A Global Clustering Algorithm to Identify Long Intergenic Non-Coding RNA - with Applications in Mouse Macrophages

Garmire, Lana X.; Garmire, David; Huang, Wendy; Yao, Joyee; Glass, Christopher K.; Subramaniam, Shankar

doi:10.1371/journal.pone.0024051

Cited by 28 publications

(31 citation statements)

References 37 publications

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“…Based on the Refseq genes showing strong co-expression relationship with lincRNAs, a method commonly used for functional prediction of unknown genes3031, we predicted the biological functions for these 37 lincRNAs (see Methods). Enrichment analysis of GO terms and KEGG pathways showed that these down-regulated lincRNAs might be associated with transcriptional regulation, RNA processing and translational elongation processes, etc .…”

Section: Resultsmentioning

confidence: 99%

Long intergenic non-coding RNA expression signature in human breast cancer

Zhang

Wagner

Guo

et al. 2016

Sci Rep

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Breast cancer is a complex disease, characterized by gene deregulation. There is less systematic investigation of the capacity of long intergenic non-coding RNAs (lincRNAs) as biomarkers associated with breast cancer pathogenesis or several clinicopathological variables including receptor status and patient survival. We designed a two-stage study, including 1,000 breast tumor RNA-seq data from The Cancer Genome Atlas (TCGA) as the discovery stage, and RNA-seq data of matched tumor and adjacent normal tissue from 50 breast cancer patients as well as 23 normal breast tissue from healthy women as the replication stage. We identified 83 lincRNAs showing the significant expression changes in breast tumors with a false discovery rate (FDR) < 1% in the discovery dataset. Thirty-seven out of the 83 were validated in the replication dataset. Integrative genomic analyses suggested that the aberrant expression of these 37 lincRNAs was probably related with the expression alteration of several transcription factors (TFs). We observed a differential co-expression pattern between lincRNAs and their neighboring genes. We found that the expression levels of one lincRNA (RP5-1198O20 with Ensembl ID ENSG00000230615) were associated with breast cancer survival with P < 0.05. Our study identifies a set of aberrantly expressed lincRNAs in breast cancer.

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Section: Resultsmentioning

confidence: 99%

Long intergenic non-coding RNA expression signature in human breast cancer

Zhang

Wagner

Guo

et al. 2016

Sci Rep

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“…Similar to many other RNA molecules, lncRNAs are initially transcribed by Poly II in the nucleus and can be regulated by transcriptional factors (45). NF-kB signaling can regulate both the activation and inhibition of lncRNA expression (46,47).…”

Section: Discussionmentioning

confidence: 99%

The NF-κB–Responsive Long Noncoding RNA FIRRE Regulates Posttranscriptional Regulation of Inflammatory Gene Expression through Interacting with hnRNPU

Liu

Xie

et al. 2017

The Journal of Immunology

113

115

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Long noncoding RNAs, a newly identified class of noncoding RNAs, are important regulators of gene expression in innate immunity. We report in this study that the transcription of FIRRE, a conserved long noncoding RNA between humans and mice, is controlled by NF-κB signaling in macrophages and intestinal epithelial cells. Functionally, FIRRE appears to positively regulate the expression of several inflammatory genes in macrophages or intestinal epithelial cells in response to LPS stimulation via posttranscriptional mechanisms. Specifically, FIRRE physically interacts with heterogeneous nuclear ribonucleoproteins U, regulating the stability of mRNAs of selected inflammatory genes through targeting the AU-rich elements of their mRNAs in cells following LPS stimulation. Therefore, our data indicate a new regulatory role for NF-κB–responsive FIRRE in the posttranscriptional regulation of inflammatory genes in the innate immune system.

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“…For example, lincRNA are generally expressed at low or medium levels relative to mRNAs from protein coding genes (Cabili et al 2011;Garmire et al 2011). Thus the mRNA transcriptome and lincRNA transcriptome may yield different levels of power, even when they are generated from the same RNA-Seq experiments and the same biological samples.…”

Section: Differences In Experimental Power Based On Transcript Typementioning

confidence: 99%

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

Ching

Huang

Garmire

2014

RNA

Self Cite

213

201

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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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A Global Clustering Algorithm to Identify Long Intergenic Non-Coding RNA - with Applications in Mouse Macrophages

Cited by 28 publications

References 37 publications

Long intergenic non-coding RNA expression signature in human breast cancer

Long intergenic non-coding RNA expression signature in human breast cancer

The NF-κB–Responsive Long Noncoding RNA FIRRE Regulates Posttranscriptional Regulation of Inflammatory Gene Expression through Interacting with hnRNPU

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

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