Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns

Moulos, Panagiotis; Hatzis, Pantelis

doi:10.1093/nar/gku1273

Cited by 100 publications

(100 citation statements)

References 30 publications

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“…They recommend limma and DESeq for data with fewer than five replicates per condition, finding that edgeR is "oversensitive" and suffers from high variability in its results while SAMSeq suffers from a lack of statistical power with few replicates. The idea of combining DGE methods is implemented in the novel tool PANDORA, which weights the results of different DGE tools according to their performance on test data and performs at least as well as the constituent tools (Moulos and Hatzis 2015).…”

Section: Introductionmentioning

confidence: 99%

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Schurch

Schofield

Gierliński

et al. 2016

RNA

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RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. An RNA-seq experiment with 48 biological replicates in each of two conditions was performed to answer these questions and provide guidelines for experimental design. With three biological replicates, nine of the 11 tools evaluated found only 20%-40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same nine tools successfully control their false discovery rate at ≲5% for all numbers of replicates, while the remaining two tools fail to control their FDR adequately, particularly for low numbers of replicates. For future RNA-seq experiments, these results suggest that at least six biological replicates should be used, rising to at least 12 when it is important to identify SDE genes for all fold changes. If fewer than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR and DESeq2 the leading tools. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.

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Section: Introductionmentioning

confidence: 99%

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Schurch

Schofield

Gierliński

et al. 2016

RNA

702

623

View full text Add to dashboard Cite

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“…Mapped reads from pure species and hybrids were down-sampled to an equivalent number per sample and then pooled by genotype (metaseqR) (Moulos and Hatzis 2014).…”

Section: Methodsmentioning

confidence: 99%

Gene regulation and speciation in house mice

2016

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One approach to understanding the process of speciation is to characterize the genetic architecture of post-zygotic isolation. As gene regulation requires interactions between loci, negative epistatic interactions between divergent regulatory elements might underlie hybrid incompatibilities and contribute to reproductive isolation. Here, we take advantage of a cross between house mouse subspecies, where hybrid dysfunction is largely unidirectional, to test several key predictions about regulatory divergence and reproductive isolation. Regulatory divergence between Mus musculus musculus and M. m. domesticus was characterized by studying allele-specific expression in fertile hybrid males using mRNA-sequencing of whole testes. We found extensive regulatory divergence between M. m. musculus and M. m. domesticus, largely attributable to cis-regulatory changes. When both cis and trans changes occurred, they were observed in opposition much more often than expected under a neutral model, providing strong evidence of widespread compensatory evolution. We also found evidence for lineage-specific positive selection on a subset of genes related to transcriptional regulation. Comparisons of fertile and sterile hybrid males identified a set of genes that were uniquely misexpressed in sterile individuals. Lastly, we discovered a nonrandom association between these genes and genes showing evidence of compensatory evolution, consistent with the idea that regulatory interactions might contribute to Dobzhansky-Muller incompatibilities and be important in speciation.

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“…All data modeling and analysis is done using SIMCA 15.0 (MKS DAS, Umeå, Sweden) and R software. The Mummichog algorithm has been used for pathway analysis [29] while MetaboAnalyst has been used for Metabolite Set Enrichment Analysis on the amino acid data [30]. Details regarding data modeling and validation results from all models are provided in Additional file 1.…”

Section: Methodsmentioning

confidence: 99%

Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis

et al. 2018

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BackgroundMetabolomics represent a valuable tool to recover biological information using body fluids and may help to characterize pathophysiological mechanisms of the studied disease. This approach has not been widely used to explore inherited metabolic diseases. This study investigates mucopolysaccharidosis type III (MPS III). A thorough and holistic understanding of metabolic remodeling in MPS III may allow the development, improvement and personalization of patient care.MethodsWe applied both targeted and untargeted metabolomics to urine samples obtained from a French cohort of 49 patients, consisting of 13 MPS IIIA, 16 MPS IIIB, 13 MPS IIIC, and 7 MPS IIID, along with 66 controls. The analytical strategy is based on ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry. Twenty-four amino acids have been assessed using tandem mass spectrometry combined with liquid chromatography. Multivariate data modeling has been used for discriminant metabolite selection. Pathway analysis has been performed to retrieve metabolic pathways impairments.ResultsData analysis revealed distinct biochemical profiles. These metabolic patterns, particularly those related to the amino acid metabolisms, allowed the different studied groups to be distinguished. Pathway analysis unveiled major amino acid pathways impairments in MPS III mainly arginine–proline metabolism and urea cycle metabolism.ConclusionThis represents one of the first metabolomics-based investigations of MPS III. These results may shed light on MPS III pathophysiology and could help to set more targeted studies to infer the biomarkers of the affected pathways, which is crucial for rare conditions such as MPS III.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1625-1) contains supplementary material, which is available to authorized users.

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Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns

Cited by 100 publications

References 30 publications

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

Gene regulation and speciation in house mice

Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis

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