Differential analysis of RNA-seq incorporating quantification uncertainty

Pimentel, Harold; Bray, Nicolas; Puente, Suzette; Melsted, Páll; Pachter, Lior

doi:10.1038/nmeth.4324

Cited by 1,302 publications

(1,201 citation statements)

References 36 publications

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“…To quantify transcript abundance we pseudo-aligned RNA-seq reads to ENSEMBL transcripts, using kallisto (v0.42.4, options: -b 50 --single -l 200 -s 30) (Bray et al, 2016). Finally, we identified differentially expressed transcripts using the sleuth R package (Pimentel et al, 2016) In addition to a sleuth q-value < 0.05, we also required differentially expressed genes to have a fold change > 1.5 and expression > 10 TPM in at least one condition.…”

Section: Star Methodsmentioning

confidence: 99%

The Transcription Factor T-bet Limits Amplification of Type I IFN Transcriptome and Circuitry in T Helper 1 Cells

et al. 2017

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Summary Host defense requires the specification of CD4+ helper T (Th) cells into distinct fates including Th1 cells that preferentially produce interferon-γ(IFN-γ) IFN-γ, a member of a large family of anti-pathogenic and anti-tumor IFNs, induces T-bet, a lineage defining transcription factor for Th1 cells, which in turn supports IFN-γ production in a feed-forward manner. Herein, we showed a cell intrinsic role of T-bet to influence how T cells perceive their secreted product in the environment. In the absence of T-bet, IFN-γ aberrantly induced a type I IFN transcriptomic program. T-bet preferentially repressed genes and pathways ordinarily activated by type I IFNs to ensure that its transcriptional response does not evoke an aberrant amplification of type I IFN signaling circuitry otherwise triggered by its own product. Thus, in addition to promoting Th1 effector commitment, T-bet acts as a repressor in differentiated Th1 cells to prevent abberant autocrine type I IFN and downstream signaling.

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Section: Star Methodsmentioning

confidence: 99%

The Transcription Factor T-bet Limits Amplification of Type I IFN Transcriptome and Circuitry in T Helper 1 Cells

et al. 2017

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“…Reads were then aligned to all known S288C RNA transcripts in a process referred to as pseudo-alignment using Kallisto (Bray et al, 2016). Data analysis was performed in R using the Sleuth package (Pimentel et al, 2017) and ggplot2. Beta values (natural log2 with technical variation removed) was converted to log2 fold change using the formula LOG(POWER(2.71828, Beta), 2) in Excel.…”

Section: Star Methodsmentioning

confidence: 99%

Resetting the Yeast Epigenome with Human Nucleosomes

Truong

Boeke

2017

Cell

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Summary Humans and yeast are separated by a billion years of evolution, yet their conserved histones retain central roles in gene regulation. Here, we “reset” yeast to use core human nucleosomes in lieu of their own – a rare event taking twenty days – which initially only worked with variant H3.1. The cells adapt by acquiring suppressor mutations in cell-division genes, or by acquiring certain aneuploid states. Converting five histone residues to their yeast counterparts restored robust growth. We reveal that humanized nucleosomes are positioned according to endogenous yeast DNA sequence and chromatin-remodeling network, as judged by a yeast-like nucleosome repeat length. However, human nucleosomes have higher DNA occupancy, globally reduce RNA content, and slow adaptation to new conditions by delaying chromatin remodeling. These humanized yeasts (including H3.3) pose fundamental new questions about how chromatin is linked to many cell processes, and provides a platform to study histone variants via yeast epigenome reprogramming.

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“…Differential gene (and transcript) analysis was performed with Sleuth (ver. 0.29;Pimentel et al, 2017), using the likelihood ratio test (LRT) and the Wald test in R Core Team (2017) to estimate significant results (ver. 3.4.0;R Core Team, 2017).…”

Section: Rna Extraction Library Preparation and Sequencingmentioning

confidence: 99%

“…3.4.0;R Core Team, 2017). Statistically significant (q-values < 0.05) differential gene expression is reported as beta values, which are bias estimators of the foldchange that accounts for the technical variability of transcripts and are reported as natural log values (Pimentel et al, 2017; see Supplementary Material S4 for results of Sleuth analysis of non-annotated transcripts).…”

Section: Rna Extraction Library Preparation and Sequencingmentioning

confidence: 99%

Environmental Stress Responses and Experimental Handling Artifacts of a Model Organism, the Copepod Acartia tonsa (Dana)

et al. 2018

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Handling animals during experiments potentially affects the differential expression of genes chosen as biomarkers of sub-lethal stress. RNA sequencing was used to examine whole-transcriptome responses caused by laboratory handling of the calanoid copepod, Acartia tonsa. Salinity shock (S = 35 to S = 5) was used as positive stress control; individuals not exposed to handling or other stressors served as negative stress control. All copepods were grown from eggs to adults without being handled or exposed to any stressors prior the experiment. Survival of nauplii and adults was estimated for up to 10 min of exposure to handling stress and salinity shock. Only adults exhibited decreased survival (44 ± 7% with 10 min of exposure) in response to handling stress and were selected for definitive experiments for RNA sequencing. After 10 min of experimental exposures to handling stress or salinity shock, adults were incubated for 15 min or 24 h at normal culture conditions. A small number of significantly differentially expressed genes (DEGs) were observed 15 min after exposure to handling stress (2 DEGs) or salinity shock (7 DEGs). However, 24 h after exposure, handling stress resulted in 276 DEGs and salinity shock resulted in 573 DEGs, of which 174 DEGs were overlapping between the treatments. Among the DEGs observed 24 h after exposure to handling stress or salinity shock, some commonly-used stress biomarkers appeared at low levels. This suggests that a stress-response was induced at the transcriptional level for these genes between 15 min and 24 h following exposure. Since handling stress clearly affects transcriptional patterns, it is important to consider handling when designing experiments, by either including additional controls or avoiding focus on impacted genes. Not considering handling in gene expression studies can lead to inaccurate conclusions. The present study provides a baseline for studying handling stress in future studies using this model organism and others.

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Differential analysis of RNA-seq incorporating quantification uncertainty

Cited by 1,302 publications

References 36 publications

The Transcription Factor T-bet Limits Amplification of Type I IFN Transcriptome and Circuitry in T Helper 1 Cells

The Transcription Factor T-bet Limits Amplification of Type I IFN Transcriptome and Circuitry in T Helper 1 Cells

Resetting the Yeast Epigenome with Human Nucleosomes

Environmental Stress Responses and Experimental Handling Artifacts of a Model Organism, the Copepod Acartia tonsa (Dana)

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