RAIN: RNA-protein Association and Interaction Networks

TY - JOUR

T1 - RAIN

T2 - RNA-protein Association and Interaction Networks

AU - Junge, Alexander

AU - Refsgaard, Jan C

AU - Garde, Christian

AU - Pan, Xiaoyong

AU - Santos, Alberto

AU - Alkan, Ferhat

AU - Anthon, Christian

AU - von Mering, Christian

AU - Workman, Christopher T

AU - Jensen, Lars Juhl

AU - Gorodkin, Jan

N1 - © The Author(s) 2017. Published by Oxford University Press.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) aswell. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literaturemining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessibleweb interface and all interaction data can be downloaded.

AB - Protein association networks can be inferred from a range of resources including experimental data, literature mining and computational predictions. These types of evidence are emerging for non-coding RNAs (ncRNAs) aswell. However, integration of ncRNAs into protein association networks is challenging due to data heterogeneity. Here, we present a database of ncRNA-RNA and ncRNA-protein interactions and its integration with the STRING database of protein-protein interactions. These ncRNA associations cover four organisms and have been established from curated examples, experimental data, interaction predictions and automatic literaturemining. RAIN uses an integrative scoring scheme to assign a confidence score to each interaction. We demonstrate that RAIN outperforms the underlying microRNA-target predictions in inferring ncRNA interactions. RAIN can be operated through an easily accessibleweb interface and all interaction data can be downloaded.

U2 - 10.1093/database/baw167

DO - 10.1093/database/baw167

M3 - Journal article

C2 - 28077569

SN - 1758-0463

VL - 2017

JO - Database: The Journal of Biological Databases and Curation

JF - Database: The Journal of Biological Databases and Curation

M1 - baw167

ER -