ArticleBMC genomics2022
A comparison of transcriptome analysis methods with reference genome.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
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- Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2.NAR genomics and bioinformatics · 2025Article
- Biosynthesis of Camphane Volatile Terpenes inPlants (Basel, Switzerland) · 2025Article
- Transcriptomic signatures of prostate cancer progression: a comprehensive RNA-seq study.3 Biotech · 2025Article
- Multi-Omics Profiling of Lipid Variation and Regulatory Mechanisms in Poultry Breast Muscles.Animals : an open access journal from MDPI · 2025Article
- Oocyte transcriptomes and follicular fluid proteomics of ovine atretic follicles reveal the underlying mechanisms of oocyte degeneration.BMC genomics · 2025Article
- Comparison of baseline global gene expression profiles of prostate cancer cell lines LNCaP and DU145.BMC research notes · 2024Article
- Where the "ruber" Meets the Road: Using the Genome of the Red Diamond Rattlesnake to Unravel the Evolutionary Processes Driving Venom Evolution.Genome biology and evolution · 2024Article
- Integrating Large Language Models in Bioinformatics Education for Medical Students: Opportunities and Challenges.Annals of biomedical engineering · 2024Article
- A comprehensive workflow for optimizing RNA-seq data analysis.BMC genomics · 2024Article
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- Transcriptomic and metabolomic dissection of skeletal muscle of crossbred Chongming white goats with different meat production performance.BMC genomics · 2024Article
- The repertoire and structure of adhesion GPCR transcript variants assembled from publicly available deep-sequenced human samples.Nucleic acids research · 2024Article
- Article
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7 authors.
Funding
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Abstract
backgroundThe application of RNA-seq technology has become more extensive and the number of analysis procedures available has increased over the past years. Selecting an appropriate workflow has become an important issue for researchers in the field.
methodsIn our study, six popular analytical procedures/pipeline were compared using four RNA-seq datasets from mouse, human, rat, and macaque, respectively. The gene expression value, fold change of gene expression, and statistical significance were evaluated to compare the similarities and differences among the six procedures. qRT-PCR was performed to validate the differentially expressed genes (DEGs) from all six procedures.
resultsCufflinks-Cuffdiff demands the highest computing resources and Kallisto-Sleuth demands the least. Gene expression values, fold change, p and q values of differential expression (DE) analysis are highly correlated among procedures using HTseq for quantification. For genes with medium expression abundance, the expression values determined using the different procedures were similar. Major differences in expression values come from genes with particularly high or low expression levels. HISAT2-StringTie-Ballgown is more sensitive to genes with low expression levels, while Kallisto-Sleuth may only be useful to evaluate genes with medium to high abundance. When the same thresholds for fold change and p value are chosen in DE analysis, StringTie-Ballgown produce the least number of DEGs, while HTseq-DESeq2, -edgeR or -limma generally produces more DEGs. The performance of Cufflinks-Cuffdiff and Kallisto-Sleuth varies in different datasets. For DEGs with medium expression levels, the biological verification rates were similar among all procedures.
conclusionResults are highly correlated among RNA-seq analysis procedures using HTseq for quantification. Difference in gene expression values mainly come from genes with particularly high or low expression levels. Moreover, biological validation rates of DEGs from all six procedures were similar for genes with medium expression levels. Investigators can choose analytical procedures according to their available computer resources, or whether genes of high or low expression levels are of interest. If computer resources are abundant, one can utilize multiple procedures to obtain the intersection of results to get the most reliable DEGs, or to obtain a combination of results to get a more comprehensive DE profile for transcriptomes.
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