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miRspongeR 2.0: an enhanced R package for exploring miRNA sponge regulation.
Zhang, Junpeng; Liu, Lin; Zhang, Wu; Li, Xiaomei; Zhao, Chunwen; Li, Sijing; Li, Jiuyong; Le, Thuc Duy.
Afiliación
  • Zhang J; Department of Information and Electronic Engineering, School of Engineering, Dali University, Dali 671003, China.
  • Liu L; UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
  • Zhang W; Department of Molecular Biology, School of Agriculture and Biological Sciences, Dali University, Dali 671003, China.
  • Li X; UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
  • Zhao C; Department of Information and Electronic Engineering, School of Engineering, Dali University, Dali 671003, China.
  • Li S; Department of Information and Electronic Engineering, School of Engineering, Dali University, Dali 671003, China.
  • Li J; UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
  • Le TD; UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, Australia.
Bioinform Adv ; 2(1): vbac063, 2022.
Article en En | MEDLINE | ID: mdl-36699386
Summary: MicroRNA (miRNA) sponges influence the capability of miRNA-mediated gene silencing by competing for shared miRNA response elements and play significant roles in many physiological and pathological processes. It has been proved that computational or dry-lab approaches are useful to guide wet-lab experiments for uncovering miRNA sponge regulation. However, all of the existing tools only allow the analysis of miRNA sponge regulation regarding a group of samples, rather than the miRNA sponge regulation unique to individual samples. Furthermore, most existing tools do not allow parallel computing for the fast identification of miRNA sponge regulation. Here, we present an enhanced version of our R/Bioconductor package, miRspongeR 2.0. Compared with the original version introduced in 2019, this package extends the resolution of miRNA sponge regulation from the multi-sample level to the single-sample level. Moreover, it supports the identification of miRNA sponge networks using parallel computing, and the construction of sample-sample correlation networks. It also provides more computational methods to infer miRNA sponge regulation and expands the ground truth for validation. With these new features, we anticipate that miRspongeR 2.0 will further accelerate the research on miRNA sponges with higher resolution and more utilities. Availability and implementation: http://bioconductor.org/packages/miRspongeR/. Supplementary information: Supplementary data are available at Bioinformatics Advances online.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Bioinform Adv Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Bioinform Adv Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido