ROAST: rotation gene set tests for complex microarray experiments

Wu, Di; Lim, Elgene; Vaillant, François; Asselin-Labat, Marie Liesse; Visvader, Jane E.; Smyth, Gordon K.

doi:10.1093/bioinformatics/btq401

Cited by 477 publications

(542 citation statements)

References 28 publications

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“…Gene set analysis tools available via roast 12 and camera 13 allow researchers to further test and prioritize screen results. This capability can be used to obtain a gene-by-gene ranking, rather than a hairpin-specific one, which can be helpful when shRNA libraries contain multiple hairpins targeting each gene.…”

Section: Methodsmentioning

confidence: 99%

shRNA-seq data analysis with edgeR

et al. 2014

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Pooled short hairpin RNA sequencing (shRNA-seq) screens are becoming increasingly popular in functional genomics research, and there is a need to establish optimal analysis tools to handle such data. Our open-source shRNA processing pipeline in edgeR provides a complete analysis solution for shRNA-seq screen data, that begins with the raw sequence reads and ends with a ranked lists of candidate shRNAs for downstream biological validation. We first summarize the raw data contained in a fastq file into a matrix of counts (samples in the columns, hairpins in the rows) with options for allowing mismatches and small shifts in hairpin position. Diagnostic plots, normalization and differential representation analysis can then be performed using established methods to prioritize results in a statistically rigorous way, with the choice of either the classic exact testing methodology or a generalized linear modelling that can handle complex experimental designs. A detailed users’ guide that demonstrates how to analyze screen data in edgeR along with a point-and-click implementation of this workflow in Galaxy are also provided. The edgeR package is freely available from http://www.bioconductor.org.

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Section: Methodsmentioning

confidence: 99%

shRNA-seq data analysis with edgeR

et al. 2014

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“…GO analyses were performed using the goseq package with default parameters (Young et al, 2010). The significant terms were then used in a gene set enrichment analysis using the mroast function implemented in the limma package (Wu et al, 2010) with "msq" set statistic and 10,000 rotations.…”

Section: Go Term Enrichment and Gene Set Analysesmentioning

confidence: 99%

Seed Production Affects Maternal Growth and Senescence in Arabidopsis

Wuest

Philipp²,

Guthörl³

et al. 2016

Plant Physiol.

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Correlative control (influence of one organ over another organ) of seeds over maternal growth is one of the most obvious phenotypic expressions of the trade-off between growth and reproduction. However, the underlying molecular mechanisms are largely unknown. Here, we characterize the physiological and molecular effects of correlative inhibition by seeds on Arabidopsis (Arabidopsis thaliana) inflorescences, i.e. global proliferative arrest (GPA) during which all maternal growth ceases upon the production of a given number of seeds. We observed transcriptional responses to growth-and branching-inhibitory hormones, and low mitotic activity in meristems upon GPA, but found that meristems retain their identity and proliferative potential. In shoot tissues, we detected the induction of stress-and senescence-related gene expression upon fruit production and GPA, and a drop in chlorophyll levels, suggestive of altered source-sink relationships between vegetative shoot and reproductive tissues. Levels of shoot reactive oxygen species, however, strongly decreased upon GPA, a phenomenon that is associated with bud dormancy in some perennials. Indeed, gene expression changes in arrested apical inflorescences after fruit removal resembled changes observed in axillary buds following release from apical dominance. This suggests that GPA represents a form of bud dormancy, and that dominance is gradually transferred from growing inflorescences to maturing seeds, allowing offspring control over maternal resources, simultaneously restricting offspring number. This would provide a mechanistic explanation for the constraint between offspring quality and quantity.

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“…For example, a large number of statistical methods based on the normal distribution have been developed to analyze intensity data from microarrays [44], including those for detecting differential expressions [46], modeling random effects [56], testing gene sets [57,58] and so on. However, most of the tools developed for RNA-seq and ChIP-seq data aim at the differential analysis, and only a few of them can handle complicate experiment designs (see Table 1).…”

Section: Namementioning

confidence: 99%

An introduction to computational tools for differential binding analysis with ChIP‐seq data

Tu¹,

Shao²

2017

Quant. Biol.

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Background: Gene transcription in eukaryotic cells is collectively controlled by a large panel of chromatin associated proteins and ChIP-seq is now widely used to locate their binding sites along the whole genome. Inferring the differential binding sites of these proteins between biological conditions by comparing the corresponding ChIP-seq samples is of general interest, yet it is still a computationally challenging task. Results: Here, we briefly review the computational tools developed in recent years for differential binding analysis with ChIP-seq data. The methods are extensively classified by their strategy of statistical modeling and scope of application. Finally, a decision tree is presented for choosing proper tools based on the specific dataset. Conclusions: Computational tools for differential binding analysis with ChIP-seq data vary significantly with respect to their applicability and performance. This review can serve as a practical guide for readers to select appropriate tools for their own datasets.

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ROAST: rotation gene set tests for complex microarray experiments

Cited by 477 publications

References 28 publications

shRNA-seq data analysis with edgeR

shRNA-seq data analysis with edgeR

Seed Production Affects Maternal Growth and Senescence in Arabidopsis

An introduction to computational tools for differential binding analysis with ChIP‐seq data

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