A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies

Merino, Gabriela Alejandra; Conesa, Ana; Fernández, Elmer

doi:10.1093/bib/bbx122

Cited by 35 publications

(12 citation statements)

References 32 publications

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“…The differential expression (DE) analysis method usually has the most substantial impact on results (64,65). For this study, we used DESeq2 to study DE of lncRNA detected in macrophages from JD(+) and JD(-) cows.…”

Section: Differential Expression (De) Of Lncrna In Macrophages From Jd(+) and Jd(-) Cowsmentioning

confidence: 99%

Identification of Long Non-coding RNA Isolated From Naturally Infected Macrophages and Associated With Bovine Johne's Disease in Canadian Holstein Using a Combination of Neural Networks and Logistic Regression

et al. 2021

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Mycobacterium avium ssp. paratuberculosis (MAP) causes chronic enteritis in most ruminants. The pathogen MAP causes Johne's disease (JD), a chronic, incurable, wasting disease. Weight loss, diarrhea, and a gradual drop in milk production characterize the disease's clinical phase, culminating in death. Several studies have characterized long non-coding RNA (lncRNA) in bovine tissues, and a previous study characterizes (lncRNA) in macrophages infected with MAP in vitro. In this study, we aim to characterize the lncRNA in macrophages from cows naturally infected with MAP. From 15 herds, feces and blood samples were collected for each cow older than 24 months, twice yearly over 3–5 years. Paired samples were analyzed by fecal PCR and blood ELISA. We used RNA-seq data to study lncRNA in macrophages from 33 JD(+) and 33 JD(–) dairy cows. We performed RNA-seq analysis using the “new Tuxedo” suite. We characterized lncRNA using logistic regression and multilayered neural networks and used DESeq2 for differential expression analysis and Panther and Reactome classification systems for gene ontology (GO) analysis. The study identified 13,301 lncRNA, 605 of which were novel lncRNA. We found seven genes close to differentially expressed lncRNA, including CCDC174, ERI1, FZD1, TWSG1, ZBTB38, ZNF814, and ZSCAN4. None of the genes associated with susceptibility to JD have been cited in the literature. LncRNA target genes were significantly enriched for biological process GO terms involved in immunity and nucleic acid regulation. These include the MyD88 pathway (TLR5), GO:0043312 (neutrophil degranulation), GO:0002446 (neutrophil-mediated immunity), and GO:0042119 (neutrophil activation). These results identified lncRNA with potential roles in host immunity and potential candidate genes and pathways through which lncRNA might function in response to MAP infection.

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Section: Differential Expression (De) Of Lncrna In Macrophages From Jd(+) and Jd(-) Cowsmentioning

confidence: 99%

Identification of Long Non-coding RNA Isolated From Naturally Infected Macrophages and Associated With Bovine Johne's Disease in Canadian Holstein Using a Combination of Neural Networks and Logistic Regression

et al. 2021

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“…Since the goal here is to investigate quantification accuracy directly, the methods in Teng et al are not directly applicable. Other studies focus only on single-cell data [ 17 ], or on differential splicing [ 18 ]. Commonly, RNA-Seq transcript level quantification is validated by PCR.…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of full-length isoform quantification from RNA-Seq

et al. 2021

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Background Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short. Results Here we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control. Conclusions Salmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.

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“…Since the goal here is to investigate quantification accuracy directly, the methods in Teng et al are not directly applicable. Other studies focus only on single-cell data (14), or on differential splicing (15).…”

Section: Introductionmentioning

confidence: 99%

Comparative evaluation of full-length isoform quantification from RNA-Seq

Sarantopoulou

Nayak

Brooks

et al. 2019

Preprint

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Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and an area of active development. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are typically short. We have generated realistic benchmarking data, and have performed a comprehensive comparative analysis of isoform quantification, including evaluating them on the level of differential expression analysis.Genome, transcriptome and pseudo alignment-based methods are included; and a naive approach is included to establish a baseline. Kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform considerably better than the naive approach. We determine the effect of structural parameters, such as number of exons or number of isoforms, on accuracy. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification should be employed selectively.Sarantopoulou et al, Benchmarking of FLI quantification for RNA-Seq -1

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A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies

Cited by 35 publications

References 32 publications

Identification of Long Non-coding RNA Isolated From Naturally Infected Macrophages and Associated With Bovine Johne's Disease in Canadian Holstein Using a Combination of Neural Networks and Logistic Regression

Identification of Long Non-coding RNA Isolated From Naturally Infected Macrophages and Associated With Bovine Johne's Disease in Canadian Holstein Using a Combination of Neural Networks and Logistic Regression

Comparative evaluation of full-length isoform quantification from RNA-Seq

Comparative evaluation of full-length isoform quantification from RNA-Seq

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