Abstract
Most of the currently available co-expression network analysis method only can capture linear correlation among genes; however, ignore the non-linear dependent correlations. Accurately and easily getting the distance values among genes are of significant importance in clustering genes which are shared in the same biological functions. We developed an online tool, lncRNA explorer (LCLE), which is able to systematically analyse gene expression data to identify more comprehensive relationships among lncRNAs and proteincoding genes (PCGs) from five different distances metrics. Our simulation results demonstrated that the selection of an appropriate distance method could help to identify novel important genes from networks. LCLE allows users to visualise figures, and download tables analysed from publically available RNAseq data such as The Cancer Genome Atlas (TCGA) and genotype-tissue expression (GTEx) or upload their own data for analysis. Overall, our web portal will benefit for biologists or clinicians without programming background in identifying novel co-regulation relations for lncRNAs and PCGs.
| Original language | American English |
|---|---|
| Pages (from-to) | 520-528 |
| Journal | International Journal of Computational Biology and Drug Design |
| Volume | 13 |
| Issue number | 5-6 |
| DOIs | |
| State | Published - Mar 21 2021 |
