Arena-Idb: a platform to build human non-coding RNA interaction networks

Bonnici, Vincenzo; Giorgio, De; Constantino, Giorgio; Liuni, Sabino; D’Elia, Domenica; Bombieri, Nicola; Licciulli, Flavio; Giugno, Rosalba

doi:10.1186/s12859-018-2298-8

Cited by 19 publications

(11 citation statements)

References 62 publications

(34 reference statements)

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“…We found that PUNISHER and MALAT1 are predicted to bind to hnRNPK, but not H19 and GAS5, based on the high-interaction scores that they give 57 and compared with other databases (Figure 3D; Table S6). 58,59 We then evaluated the effect of hnRNPK knockdown on sEV-incorporated PUNISHER levels and other lncRNA expression in ECs (Figure 3E). We found that upon hnRNPK knockdown in EC, the expression of PUNISHER is reduced, whereas other lncRNAs remain unchanged, suggesting that the interaction of hnRNPK to PUNISHER is specific (Figure 3E).…”

Section: Identification Of Candidate Lncrnasmentioning

confidence: 99%

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CAD increases the long noncoding RNA PUNISHER in small extracellular vesicles and regulates endothelial cell function via vesicular shuttling

Hosen¹,

Li²,

Liu³

et al. 2021

Molecular Therapy - Nucleic Acids

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Long noncoding RNAs (lncRNAs) have emerged as biomarkers and regulators of cardiovascular disease. However, the expression pattern of circulating extracellular vesicle (EV)-incorporated lncRNAs in patients with coronary artery disease (CAD) is still poorly investigated. A human lncRNA array revealed that certain EV-lncRNAs are significantly dysregulated in CAD patients. Circulating small EVs (sEVs) from patients with (n = 30) or without (n = 30) CAD were used to quantify PUNISHER (also known as AGAP2-antisense RNA 1 [AS1]), GAS5, MALAT1, and H19 RNA levels. PUNISHER (p = 0.002) and GAS5 (p = 0.02) were significantly increased in patients with CAD, compared to non-CAD patients. Fluorescent labeling and quantitative real-time PCR of sEVs demonstrated that functional PUNISHER was transported into the recipient cells. Mechanistically, the RNA-binding protein, heterogeneous nuclear ribonucleoprotein K (hnRNPK), interacts with PUNISHER, regulating its loading into sEVs. Knockdown of PUNISHER abrogated the EV-mediated effects on endothelial cell (EC) migration, proliferation, tube formation, and sprouting. Angiogenesis-related gene profiling showed that the expression of vascular endothelial growth factor A (VEGFA) RNA was significantly increased in EV recipient cells. Protein stability and RNA immunoprecipitation indicated that the PUNISHER-hnRNPK axis regulates the stability and binding of VEGFA mRNA to hnRNPK. Loss of PUNISHER in EVs abolished the EV-mediated promotion of VEGFA gene and protein expression. Intercellular transfer of EV-incorporated PUN-ISHER promotes a pro-angiogenic phenotype via a VEGFAdependent mechanism.

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Section: Identification Of Candidate Lncrnasmentioning

confidence: 99%

“…The enrichment was confirmed with input (10%) from the cellular lysates. (D) In silico target prediction of hnRNPK by using publicly available tools, based on the coverage score, by using RNAInter,57 RNACentral,58 and Arena-Idb59 databases. Based on their interaction scores, only MALAT1 and AGAP2-AS1 (PUNISHER) are predicted to bind to hnRNPK.…”

mentioning

confidence: 99%

CAD increases the long noncoding RNA PUNISHER in small extracellular vesicles and regulates endothelial cell function via vesicular shuttling

Hosen¹,

Li²,

Liu³

et al. 2021

Molecular Therapy - Nucleic Acids

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“…Expression data for such analyses can be found in widely used repositories such as GEO (Barrett et al, 2013) or ArrayExpress (Parkinson et al, 2007). Notably, Arena-Idb (Bonnici et al, 2018) repository can be used for human non-coding RNAs interactions.…”

Section: Gene Co-expression Networkmentioning

confidence: 99%

A Guide to Conquer the Biological Network Era Using Graph Theory

Koutrouli

Karatzas

Páez-Espino

et al. 2020

Front. Bioeng. Biotechnol.

202

148

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Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer-and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

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“…Graphs are a well-known mathematical structure used to encode relationships among elements of a set. They are employed in the representation of many biochemical systems at various levels from molecular representation [ 1 ], to protein-protein or RNA-mate interaction networks [ 2 , 3 ] including also disease characterization [ 4 ]. The search for substructures, also called subgraphs, in biochemical systems is widely involved in many bioinformatic approaches as well as in the field of computational chemistry.…”

Section: Introductionmentioning

confidence: 99%

GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases

et al. 2021

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Background Graphs are mathematical structures widely used for expressing relationships among elements when representing biomedical and biological information. On top of these representations, several analyses are performed. A common task is the search of one substructure within one graph, called target. The problem is referred to as one-to-one subgraph search, and it is known to be NP-complete. Heuristics and indexing techniques can be applied to facilitate the search. Indexing techniques are also exploited in the context of searching in a collection of target graphs, referred to as one-to-many subgraph problem. Filter-and-verification methods that use indexing approaches provide a fast pruning of target graphs or parts of them that do not contain the query. The expensive verification phase is then performed only on the subset of promising targets. Indexing strategies extract graph features at a sufficient granularity level for performing a powerful filtering step. Features are memorized in data structures allowing an efficient access. Indexing size, querying time and filtering power are key points for the development of efficient subgraph searching solutions. Results An existing approach, GRAPES, has been shown to have good performance in terms of speed-up for both one-to-one and one-to-many cases. However, it suffers in the size of the built index. For this reason, we propose GRAPES-DD, a modified version of GRAPES in which the indexing structure has been replaced with a Decision Diagram. Decision Diagrams are a broad class of data structures widely used to encode and manipulate functions efficiently. Experiments on biomedical structures and synthetic graphs have confirmed our expectation showing that GRAPES-DD has substantially reduced the memory utilization compared to GRAPES without worsening the searching time. Conclusion The use of Decision Diagrams for searching in biochemical and biological graphs is completely new and potentially promising thanks to their ability to encode compactly sets by exploiting their structure and regularity, and to manipulate entire sets of elements at once, instead of exploring each single element explicitly. Search strategies based on Decision Diagram makes the indexing for biochemical graphs, and not only, more affordable allowing us to potentially deal with huge and ever growing collections of biochemical and biological structures.

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Arena-Idb: a platform to build human non-coding RNA interaction networks

Cited by 19 publications

References 62 publications

CAD increases the long noncoding RNA PUNISHER in small extracellular vesicles and regulates endothelial cell function via vesicular shuttling

CAD increases the long noncoding RNA PUNISHER in small extracellular vesicles and regulates endothelial cell function via vesicular shuttling

A Guide to Conquer the Biological Network Era Using Graph Theory

GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases

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