配对末端标签测序分析染色质相互作用(chromatin interaction analysis by paired-end tag sequencing,ChIA-PET)技术是一项在全基因组范围内分析远程染色质相互作用的新技术。它把染色质免疫沉淀(chromatin immunoprecipitation,ChIP)...配对末端标签测序分析染色质相互作用(chromatin interaction analysis by paired-end tag sequencing,ChIA-PET)技术是一项在全基因组范围内分析远程染色质相互作用的新技术。它把染色质免疫沉淀(chromatin immunoprecipitation,ChIP)技术、染色质邻近式连接(chromatin proximity ligation)技术、配对末端标签(paired-endtag,PET)技术和新一代测序(next-generation sequencing)技术融为一体,在基因组三维折叠和套环状态下分析基因表达和调控。ChIA-PET技术已用于确定人乳腺腺癌细胞内雌激素受体a的结合位点之间的相互作用。随着更多蛋白质因子的发现及其抗体的应用,该技术可实时捕获全基因组范围内参与复制、转录过程的蛋白质因子结合位点以及结合位点间的相互作用,这对于阐明基因调控和疾病发生机制具有重大意义。展开更多
Studies on the lung cancer genome are indispensable for developing a cure for lung cancer.Whole-genome resequencing,genome-wide association studies,and transcriptome sequencing have greatly improved our understanding ...Studies on the lung cancer genome are indispensable for developing a cure for lung cancer.Whole-genome resequencing,genome-wide association studies,and transcriptome sequencing have greatly improved our understanding of the cancer genome.However,dysregulation of longrange chromatin interactions in lung cancer remains poorly described.To better understand the three-dimensional(3D)genomic interaction features of the lung cancer genome,we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II(RNAPII),CCCTC-binding factor(CTCF),enhancer of zeste homolog 2(EZH2),and histone 3 lysine 27 trimethylation(H3K27me3)using long-read chromatin interaction analysis by paired-end tag sequencing(ChIA-PET).Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes,either through loops or domains,and their distributions along the genome were distinct from and complementary to those associated with RNAPII.Cancer-related genes were highly enriched with chromatin interactions,and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes,such as additional repressive interactions on FOXO4 and promoter–promoter interactions between NF1 and RNF135.Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes,suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes.These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.展开更多
Background:With the development of rapid and cheap sequencing techniques,the cost of whole-genome sequencing(WGS)has dropped significantly.However,the complexity of the human genome is not limited to the pure sequence...Background:With the development of rapid and cheap sequencing techniques,the cost of whole-genome sequencing(WGS)has dropped significantly.However,the complexity of the human genome is not limited to the pure sequenceand additional experiments are required to learn the human genome's influence on complex traits.One of the most exciting aspects for scientists nowadays is the spatial organisation of the genome,which can be discovered using spatial experiments(e.g.,Hi-C,ChIA-PET).The information about the spatial contacts helps in the analysis and brings new insights into our understanding of the disease developments.Methods:We have used an ensemble of deep learning with classical machine learning algorithms.The deep learning network we used was DNABERT,which utilises the BERT language model(based on transformers)for the genomic function.The classical machine learning models included support vector machines(SVMs),random forests(RFs),and K-nearest neighbor(KNN).The whole approach was wrapped together as deep hybrid learning(DHL).Results:We found that the DNABERT can be used to predict the ChIA-PET experiments with high precision.Additionally,the DHL approach has increased the metrics on CTCF and RNAPII sets.Conclusions:DHL approach should be taken into consideration for the models utilising the power of deep learning.While straightforward in the concept,it can improve the results significantly.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.31970590).
文摘Studies on the lung cancer genome are indispensable for developing a cure for lung cancer.Whole-genome resequencing,genome-wide association studies,and transcriptome sequencing have greatly improved our understanding of the cancer genome.However,dysregulation of longrange chromatin interactions in lung cancer remains poorly described.To better understand the three-dimensional(3D)genomic interaction features of the lung cancer genome,we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II(RNAPII),CCCTC-binding factor(CTCF),enhancer of zeste homolog 2(EZH2),and histone 3 lysine 27 trimethylation(H3K27me3)using long-read chromatin interaction analysis by paired-end tag sequencing(ChIA-PET).Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes,either through loops or domains,and their distributions along the genome were distinct from and complementary to those associated with RNAPII.Cancer-related genes were highly enriched with chromatin interactions,and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes,such as additional repressive interactions on FOXO4 and promoter–promoter interactions between NF1 and RNF135.Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes,suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes.These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.
基金supported by National Science Centre,Poland(Nos.2019/35/O/ST6/02484 and 2020/37/B/NZ2/03757)Foundation for Polish Science,co-financed by the European Union under the European Regional Development Fund(TEAM to DP)The work has been co-supported by European Commission Horizon 2020 Marie Skodowska-Curie ITN Enhpathy grant“Molecular Basis of Human enhanceropathies”and National Institute of Health USA 4DNucleome grant 1U54DK107967-01“Nucleome Positioning System for Spatiotemporal Genome Organization and Regulation”:Research was co-funded by Warsaw University of Technology within the Excellence Initiative:Research University(IDUB)programme.Computations were performed thanks to the Laboratory of Bioinformatics and Computational Genomics,Faculty of Mathematics and Information Science,Warsaw University of Technology using the Artificial Intelligence HPC platform financed by Polish Ministry of Science and Higher Education(No.7054/IA/SP/2020 of 2020-08-28).
文摘Background:With the development of rapid and cheap sequencing techniques,the cost of whole-genome sequencing(WGS)has dropped significantly.However,the complexity of the human genome is not limited to the pure sequenceand additional experiments are required to learn the human genome's influence on complex traits.One of the most exciting aspects for scientists nowadays is the spatial organisation of the genome,which can be discovered using spatial experiments(e.g.,Hi-C,ChIA-PET).The information about the spatial contacts helps in the analysis and brings new insights into our understanding of the disease developments.Methods:We have used an ensemble of deep learning with classical machine learning algorithms.The deep learning network we used was DNABERT,which utilises the BERT language model(based on transformers)for the genomic function.The classical machine learning models included support vector machines(SVMs),random forests(RFs),and K-nearest neighbor(KNN).The whole approach was wrapped together as deep hybrid learning(DHL).Results:We found that the DNABERT can be used to predict the ChIA-PET experiments with high precision.Additionally,the DHL approach has increased the metrics on CTCF and RNAPII sets.Conclusions:DHL approach should be taken into consideration for the models utilising the power of deep learning.While straightforward in the concept,it can improve the results significantly.
