miEAA 2.0: Integrating multi-species microRNA enrichment analysis and workflow management systems

Kern, Fabian; Fehlmann, Tobias; Solomon, Jeffrey; Schwed, Louisa; Backes, Christina; Meese, Eckart; Keller, Andreas

doi:10.1101/2020.03.05.978890

Cited by 9 publications

(9 citation statements)

References 39 publications

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“…The definition of hub miRNA was that the connection of the node was not less than 2 in one cancer. Specifically, the disease and pathway enrichment analyses were performed with the online tool miEAA (v2.0, ) ( 45 ). The miRNAs from the network were picked to run miEAA using the miRNA enrichment analysis, in which two categories (disease items from the MNDR database and pathway items from the miRWalk database) were selected with default parameters’ setting.…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

et al. 2021

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BackgroundN6-methyladenosine (m6A), the most abundant chemical modification on eukaryotic messenger RNA (mRNA), is modulated by three class of regulators namely “writers,” “erasers,” and “readers.” Increasing studies have shown that aberrant expression of m6A regulators plays broad roles in tumorigenesis and progression. However, it is largely unknown regarding the expression regulation for RNA m6A regulators in human cancers.ResultsHere we characterized the expression profiles of RNA m6A regulators in 13 cancer types with The Cancer Genome Atlas (TCGA) data. We showed that METTL14, FTO, and ALKBH5 were down-regulated in most cancers, whereas YTHDF1 and IGF2BP3 were up-regulated in 12 cancer types except for thyroid carcinoma (THCA). Survival analysis further revealed that low expression of several m6A regulators displayed longer overall survival times. Then, we analyzed microRNA (miRNA)-regulated and DNA methylation-regulated expression changes of m6A regulators in pan-cancer. In total, we identified 158 miRNAs and 58 DNA methylation probes (DMPs) involved in expression regulation for RNA m6A regulators. Furthermore, we assessed the survival significance of those regulatory pairs. Among them, 10 miRNAs and 7 DMPs may promote cancer initiation and progression; conversely, 3 miRNA/mRNA pairs in kidney renal clear cell carcinoma (KIRC) may exert tumor-suppressor function. These findings are indicative of their potential prognostic values. Finally, we validated two of those miRNA/mRNA pairs (hsa-miR-1307-3p/METTL14 and hsa-miR-204-5p/IGF2BP3) that could serve a critical role for potential clinical application in KIRC patients.ConclusionsOur findings highlighted the importance of upstream regulation (miRNA and DNA methylation) governing m6A regulators’ expression in pan-cancer. As a result, we identified several informative regulatory pairs for prognostic stratification. Thus, our study provides new insights into molecular mechanisms of m6A modification in human cancers.

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Section: Methodsmentioning

confidence: 99%

Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

et al. 2021

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“…For annotated miRNAs the lists of statistically significant differentially expressed miRNA lists were used as input to miRNA Enrichment Analysis and Annotation Tool (miEAA), that performs miRNA target prediction and over-representation analysis of gene ontology terms simultaneously by combining linked external databases [ 78 ]. Over-representation analysis was performed for Reactome Pathways via miRPathDB [ 79 ] for each up- and down-regulated miRNA list separately.…”

Section: Methodsmentioning

confidence: 99%

Cellular, Extracellular and Extracellular Vesicular miRNA Profiles of Pre-Ovulatory Follicles Indicate Signaling Disturbances in Polycystic Ovaries

Rooda

Hasan

Roos

et al. 2020

IJMS

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Cell-free RNAs have the potential to act as a means of gene expression regulation between cells and are therefore used as diagnostic markers describing the state of tissue environment. The origin and functions of such RNAs in human ovarian follicle, the environment of oocyte maturation, are unclear. The current study investigates the difference in the microRNA profiles of fertile women and polycystic ovary syndrome (PCOS) patients in three compartments from the same preovulatory follicle: mural granulosa cells (MGC), cell-free follicular fluid (FF), and extracellular vesicles (EV) of the FF by small RNA sequencing. In silico analysis was used for the prediction and over-representation of targeted pathways for the detected microRNAs. PCOS follicles were distinguished from normal tissue by the differential expression of 30 microRNAs in MGC and 10 microRNAs in FF (FDR < 0.1) that commonly regulate cytokine signaling pathways. The concentration of EV-s was higher in the FF of PCOS patients (p = 0.04) containing eight differentially expressed microRNAs (p < 0.05). In addition, we present the microRNA profiles of MGC, FF, and EV in the fertile follicle and demonstrate that microRNAs loaded into EVs target mRNAs of distinct signaling pathways in comparison to microRNAs in FF. To conclude, the three follicular compartments play distinct roles in the signaling disturbances associated with PCOS.

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“…For the NCER-PD microarray evaluation, samples were processed as previously described 30 . Based on the computed sample detection matrix, features were filtered to exclude miRNAs with a detection rate less than 50% in each sub-cohort (Idiopathic PD, Parkinsonism, Control).…”

Section: Quality Control and Sample Preprocessingmentioning

confidence: 99%

“…For the aging trajectories of de-regulated miRNAs, effect sizes were computed for binned ages at study consent values using a sliding window approach. For each integer i in the interval [30,80], effect size (cohen's d) was calculated for each miRNA using samples from the agebin [i, i + 10). Thereby, we required at least 10 samples from both case and control group and a minimum absolute value of 0.3 to consider the effect size for a miRNA at age i.…”

Section: Bioinformatics and Data Analysismentioning

confidence: 99%

“…To perform Principal Variance Component Analysis (PVCA) to estimate any influence of batch variables on the expression and the degree of expression variance associated to the annotation variables, we used the function pvcaBatchAssess from the R package pvca v1.28.0. MiRNA set enrichment (Kolmogorov-Smirnow test) and over-representation analyses (Hypergeometric tests) were performed with miEAA 2.0 30 . Analysis of significant overlaps between miRNAs detected in either NGS or the microarray data was accomplished with DynaVenn 29 , once for the two lists of miRNAs sorted by increasing AUC and once sorted by decreasing AUC.…”

Section: Bioinformatics and Data Analysismentioning

confidence: 99%

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Deep sncRNA-seq of the PPMI cohort to study Parkinson’s disease progression

Kern

Fehlmann

Violich

et al. 2020

Preprint

Self Cite

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Coding and non-coding RNAs have diagnostic and prognostic importance in Parkinson's diseases (PD). We studied circulating small non-coding RNAs (sncRNAs) in 7, 003 samples from two longitudinal PD cohorts (Parkinson's Progression Marker Initiative (PPMI) and Luxembourg Parkinson's Study (NCER-PD)) and modelled their influence on the transcriptome. First, we sequenced sncRNAs in 5, 450 blood samples of 1, 614 individuals in PPMI. The majority of 323 billion reads (59 million reads per sample) mapped to miRNAs. Other covered RNA classes include piRNAs, rRNAs, snoRNAs, tRNAs, scaRNAs, and snRNAs. De-regulated miRNAs were associated with the disease and disease progression and occur in two distinct waves in the third and seventh decade of live. Originating mostly from a characteristic set of immune cells they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. By profiling 1, 553 samples from 1, 024 individuals in the NCER-PD cohort using an independent technology, we validate relevant findings from the sequencing study. Finally, network analysis of sncRNAs and transcriptome sequencing of the original cohort identified regulatory modules emerging in progressing PD patients.miRNA studies to date, covering over 3, 000 patients and controls 21 .Advanced biomarker studies however require carefully designed cohorts. Already a few large-scale PD studies aiming to advance diagnosis, prognosis and therapeutics fulfil respective requirements 22-24 . Among them, the Parkinson's Progression Marker Initiative (PPMI) is a multi-cohort, longitudinal observational study designed to discover and validate objective biomarkers of Parkinson's 25 . The PPMI project constitutes a global effort of 33 clinical sites in 11 countries with regular study participant assessments (Figure 1a). It also features comprehensive clinical phenotyping to observe hundreds of characteristics of the known subtypes of the disease, such as the idiopathic and genetic forms. Further, longitudinal biosampling following rigorous Standard Operating Procedures (SOPs) is performed to set a framework for the discovery and validation of early-onset and prognostic biomarkers. To identify potential non-coding RNA and transcriptomic markers in PPMI we performed RNA-seq on blood samples drawn at each clinical visit. For short and long RNAs, we carried out optimized assays and sequenced separate aliquots from the same blood samples for paired RNA analyses. Here, we present the evaluation of the sncRNA-seq fraction for disease detection and progression tracking. We examine the potential of different classes of small RNAs but emphasise the role of miRNAs. We also validate relevant findings for miRNAs on the Luxembourg Parkinson's Study in the framework of the National Centre for Excellence in Research on PD (NCER-PD) cohort 22 , which was performed independently and with a different technology. Finally, we provide insights on how the key non-coding RNAs regulate gene expression by utilizing the long RNA sequencing data. Specific ana...

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miEAA 2.0: Integrating multi-species microRNA enrichment analysis and workflow management systems

Cited by 9 publications

References 39 publications

Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

Comprehensive Analysis of Expression Regulation for RNA m6A Regulators With Clinical Significance in Human Cancers

Cellular, Extracellular and Extracellular Vesicular miRNA Profiles of Pre-Ovulatory Follicles Indicate Signaling Disturbances in Polycystic Ovaries

Deep sncRNA-seq of the PPMI cohort to study Parkinson’s disease progression

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