Name
Title | ||
|---|---|---|
TRANSDIRE: data-driven direct reprogramming by a pioneer factor-guided trans-omics approach |
Abstract | ||
|---|---|---|
Motivation: Direct reprogramming involves the direct conversion of fully differentiated mature cell types into various other cell types while bypassing an intermediate pluripotent state (e.g. induced pluripotent stem cells). Cell differentiation by direct reprogramming is determined by two types of transcription factors (TFs): pioneer factors (PFs) and cooperative TFs. PFs have the distinct ability to open chromatin aggregations, assemble a collective of cooperative TFs and activate gene expression. The experimental determination of two types of TFs is extremely difficult and costly. Results: In this study, we developed a novel computational method, TRANSDIRE (TRANS-omics-based approach for Direct REprogramming), to predict the TFs that induce direct reprogramming in various human cell types using multiple omics data. In the algorithm, potential PFs were predicted based on low signal chromatin regions, and the cooperative TFs were predicted through a trans-omics analysis of genomic data (e.g. enhancers), transcriptome data (e.g. gene expression profiles in human cells), epigenome data (e.g. chromatin immunoprecipitation sequencing data) and interactome data. We applied the proposed methods to the reconstruction of TFs that induce direct reprogramming from fibroblasts to six other cell types: hepatocytes, cartilaginous cells, neurons, cardiomyocytes, pancreatic cells and Paneth cells. We demonstrated that the methods successfully predicted TFs for most cell conversions with high accuracy. Thus, the proposed methods are expected to be useful for various practical applications in regenerative medicine. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/bioinformatics/btac209 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 38 | 10 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryohei Eguchi | 1 | 0 | 0.34 |
| Momoko Hamano | 2 | 0 | 0.68 |
| Michio Iwata | 3 | 0 | 0.34 |
| Toru Nakamura | 4 | 0 | 0.34 |
| Shinya Oki | 5 | 0 | 0.34 |
| Yoshihiro Yamanishi | 6 | 1268 | 83.44 |