Data-based filtering for replicated high-throughput transcriptome sequencing experiments

Rau, Andrea; Gallopin, Mélina; Celeux, Gilles; Jaffrézic, Florence

doi:10.1093/bioinformatics/btt350

Cited by 197 publications

(197 citation statements)

References 28 publications

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“…About 247 million read pairs were unambiguously mapped on the full M. truncatula genome sequence Mt20120830 (accessible at the SYMbiMICS Web site https://iant.toulouse.inra.fr/symbimics/; download section), with an average of 61.9 million read pairs per condition (Supplemental Table S1). Using a threshold statistically defined following Rau et al (2013), these data enabled the detection of 17,191 genes, which included MtEXP7, a gene specifically expressed in the epidermis . Complete results, including correspondence with Mt4.0 gene models and annotation data, are provided in Supplemental Table S2 or at the SYMbiMICS Web site, where epidermis and previous nodule RNAseq data can be easily queried using various requests (M. truncatula gene or Affymetrix oligonucleotide identifiers, keywords, and BLAST search).…”

Section: Lcm-rnaseq Analysis Of the Root Epidermal Response To Nf Trementioning

confidence: 99%

A Laser Dissection-RNAseq Analysis Highlights the Activation of Cytokinin Pathways by Nod Factors in the Medicago truncatula Root Epidermis

et al. 2016

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Nod factors (NFs) are lipochitooligosaccharidic signal molecules produced by rhizobia, which play a key role in the rhizobiumlegume symbiotic interaction. In this study, we analyzed the gene expression reprogramming induced by purified NF (4 and 24 h of treatment) in the root epidermis of the model legume Medicago truncatula. Tissue-specific transcriptome analysis was achieved by laser-capture microdissection coupled to high-depth RNA sequencing. The expression of 17,191 genes was detected in the epidermis, among which 1,070 were found to be regulated by NF addition, including previously characterized NF-induced marker genes. Many genes exhibited strong levels of transcriptional activation, sometimes only transiently at 4 h, indicating highly dynamic regulation. Expression reprogramming affected a variety of cellular processes, including perception, signaling, regulation of gene expression, as well as cell wall, cytoskeleton, transport, metabolism, and defense, with numerous NF-induced genes never identified before. Strikingly, early epidermal activation of cytokinin (CK) pathways was indicated, based on the induction of CK metabolic and signaling genes, including the CRE1 receptor essential to promote nodulation. These transcriptional activations were independently validated using promoter:b-glucuronidase fusions with the MtCRE1 CK receptor gene and a CK response reporter (TWO COMPONENT SIGNALING SENSOR NEW). A CK pretreatment reduced the NF induction of the EARLY NODULIN11 (ENOD11) symbiotic marker, while a CK-degrading enzyme (CYTOKININ OXIDASE/DEHYDROGENASE3) ectopically expressed in the root epidermis led to increased NF induction of ENOD11 and nodulation. Therefore, CK may play both positive and negative roles in M. truncatula nodulation.

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Section: Lcm-rnaseq Analysis Of the Root Epidermal Response To Nf Trementioning

confidence: 99%

A Laser Dissection-RNAseq Analysis Highlights the Activation of Cytokinin Pathways by Nod Factors in the Medicago truncatula Root Epidermis

et al. 2016

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“…We removed the genes with zero counts in all conditions, as well as genes whose maximum counts are <5 as recommended (Rau et al 2013). The description of parameters for these data sets is summarized in Table 1.…”

Section: Estimation Of Parameters In the Data Setsmentioning

confidence: 99%

Power analysis and sample size estimation for RNA-Seq differential expression

Ching

Huang

Garmire

2014

RNA

204

200

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It is crucial for researchers to optimize RNA-seq experimental designs for differential expression detection. Currently, the field lacks general methods to estimate power and sample size for RNA-Seq in complex experimental designs, under the assumption of the negative binomial distribution. We simulate RNA-Seq count data based on parameters estimated from six widely different public data sets (including cell line comparison, tissue comparison, and cancer data sets) and calculate the statistical power in paired and unpaired sample experiments. We comprehensively compare five differential expression analysis packages (DESeq, edgeR, DESeq2, sSeq, and EBSeq) and evaluate their performance by power, receiver operator characteristic (ROC) curves, and other metrics including areas under the curve (AUC), Matthews correlation coefficient (MCC), and F-measures. DESeq2 and edgeR tend to give the best performance in general. Increasing sample size or sequencing depth increases power; however, increasing sample size is more potent than sequencing depth to increase power, especially when the sequencing depth reaches 20 million reads. Long intergenic noncoding RNAs (lincRNA) yields lower power relative to the protein coding mRNAs, given their lower expression level in the same RNA-Seq experiment. On the other hand, paired-sample RNA-Seq significantly enhances the statistical power, confirming the importance of considering the multifactor experimental design. Finally, a local optimal power is achievable for a given budget constraint, and the dominant contributing factor is sample size rather than the sequencing depth. In conclusion, we provide a power analysis tool (http://www2.hawaii.edu/~lgarmire/ RNASeqPowerCalculator.htm) that captures the dispersion in the data and can serve as a practical reference under the budget constraint of RNA-Seq experiments.

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“…Aspects of transcriptomic experimental design and data analysis have been questioned in the past. Early transcriptomic studies were hampered by inappropriate statistical analyses, a problem that was resolved through the development of statistical methods specific to transcriptomic datasets (Storey and Tibshirani, 2003;Hackstadt and Hess, 2009;Rau et al, 2013). Analyzing long lists of differentially expressed genes also posed a challenge for early transcriptomic experiments (Gracey, 2007).…”

Section: Repeatedly Identifying the Same Genesmentioning

confidence: 99%

Considerations for the use of transcriptomics in identifying the ‘genes that matter’ for environmental adaptation

Evans¹

2015

Journal of Experimental Biology

113

106

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Transcriptomics has emerged as a powerful approach for exploring physiological responses to the environment. However, like any other experimental approach, transcriptomics has its limitations. Transcriptomics has been criticized as an inappropriate method to identify genes with large impacts on adaptive responses to the environment because: (1) genes with large impacts on fitness are rare; (2) a large change in gene expression does not necessarily equate to a large effect on fitness; and (3) protein activity is most relevant to fitness, and mRNA abundance is an unreliable indicator of protein activity. In this review, these criticisms are re-evaluated in the context of recent systems-level experiments that provide new insight into the relationship between gene expression and fitness during environmental stress. In general, these criticisms remain valid today, and indicate that exclusively using transcriptomics to screen for genes that underlie environmental adaptation will overlook constitutively expressed regulatory genes that play major roles in setting tolerance limits. Standard practices in transcriptomic data analysis pipelines may also be limiting insight by prioritizing highly differentially expressed and conserved genes over those genes that undergo moderate fold-changes and cannot be annotated. While these data certainly do not undermine the continued and widespread use of transcriptomics within environmental physiology, they do highlight the types of research questions for which transcriptomics is best suited and the need for more gene functional analyses. Such information is pertinent at a time when transcriptomics has become increasingly tractable and many researchers may be contemplating integrating transcriptomics into their research programs.

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Data-based filtering for replicated high-throughput transcriptome sequencing experiments

Cited by 197 publications

References 28 publications

A Laser Dissection-RNAseq Analysis Highlights the Activation of Cytokinin Pathways by Nod Factors in the Medicago truncatula Root Epidermis

A Laser Dissection-RNAseq Analysis Highlights the Activation of Cytokinin Pathways by Nod Factors in the Medicago truncatula Root Epidermis

Power analysis and sample size estimation for RNA-Seq differential expression

Considerations for the use of transcriptomics in identifying the ‘genes that matter’ for environmental adaptation

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