A data-driven biocomputing pipeline with meta-analysis on high throughput transcriptomics to identify genome-wide miRNA markers associated with type 2 diabetes.

ABSTRACT:

Background

MicroRNAs (miRNAs) are sought-after biomarkers of complex, polygenic diseases such as type 2 diabetes (T2D). Data-driven biocomputing provides robust and novel avenues for synthesizing evidence from individual miRNA seq studies.

Objective

To identify miRNA markers associated with T2D, via a data-driven, biocomputing approach on high throughput transcriptomics.

Materials and methods

The pipeline consisted of five sequential steps using miRNA seq data retrieved from the National Center for Biotechnology Information Gene Expression Omnibus platform: systematic review; identification of differentially expressed miRNAs (DE-miRNAs); meta-analysis of DE-miRNAs; network analysis; and downstream analyses. Three normalization algorithms (trimmed mean of M-values; upp

SUBMITTER: De Silva K 

PROVIDER: S-EPMC8829580 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature