Identification of Disease-miRNA Networks Across Different Cancer Types Using SWIM

TY - CHAP

T1 - Identification of Disease-miRNA Networks Across Different Cancer Types Using SWIM

AU - Fiscon, Giulia

AU - Conte, Federica

AU - Farina, Lorenzo

AU - Pellegrini, Marco

AU - Russo, Francesco

AU - Paci, Paola

PY - 2019

Y1 - 2019

N2 - MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) involved in several biological processes and diseases. MiRNAs regulate gene expression at the posttranscriptional level, mostly downregulating their targets by binding specific regions of transcripts through imperfect sequence complementarity. Prediction of miRNA-binding sites is challenging, and target prediction algorithms are usually based on sequence complementarity. In the last years, it has been shown that by adding miRNA and protein coding gene expression, we are able to build tissue-, cell line-, or disease-specific networks improving our understanding of complex biological scenarios. In this chapter, we present an application of a recently published software named SWIM, that allows to identify key genes in a network of interactions by defining appropriate "roles" of genes according to their local/global positioning in the overall network. Furthermore, we show how the SWIM software can be used to build miRNA-disease networks, by applying the approach to tumor data obtained from The Cancer Genome Atlas (TCGA).

AB - MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) involved in several biological processes and diseases. MiRNAs regulate gene expression at the posttranscriptional level, mostly downregulating their targets by binding specific regions of transcripts through imperfect sequence complementarity. Prediction of miRNA-binding sites is challenging, and target prediction algorithms are usually based on sequence complementarity. In the last years, it has been shown that by adding miRNA and protein coding gene expression, we are able to build tissue-, cell line-, or disease-specific networks improving our understanding of complex biological scenarios. In this chapter, we present an application of a recently published software named SWIM, that allows to identify key genes in a network of interactions by defining appropriate "roles" of genes according to their local/global positioning in the overall network. Furthermore, we show how the SWIM software can be used to build miRNA-disease networks, by applying the approach to tumor data obtained from The Cancer Genome Atlas (TCGA).

U2 - 10.1007/978-1-4939-9207-2_10

DO - 10.1007/978-1-4939-9207-2_10

M3 - Book chapter

C2 - 30963493

SN - 978-1-4939-9206-5

T3 - Methods in Molecular Biology

SP - 169

EP - 181

BT - MicroRNA Target Identification

A2 - Laganà, Alessandro

PB - Humana Press

ER -