Deciphering the complexity of human non-coding promoter-proximal transcriptome
Deciphering the complexity of human non-coding promoter-proximal transcriptome
复制标题
破译人类非编码启动子近端转录组的复杂性
DOI:
10.1093/bioinformatics/bty981
复制
发表时间:
2019
期刊:
影响因子:
5.8
通讯作者:
Mapelli S
中科院分区:
文献类型:
--
作者:
Mapelli S
MotivationLong non-coding RNAs (lncRNAs) have gained increasing relevance in epigenetic regulation and nuclear functional organization. High-throughput sequencing approaches have revealed frequent non-coding transcription in promoter-proximal regions. However, a comprehensive catalogue of promoter-associated RNAs (paRNAs) and an analysis of the possible interactions with neighboring genes and genomic regulatory elements are missing.ResultsIntegrating data from multiple cell types and experimental platforms we identified thousands of paRNAs in the human genome. paRNAs are transcribed in both sense and antisense orientation, are mostly non-polyadenylated and retained in the cell nucleus. Transcriptional regulators, epigenetic effectors and activating chromatin marks are enriched in paRNA-positive promoters. Furthermore, paRNA-positive promoters exhibit chromatin signatures of both active promoters and enhancers. Promoters with paRNAs reside preferentially at chromatin loop boundaries, suggesting an involvement in anchor site recognition and chromatin looping. Importantly, these features are independent of the transcriptional state of neighboring genes. Thus, paRNAs may act ascis-regulatory modules with an impact on local recruitment of transcription factors, epigenetic state and chromatin loop organization. This study provides a comprehensive analysis of the promoter-proximal transcriptome and offers novel insights into the roles of paRNAs in epigenetic processes and human diseases.Availability and implementationGenomic coordinates of predicted paRNAs are available at https://figshare.com: https://doi.org/10.6084/m9.figshare.7392791.v1 and https://doi.org/10.6084/m9.figshare.4856630.v2.Supplementary informationSupplementary data are available atBioinformaticsonline.
登录
查看更多内容
影响因子:
16
作者:
Heinz S;Benner C;Spann N;Bertolino E;Lin YC;Laslo P;Cheng JX;Murre C;Singh H;Glass CK
通讯作者:
Glass CK
影响因子:
16.6
作者:
Pisignano G;Napoli S;Magistri M;Mapelli SN;Pastori C;Di Marco S;Civenni G;Albino D;Enriquez C;Allegrini S;Mitra A;D'Ambrosio G;Mello-Grand M;Chiorino G;Garcia-Escudero R;Varani G;Carbone GM;Catapano CV
通讯作者:
Catapano CV
影响因子:
64.5
作者:
Rao SS;Huntley MH;Durand NC;Stamenova EK;Bochkov ID;Robinson JT;Sanborn AL;Machol I;Omer AD;Lander ES;Aiden EL
通讯作者:
Aiden EL
影响因子:
4.4
作者:
Lepoivre C;Belhocine M;Bergon A;Griffon A;Yammine M;Vanhille L;Zacarias-Cabeza J;Garibal MA;Koch F;Maqbool MA;Fenouil R;Loriod B;Holota H;Gut M;Gut I;Imbert J;Andrau JC;Puthier D;Spicuglia S
通讯作者:
Spicuglia S