miRPathDB: a new dictionary on microRNAs and target pathways

Backes, Christina; Kehl, Tim; Stöckel, Daniel; Fehlmann, Tobias; Schneider, Lara; Meese, Eckart; Lenhof, Hans‐Peter; Keller, Andreas

doi:10.1093/nar/gkw926

Cited by 111 publications

(91 citation statements)

References 28 publications

(40 reference statements)

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“…We used miRSystem, an integrated miRNA function prediction website based on multiple miRNA target prediction algorithms, to facilitate the summarization of miRNA-mRNA interaction [106]. There are other similar websites, such as miRGator (available online: ) [107] and miRPathDB (available online: ) [108]. All of these tools provide a similar function and deliver comparable results.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus

Ding

Liang

et al. 2017

IJMS

109

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MicroRNAs (miRNAs) are small noncoding RNAs that modulate the cellular transcriptome at the post-transcriptional level. miRNA plays important roles in different disease manifestation, including type 2 diabetes mellitus (T2DM). Many studies have characterized the changes of miRNAs in T2DM, a complex systematic disease; however, few studies have integrated these findings and explored the functional effects of the dysregulated miRNAs identified. To investigate the involvement of miRNAs in T2DM, we obtained and analyzed all relevant studies published prior to 18 October 2016 from various literature databases. From 59 independent studies that met the inclusion criteria, we identified 158 dysregulated miRNAs in seven different major sample types. To understand the functional impact of these deregulated miRNAs, we performed targets prediction and pathway enrichment analysis. Results from our analysis suggested that the altered miRNAs are involved in the core processes associated with T2DM, such as carbohydrate and lipid metabolisms, insulin signaling pathway and the adipocytokine signaling pathway. This systematic survey of dysregulated miRNAs provides molecular insights on the effect of deregulated miRNAs in different tissues during the development of diabetes. Some of these miRNAs and their mRNA targets may have diagnostic and/or therapeutic utilities in T2DM.

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Section: Discussionmentioning

confidence: 99%

A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus

Ding

Liang

et al. 2017

IJMS

109

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“…(D) MicroRNA pathway analysis. Pathways modulated by miRNAs were identified using the miRPathDB (Backes et al 2017). A heat map of pathways targeted by at least two miRNAs (of the 13 influenced by SIT, q < 0.05), as indicated by experimental evidence are displayed.…”

Section: Discussionmentioning

confidence: 99%

“…MiRNA target pathways were identified using the miRPathDB (Backes et al. ). The miRNA expression differences between paired samples were presented as log fold change.…”

Section: Methodsmentioning

confidence: 99%

“…Normalization factors were calculated to scale the raw library sizes and a common negative binomial dispersion parameter estimated using the Bioconductor package edgeR (Robinson et al 2010;McCarthy et al 2012) and normalized using RUVSeq (Risso et al 2014), which reduces the variability in an unbiased manner by estimating variation by residuals. MiRNA target pathways were identified using the miR-PathDB (Backes et al 2017). The miRNA expression differences between paired samples were presented as log fold change.…”

Section: Bioinformaticsmentioning

confidence: 99%

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Small non-coding RNAs are altered by short-term sprint interval training in men

et al. 2018

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Small non‐coding RNAs (ncRNAs) are emerging as important molecules for normal biological processes and are deregulated in disease. Exercise training is a powerful therapeutic strategy that prevents cardiometabolic disease and improves cardiorespiratory fitness and performance. Despite the known systemic health benefits of exercise training, the underlying molecular mechanisms are incompletely understood. Recent evidence suggests a role for epigenetic mechanisms, such as microRNAs, but whether other small ncRNAs are modulated by chronic exercise training is unknown. Here, we used small RNA sequencing to explore whether sprint interval training (SIT) controls the abundance of circulating small ncRNAs in human whole blood samples. Ten healthy men performed SIT three times a week for 6 weeks. After training, subjects showed marked improvements in maximal oxygen consumption and cycling performance with concurrent changes to the abundance of diverse species of circulating small ncRNAs (n = 1266 small ncRNAs, n = 13 microRNAs, q < 0.05). Twelve microRNAs altered by 6 weeks of SIT were ubiquitously expressed microRNAs and two regulated important signaling pathways, including p53, thyroid hormone and cell cycle signaling. MicroRNAs altered by 6 weeks of SIT were unchanged after a single session of SIT (n = 24, all P > 0.05). Relative to older individuals, younger subjects exhibited an increased acute SIT‐induced fold change in miR‐1301‐3p (P = 0.02) – a microRNA predicted to target mRNAs involved in alternative splicing, phosphoprotein and chromosomal rearrangement processes (all P < 0.001). Our findings indicate many species of circulating small ncRNAs are modulated by exercise training and that they could control signaling pathways responsible for health benefits achieved from exercise.

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“…Computational target prediction is coordinated in a sequence-speciic manner and the target genes are normally predicted based on information derived from the potency of binding between miRNA and putative targets. Generally, the methods for computational prediction of miRNA targets can be grouped in single platforms such as TargetScan [95], PicTar [115], RNAhybrid [105] or multiple platforms such as miRwalk [116], TarBases [121], miRecords [117] as well as integrative platforms which include downstream analyses of putative target genes such as DIANA-microT-CDS [96], miRPathDB [184], etc. A collection of tools for miRNA target prediction are available at htps://omictools.com/mirna-targetprediction-category and htps://tools4mirs.org/software/target_prediction/ [185] ( Table 3).…”

Section: Bioinformatics Tools For Target Prediction and Functional Inmentioning

confidence: 99%

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

Ngoc¹,

Dudemaine²,

Fomenky³

et al. 2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

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The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a beter understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identiication of ncRNAs and functional inference of identiied ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species.

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miRPathDB: a new dictionary on microRNAs and target pathways

Cited by 111 publications

References 28 publications

A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus

A Systematic Study of Dysregulated MicroRNA in Type 2 Diabetes Mellitus

Small non-coding RNAs are altered by short-term sprint interval training in men

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

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