Background: Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software. Results: Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time. Conclusions: SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly.

SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks / Paci, P.; Fiscon, G.. - In: BMC BIOINFORMATICS. - ISSN 1471-2105. - 23:1(2022), pp. 1-13. [10.1186/s12859-022-04695-x]

SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks

Paci P.
;
Fiscon G.
2022

Abstract

Background: Recently, we developed a mathematical model for identifying putative competing endogenous RNA (ceRNA) interactions. This methodology has aroused a broad acknowledgment within the scientific community thanks to the encouraging results achieved when applied to breast invasive carcinoma, leading to the identification of PVT1, a long non-coding RNA functioning as ceRNA for the miR-200 family. The main shortcoming of the model is that it is no freely available and implemented in MATLAB®, a proprietary programming platform requiring a paid license for installing, operating, manipulating, and running the software. Results: Breaking through these model limitations demands to distribute it in an open-source, freely accessible environment, such as R, designed for an ordinary audience of users that are not able to afford a proprietary solution. Here, we present SPINNAKER (SPongeINteractionNetworkmAKER), the open-source version of our widely established mathematical model for predicting ceRNAs crosstalk, that is released as an exhaustive collection of R functions. SPINNAKER has been even designed for providing many additional features that facilitate its usability, make it more efficient in terms of further implementation and extension, and less intense in terms of computational execution time. Conclusions: SPINNAKER source code is freely available at https://github.com/sportingCode/SPINNAKER.git together with a thoroughgoing PPT-based guideline. In order to help users get the key points more conveniently, also a practical R-styled plain-text guideline is provided. Finally, a short movie is available to help the user to set the own directory, properly.
2022
Algorithms; ceRNA network; miRNA sponge; Network theory; Female; Gene Regulatory Networks; Humans; RNA, Messenger; Software; Breast Neoplasms; MicroRNAs; Models, Theoretical; RNA, Long Noncoding
01 Pubblicazione su rivista::01a Articolo in rivista
SPINNAKER: an R-based tool to highlight key RNA interactions in complex biological networks / Paci, P.; Fiscon, G.. - In: BMC BIOINFORMATICS. - ISSN 1471-2105. - 23:1(2022), pp. 1-13. [10.1186/s12859-022-04695-x]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1634701
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