2021年10月23日
Identification of Enhancers and Promoters in the Genome by Multidimensional Scaling
Genes
- ,
- 巻
- 12
- 号
- 11
- 開始ページ
- 1671
- 終了ページ
- 1671
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.3390/genes12111671
- 出版者・発行元
- MDPI AG
The positions of enhancers and promoters on genomic DNA remain poorly understood. Chromosomes cannot be observed during the cell division cycle because the genome forms a chromatin structure and spreads within the nucleus. However, high-throughput chromosome conformation capture (Hi-C) measures the physical interactions of genomes. In previous studies, DNA extrusion loops were directly derived from Hi-C heat maps. Multidimensional Scaling (MDS) is used in this assessment to more precisely locate enhancers and promoters. MDS is a multivariate analysis method that reproduces the original coordinates from the distance matrix between elements. We used Hi-C data of cultured osteosarcoma cells and applied MDS as the distance matrix of the genome. In addition, we selected columns 2 and 3 of the orthogonal matrix U as the desired structure. Overall, the DNA loops from the reconstructed genome structure contained bioprocesses involved in transcription, such as the pre-transcriptional initiation complex and RNA polymerase II initiation complex, and transcription factors involved in cancer, such as Foxm1 and CREB3. Therefore, our results are consistent with the biological findings. Our method is suitable for identifying enhancers and promoters in the genome.
- リンク情報
-
- DOI
- https://doi.org/10.3390/genes12111671
- 共同研究・競争的資金等の研究課題
- PCA及びTDを用いた教師無し学習による変数選択法によるscRNA-seq解析
- 共同研究・競争的資金等の研究課題
- テンソル分解を用いた教師無し学習による変数選択法を用いたトランスオミクス解析
- 共同研究・競争的資金等の研究課題
- テンソル分解を用いた教師無し学習による変数選択法のヒストン修飾解析への応用
- URL
- https://www.mdpi.com/2073-4425/12/11/1671/pdf
- ID情報
-
- DOI : 10.3390/genes12111671
- eISSN : 2073-4425
- ORCIDのPut Code : 101984024