The exact probability distribution of the rank product statistics for replicated experiments

Eisinga, Rob; Breitling, Rainer; Heskes, Tom

doi:10.1016/j.febslet.2013.01.037

Cited by 36 publications

(41 citation statements)

References 12 publications

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“…The Rank Products (RP) method [17, 18] is a useful non-parametric test to evaluate the significance of differential expression by a series of fold change comparisons. Rankings arise from consistencies in fold change differences between samples; as such, a series of pairwise comparisons for each individual tissue against the rest of the sequenced tissues in our study identifies high-ranking transcripts that are preferentially expressed in a tissue compared to the rest.…”

Section: Resultsmentioning

confidence: 99%

A detailed gene expression study of the Miscanthusgenus reveals changes in the transcriptome associated with the rejuvenation of spring rhizomes

et al. 2013

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BackgroundThe Miscanthus genus of perennial C4 grasses contains promising biofuel crops for temperate climates. However, few genomic resources exist for Miscanthus, which limits understanding of its interesting biology and future genetic improvement. A comprehensive catalog of expressed sequences were generated from a variety of Miscanthus species and tissue types, with an emphasis on characterizing gene expression changes in spring compared to fall rhizomes.ResultsIllumina short read sequencing technology was used to produce transcriptome sequences from different tissues and organs during distinct developmental stages for multiple Miscanthus species, including Miscanthus sinensis, Miscanthus sacchariflorus, and their interspecific hybrid Miscanthus × giganteus. More than fifty billion base-pairs of Miscanthus transcript sequence were produced. Overall, 26,230 Sorghum gene models (i.e., ~ 96% of predicted Sorghum genes) had at least five Miscanthus reads mapped to them, suggesting that a large portion of the Miscanthus transcriptome is represented in this dataset. The Miscanthus × giganteus data was used to identify genes preferentially expressed in a single tissue, such as the spring rhizome, using Sorghum bicolor as a reference. Quantitative real-time PCR was used to verify examples of preferential expression predicted via RNA-Seq. Contiguous consensus transcript sequences were assembled for each species and annotated using InterProScan. Sequences from the assembled transcriptome were used to amplify genomic segments from a doubled haploid Miscanthus sinensis and from Miscanthus × giganteus to further disentangle the allelic and paralogous variations in genes.ConclusionsThis large expressed sequence tag collection creates a valuable resource for the study of Miscanthus biology by providing detailed gene sequence information and tissue preferred expression patterns. We have successfully generated a database of transcriptome assemblies and demonstrated its use in the study of genes of interest. Analysis of gene expression profiles revealed biological pathways that exhibit altered regulation in spring compared to fall rhizomes, which are consistent with their different physiological functions. The expression profiles of the subterranean rhizome provides a better understanding of the biological activities of the underground stem structures that are essentials for perenniality and the storage or remobilization of carbon and nutrient resources.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-14-864) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

A detailed gene expression study of the Miscanthusgenus reveals changes in the transcriptome associated with the rejuvenation of spring rhizomes

et al. 2013

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“…To combine the experiments we calculate the product of the rank for every fragment. To approximate a p value we make use of the Gamma distribution (Koziol, 2010):

P = 1 - Γ - ln \frac{r}{{(n + 1)}^{k}}

Although, an exact calculation of the p value has been proposed (Eisinga et al., 2013), for larger samples this is computationally prohibitive.…”

Section: Methodsmentioning

confidence: 99%

The Cohesin Release Factor WAPL Restricts Chromatin Loop Extension

Haarhuis

Weide

Blomen

et al. 2017

Cell

686

120

777

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SummaryThe spatial organization of chromosomes influences many nuclear processes including gene expression. The cohesin complex shapes the 3D genome by looping together CTCF sites along chromosomes. We show here that chromatin loop size can be increased and that the duration with which cohesin embraces DNA determines the degree to which loops are enlarged. Cohesin’s DNA release factor WAPL restricts this loop extension and also prevents looping between incorrectly oriented CTCF sites. We reveal that the SCC2/SCC4 complex promotes the extension of chromatin loops and the formation of topologically associated domains (TADs). Our data support the model that cohesin structures chromosomes through the processive enlargement of loops and that TADs reflect polyclonal collections of loops in the making. Finally, we find that whereas cohesin promotes chromosomal looping, it rather limits nuclear compartmentalization. We conclude that the balanced activity of SCC2/SCC4 and WAPL enables cohesin to correctly structure chromosomes.

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“…Although, an exact calculation of the P -value has been proposed by Eisinga et al . (27), for larger samples this is computationally prohibitive. A Benjamini and Hochberg multiple testing procedure (28) is then applied with the aim to control for the false discovery rate (‘fdr’).…”

Section: Methodsmentioning

confidence: 99%

peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data

Geeven

Teunissen

Laat

et al. 2018

Nucleic Acids Research

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It is becoming increasingly clear that chromosome organization plays an important role in gene regulation. High-resolution methods such as 4C, Capture-C and promoter capture Hi-C (PCHiC) enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/Capture-C/PCHiC analyses is the reliable identification of chromatin loops, preferably not based on visual inspection of a DNA contact profile, but on reproducible statistical analysis that robustly scores interaction peaks in the non-uniform contact background. Here, we present peakC, an R package for the analysis of 4C/Capture-C/PCHiC data. We generated 4C data for 13 viewpoints in two tissues in at least triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates. Our software is user-friendly and enables robust peak calling for one-vs-all chromosome capture experiments. peakC is available at: https://github.com/deWitLab/peakC.

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The exact probability distribution of the rank product statistics for replicated experiments

Cited by 36 publications

References 12 publications

A detailed gene expression study of the Miscanthusgenus reveals changes in the transcriptome associated with the rejuvenation of spring rhizomes

A detailed gene expression study of the Miscanthusgenus reveals changes in the transcriptome associated with the rejuvenation of spring rhizomes

The Cohesin Release Factor WAPL Restricts Chromatin Loop Extension

peakC: a flexible, non-parametric peak calling package for 4C and Capture-C data

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