Computational Drug Discovery and Design

11–50% of reproducible results [29–31]. We also found that the

findings from recent literature reviews on miRNAs were discrepant

(Table1). Therefore, this meta-analysis method would fill this gap

to identify consistently dysregulated miRNAs in T2D.

2 Materials

1. Statistical software R (https://www.r-project.org) and RStudio
   (https://www.rstudio.com).


2. Installation ofmetaforpackage in R environment.


3. An example dataset with dysregulated miRNAs (Additional file
   data.csv).

2.1 Instructions R could be downloaded and installed fromhttps://www.r-project.
organd RStudio could be downloaded fromhttps://www.rstudio.
com. In RStudio we can installmetatorpackage [37](seeNote 1).

Table 1
Inconsistencies among literature reviews on miRNA dysregulation in T2D

miRNA

Literature review

Guay

(2011)

[32]

Guay

(2012)

[33]

Hamar

(2012)

[34]

Karolina

(2012) [11]

McClelland

(2014) [35]

Natarajan

(2012) [36]

Shantikumar

(2012) [14]

miR-103

(adipose)

N– D – U – N

miR-107

(adipose)

D– – – U – –

miR-132

(adipose)

––U– – – D

miR-143

(adipose)

N– – D – – N

miR-144

(liver)

D– – U – – –

miR-192

(kidney)

––UN N N –

miR-21

(kidney)

––– D U N –

miR-29c

(liver)

N– – – – – –

miR-375

(islets)

DU– U U – U

476 Hongmei Zhu and Siu-wai Leung