Robustness of differential gene expression analysis of RNA-seq

Stupnikov, Alexey; McInerney, Caitríona E.; Savage, Kienan I.; McIntosh, Stuart; Emmert‐Streib, Frank; Kennedy, Richard D.; Salto-Tellez, Manuel; Prise, Kevin M.; McArt, Darragh G.

doi:10.1016/j.csbj.2021.05.040

Cited by 60 publications

(46 citation statements)

References 52 publications

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“…Protocols used for the sequencing RNA library preparation often require at least 100 ng total RNA, and unfortunately, some biological matrices, such as an oropharyngeal swab, do not satisfy this request. In this case, in order to comply with guidelines and to increase the likelihood to obtain sufficient RNA amount in terms of the coverage and depth for the robustness of the sequencing data [29], RNA enrichment through amplification reactions of the polymerase enzymes is necessary [3]. However, the nature of the polymerases is to introduce errors of the read and/or other synthesis artifacts [30].…”

Section: Discussionmentioning

confidence: 99%

Direct RNA Nanopore Sequencing of SARS-CoV-2 Extracted from Critical Material from Swabs

Vacca

Fiannaca

Tramuto

et al. 2022

Life

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In consideration of the increasing prevalence of COVID-19 cases in several countries and the resulting demand for unbiased sequencing approaches, we performed a direct RNA sequencing (direct RNA seq.) experiment using critical oropharyngeal swab samples collected from Italian patients infected with SARS-CoV-2 from the Palermo region in Sicily. Here, we identified the sequences SARS-CoV-2 directly in RNA extracted from critical samples using the Oxford Nanopore MinION technology without prior cDNA retrotranscription. Using an appropriate bioinformatics pipeline, we could identify mutations in the nucleocapsid (N) gene, which have been reported previously in studies conducted in other countries. In conclusion, to the best of our knowledge, the technique used in this study has not been used for SARS-CoV-2 detection previously owing to the difficulties in the extraction of RNA of sufficient quantity and quality from routine oropharyngeal swabs. Despite these limitations, this approach provides the advantages of true native RNA sequencing and does not include amplification steps that could introduce systematic errors. This study can provide novel information relevant to the current strategies adopted in SARS-CoV-2 next-generation sequencing.

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Section: Discussionmentioning

confidence: 99%

Direct RNA Nanopore Sequencing of SARS-CoV-2 Extracted from Critical Material from Swabs

Vacca

Fiannaca

Tramuto

et al. 2022

Life

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“…Comparative studies of analysis of transcriptome by different software tools have been reported and different softwares often give different results [ 6 , 14 , 30 – 33 ]. Researchers have to decide an appropriate software tool and pipeline based on their experimental requirements.…”

Section: Discussionmentioning

confidence: 99%

A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses

Thawng

Smith

2022

BMC Genomics

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Background In this comparative study we evaluate the performance of four software tools: DNAstar-D (DESeq2), DNAstar-E (edgeR), CLC Genomics and Partek Flow for identification of differentially expressed genes (DEGs) using a transcriptome of E. coli. The RNA-seq data are from the effect of below-background radiation 5.5 nGy total dose (0.2nGy/hr) on E. coli grown shielded from natural radiation 655 m below ground in a pre-World War II steel vault. The gene expression response to three supplemented sources of radiation designed to mimic natural background, 1952 – 5720 nGy in total dose (71–208 nGy/hr), are compared to this “radiation-deprived” treatment. In addition, RNA-seq data of Caenorhabditis elegans nematode from similar radiation treatments was analyzed by three of the software packages. Results In E. coli, the four software programs identified one of the supplementary sources of radiation (KCl) to evoke about 5 times more transcribed genes than the minus-radiation treatment (69–114 differentially expressed genes, DEGs), and so the rest of the analyses used this KCl vs “Minus” comparison. After imposing a 30-read minimum cutoff, one of the DNAStar options shared two of the three steps (mapping, normalization, and statistic) with Partek Flow (they both used median of ratios to normalize and the DESeq2 statistical package), and these two programs identified the highest number of DEGs in common with each other (53). In contrast, when the programs used different approaches in each of the three steps, between 31 and 40 DEGs were found in common. Regarding the extent of expression differences, three of the four programs gave high fold-change results (15–178 fold), but one (DNAstar’s DESeq2) resulted in more conservative fold-changes (1.5–3.5). In a parallel study comparing three qPCR commercial validation software programs, these programs also gave variable results as to which genes were significantly regulated. Similarly, the C. elegans analysis showed exaggerated fold-changes in CLC and DNAstar’s edgeR while DNAstar-D was more conservative. Conclusions Regarding the extent of expression (fold-change), and considering the subtlety of the very low level radiation treatments, in E. coli three of the four programs gave what we consider exaggerated fold-change results (15 – 178 fold), but one (DNAstar’s DESeq2) gave more realistic fold-changes (1.5–3.5). When RT-qPCR validation comparisons to transcriptome results were carried out, they supported the more conservative DNAstar-D’s expression results. When another model organism’s (nematode) response to these radiation differences was similarly analyzed, DNAstar-D also resulted in the most conservative expression patterns. Therefore, we would propose DESeq2 (“DNAstar-D”) as an appropriate software tool for differential gene expression studies for treatments expected to give subtle transcriptome responses.

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“…The heatmap of DEGs was generated according to the results from EdgeR, DESeq, and Limma analysis (Figure S2). We integrated the results of the three methods to reduce the error, and the results were visualized by Venn diagram (Stupnikov et al, 2021). As shown, 1,606 upregulated and 1,964 downregulated genes were identified (Figures 1B, C).…”

Section: Differential Analysis Selected 1606 Upregulated Genes and 19...mentioning

confidence: 99%

Bioinformatics Analysis Highlights Five Differentially Expressed Genes as Prognostic Biomarkers of Cervical Cancer and Novel Option for Anticancer Treatment

Cui

et al. 2022

Front. Cell. Infect. Microbiol.

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Cervical cancer is one of the most common gynecological malignancies and is related to human papillomavirus (HPV) infection, especially high-risk type HPV16 and HPV18. Aberrantly expressed genes are involved in the development of cervical cancer, which set a genetic basis for patient prognosis. In this study, we identified a set of aberrantly expressed key genes from The Cancer Genome Atlas (TCGA) database, which could be used to accurately predict the survival rate of patients with cervical squamous cell carcinoma (CESC). A total of 3,570 genes that are differentially expressed between normal and cancerous samples were analyzed by the algorithm of weighted gene co-expression network analysis (WGCNA): 1,606 differentially expressed genes (DEGs) were upregulated, while 1,964 DEGs were downregulated. Analysis of these DEGs divided them into 7 modules including 76 hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis revealed a significant increase of genes related to cell cycle, DNA replication, p53 signaling pathway, cGMP-PKG signaling pathway, and Fanconi anemia (FA) pathway in CESC. These biological activities are previously reported to associate with cervical cancer or/and HPV infection. Finally, we highlighted 5 key genes (EMEMP2, GIMAP4, DYNC2I2, FGF13-AS1, and GIMAP1) as robust prognostic markers to predict patient’s survival rate (p = 3.706e-05) through univariate and multivariate regression analyses. Thus, our study provides a novel option to set up several biomarkers for cervical cancer prognosis and anticancer drug targets.

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Robustness of differential gene expression analysis of RNA-seq

Abstract: Graphical abstract

Cited by 60 publications

References 52 publications

Direct RNA Nanopore Sequencing of SARS-CoV-2 Extracted from Critical Material from Swabs

Direct RNA Nanopore Sequencing of SARS-CoV-2 Extracted from Critical Material from Swabs

A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses

Bioinformatics Analysis Highlights Five Differentially Expressed Genes as Prognostic Biomarkers of Cervical Cancer and Novel Option for Anticancer Treatment

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