling. Numerous advantages of RNA-Seq suggest that it will be the platform of choice for genome-wide expression studies. RNA-Seq generates large volumes of data which require statistical methods for data processing and accurate inference. This article reviews the RNA-Seq technologies followed by a detailed discussion of current statistical methods for normalization and differential expression analysis. Key wordsRNA-Seq, Normalization, Biological counts, Differential expression Transcriptome profilingOver 99.9% of genome sequences are the same in all humans [1,2] , yet individuals show great distinction from each other.In a multicellular organism nearly all cells contain the same genome, but they develop into different tissues. A major source for many of these variations is the different gene expression patterns [3] . In control of gene expression, the transcriptome is the complete set of ribonucleic acid (RNA) transcripts, including messenger RNA (mRNA), ribosomal RNA (rRNA), transfer RNA (tRNA), and other non-coding RNA in a given cell type. It is the connection between genes and phenotype. The constitution of the transcriptome for an organism varies at different developmental stages or under different physiological conditions. As a quantitatively cataloged transcriptome provides us information on the underlying genetic mechanisms, the transcriptome is studied in relation to many diseases, e.g., cancers. One of the main goals in a cancer transcriptome study is quantifying the changes in expression levels of all the transcripts in tumor cells. In this manuscript, we discuss the cutting edge methods for quantifying the transcriptome and how the resulting data is used to determine significantly differentially expressed transcripts. MicroarraysMicroarrays have been the primary technology for quantitative transcriptome analysis since the mid-1990s [4] , and they have discovered many results in cancer research [5][6][7][8] . Although expression studies by microarrays have been very successful in the last decade, there are at least three intrinsic limitations to this hybridization-based technology. First, the background noise in microarrays is large due to cross-hybridization of closely related genes. Second, the microarray signal often reaches a limit of detection or saturation, therefore microarrays have a limited dynamic range (a few hundredfold) [9] .

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Statistical Design and Analysis of RNA Sequencing Data

Cited by 351 publications

References 45 publications

A Survey for Novel Imprinted Genes in the Mouse Placenta by mRNA-seq

A Survey for Novel Imprinted Genes in the Mouse Placenta by mRNA-seq

Replication Concepts for Bioenergy Research Experiments

Statistical modeling for differential transcriptome analysis using RNA-Seq technology

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