Differential gene expression tools exhibit substandard performance for long non-coding RNA-sequencing data
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE104756 | GEO | 2018/05/23
REPOSITORIES: GEO
Similar Datasets
Project description:BackgroundLong non-coding RNAs (lncRNAs) are typically expressed at low levels and are inherently highly variable. This is a fundamental challenge for differential expression (DE) analysis. In this study, the performance of 25 pipelines for testing DE in RNA-seq data is comprehensively evaluated, with a particular focus on lncRNAs and low-abundance mRNAs. Fifteen performance metrics are used to evaluate DE tools and normalization methods using simulations and analyses of six diverse RNA-seq datasets.ResultsGene expression data are simulated using non-parametric procedures in such a way that realistic levels of expression and variability are preserved in the simulated data. Throughout the assessment, results for mRNA and lncRNA were tracked separately. All the pipelines exhibit inferior performance for lncRNAs compared to mRNAs across all simulated scenarios and benchmark RNA-seq datasets. The substandard performance of DE tools for lncRNAs applies also to low-abundance mRNAs. No single tool uniformly outperformed the others. Variability, number of samples, and fraction of DE genes markedly influenced DE tool performance.ConclusionsOverall, linear modeling with empirical Bayes moderation (limma) and a non-parametric approach (SAMSeq) showed good control of the false discovery rate and reasonable sensitivity. Of note, for achieving a sensitivity of at least 50%, more than 80 samples are required when studying expression levels in realistic settings such as in clinical cancer research. About half of the methods showed a substantial excess of false discoveries, making these methods unreliable for DE analysis and jeopardizing reproducible science. The detailed results of our study can be consulted through a user-friendly web application, giving guidance on selection of the optimal DE tool ( http://statapps.ugent.be/tools/AppDGE/ ).
| S-EPMC6058388 | biostudies-literature
Project description:Differential gene expression tools exhibit substandard performance for long non-coding RNA-sequencing data
| PRJNA413772 | ENA
Project description:single cell polyA+ and total RNA-seq data was generated for NGP, SK-N-BE-2C and SHSY5Y-MYCN-TR cells
2019-01-01 | GSE119983 | GEO
Project description:single cell polyA+ and total RNA-seq data was generated for NGP, SK-N-BE-2C and SHSY5Y-MYCN-TR cells
2019-01-01 | GSE119982 | GEO
Project description:single cell polyA+ and total RNA-seq data was generated for NGP, SK-N-BE-2C and SHSY5Y-MYCN-TR cells
2019-01-01 | GSE119936 | GEO
Project description:This dataset contains whole-genome RNA sequencing results from cortical neuronal cultures and serves as the basis for characterization of extra-coding RNA species from neuronal systems. This experiment contains six biological samples, each of which underwent PolyA+ and PolyA- RNA-seq. Samples were either unstimulated (i.e., treated with media alone; samples V1 and V2), stimulated with 25mM potassium chloride for 1hr (K1, K2) or inactivated with tetrodotoxin for 1hr (T1, T2). Datasets were obtained using RNA-seq from PolyA+ fractions or PolyA- fractions of RNA. PolyA- fractions are denoted "ec". Thus, 12 samples are listed here due to the difference in RNA library preparation.
2016-05-31 | E-GEOD-64902 | biostudies-arrayexpress
Project description:Arraystar SE-lncRNAs Arrays was used to systematically profile the differentially expressed SE-lncRNAs along with corresponding SE-regulated protein coding genes from eight leiomyomas and paired myometrium. The analysis indicated 7680 SE-lncRNAs were expressed, of which 721 SE-lncRNAs were increased, while 247 SE-lncRNAs were decreased by 1.5-fold or greater in leiomyoma. Thirteen novel SE-lncRNAs and their corresponding protein coding genes were selected, and their expression was confirmed in eighty-one paired leiomyoma tissues by quantitative real-time PCR. The thirteen pairs of SE-lncRNAs and their corresponding protein coding genes included RP11-353N14.2/CBX4, SOCS2-AS1/SOCS2, RP1-170O19.14/HOXA11, CASC15/PRL, EGFLAM-AS1/EGFLAM, RP11-225H22/NEURL1, RP5-1086K13.1/CD58, AC092839.3/SPTBN1, RP11-69I8.3/CTGF, TM4SF1-AS1/TM4SF1, RP11-373D23/FOSL2, RP11-399K21.11/COMTD1 and CTB-113P19.1/SPARC. Collectively, our results indicate that the differential expression of SE in fibroids is another mechanism contributing to dysregulation of protein coding genes in fibroids.
2022-01-11 | GSE193320 | GEO
Project description:Sequencing of polyA RNA fraction from MT oocytes and 1cell embryos.
2017-05-21 | E-MTAB-4775 | biostudies-arrayexpress
Project description:Accumulated evidence suggests that the widely expressed long-non-coding RNAs (lncRNAs) are involved in biogenesis. Some aberrant lncRNAs are closely related to pathological changes, for instance, in cancer. Both in tumorigenesis and cancer progression, depending on the interplay with cellular molecules, lncRNAs can modulate transcriptional interference, chromatin remodeling, post-translational regulation and protein modification, and further interfere with signaling pathways. Aiming to the diagnosis/ prognosis markers or potential therapeutical targets, it is important to figure out the specific mechanism and the tissue-specific expressing patterns of lncRNAs. Generally, the bioinformatics analysis is the first step. More and more in silico databases are increasing. But the existing integrative online platforms' functions are not only having their unique features but also share some common features, which may lead to a waste of time for researchers. Here, we reviewed these web tools according to the functions. For each database, we clarified the data source, analysis method and the evidence that the analysis result is derived from. This review also illustrated examples in practical use for a specific lncRNA by these web tools. It will provide convenience for researchers to quickly choose the appropriate bioinformatics web tools in oncology studies.
| S-EPMC8299716 | biostudies-literature
Project description:The increasing amount of transcriptomic data has brought to light vast numbers of potential novel RNA transcripts. Accurately distinguishing novel long non-coding RNAs (lncRNAs) from protein-coding messenger RNAs (mRNAs) has challenged bioinformatic tool developers. Most recently, tools implementing deep learning architectures have been developed for this task, with the potential of discovering sequence features and their interactions still not surfaced in current knowledge. We compared the performance of deep learning tools with other predictive tools that are currently used in lncRNA coding potential prediction. A total of 15 tools representing the variety of available methods were investigated. In addition to known annotated transcripts, we also evaluated the use of the tools in actual studies with real-life data. The robustness and scalability of the tools' performance was tested with varying sized test sets and test sets with different proportions of lncRNAs and mRNAs. In addition, the ease-of-use for each tested tool was scored. Deep learning tools were top performers in most metrics and labelled transcripts similarly with each other in the real-life dataset. However, the proportion of lncRNAs and mRNAs in the test sets affected the performance of all tools. Computational resources were utilized differently between the top-ranking tools, thus the nature of the study may affect the decision of choosing one well-performing tool over another. Nonetheless, the results suggest favouring the novel deep learning tools over other tools currently in broad use.
| S-EPMC9123429 | biostudies-literature