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Journal articles on the topic 'Chia-PET'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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1

Liu, Tong, and Zheng Wang. "DeepChIA-PET: Accurately predicting ChIA-PET from Hi-C and ChIP-seq with deep dilated networks." PLOS Computational Biology 19, no. 7 (2023): e1011307. http://dx.doi.org/10.1371/journal.pcbi.1011307.

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Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) can capture genome-wide chromatin interactions mediated by a specific DNA-associated protein. The ChIA-PET experiments have been applied to explore the key roles of different protein factors in chromatin folding and transcription regulation. However, compared with widely available Hi-C and ChIP-seq data, there are not many ChIA-PET datasets available in the literature. A computational method for accurately predicting ChIA-PET interactions from Hi-C and ChIP-seq data is needed that can save the efforts of performing wet-lab
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2

Li, Sun, Chang, Cai, Hong, and Zhou. "Chromatin Interaction Analysis with Updated ChIA-PET Tool (V3)." Genes 10, no. 7 (2019): 554. http://dx.doi.org/10.3390/genes10070554.

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Understanding chromatin interactions is important because they create chromosome conformation and link the cis- and trans- regulatory elements to their target genes for transcriptional regulation. Chromatin Interaction Analysis with Paired-End Tag (ChIA-PET) sequencing is a genome-wide high-throughput technology that detects chromatin interactions associated with a specific protein of interest. We developed ChIA-PET Tool for ChIA-PET data analysis in 2010. Here, we present the updated version of ChIA-PET Tool (V3) as a computational package to process the next-generation sequence data generate
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Lee, Byoungkoo, Jiahui Wang, Liuyang Cai, et al. "ChIA-PIPE: A fully automated pipeline for comprehensive ChIA-PET data analysis and visualization." Science Advances 6, no. 28 (2020): eaay2078. http://dx.doi.org/10.1126/sciadv.aay2078.

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ChIA-PET (chromatin interaction analysis with paired-end tags) enables genome-wide discovery of chromatin interactions involving specific protein factors, with base pair resolution. Interpretation of ChIA-PET data requires a robust analytic pipeline. Here, we introduce ChIA-PIPE, a fully automated pipeline for ChIA-PET data processing, quality assessment, visualization, and analysis. ChIA-PIPE performs linker filtering, read mapping, peak calling, and loop calling and automates quality control assessment for each dataset. To enable visualization, ChIA-PIPE generates input files for two-dimensi
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4

Hershey, David. "Don't Just Pet Your Chia." Science Activities: Classroom Projects and Curriculum Ideas 32, no. 2 (1995): 8–12. http://dx.doi.org/10.1080/00368121.1995.10113179.

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5

Vardaxis, Ioannis, Finn Drabløs, Morten B. Rye, and Bo Henry Lindqvist. "MACPET: model-based analysis for ChIA-PET." Biostatistics 21, no. 3 (2019): 625–39. http://dx.doi.org/10.1093/biostatistics/kxy084.

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Summary We present model-based analysis for ChIA-PET (MACPET), which analyzes paired-end read sequences provided by ChIA-PET for finding binding sites of a protein of interest. MACPET uses information from both tags of each PET and searches for binding sites in a two-dimensional space, while taking into account different noise levels in different genomic regions. MACPET shows favorable results compared with MACS in terms of motif occurrence and spatial resolution. Furthermore, significant binding sites discovered by MACPET are involved in a higher number of significant three-dimensional intera
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6

Small, Ernest. "34. Chia – not just a pet." Biodiversity 12, no. 1 (2011): 49–56. http://dx.doi.org/10.1080/14888386.2011.575104.

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7

Li, Guipeng, Yang Chen, Michael P. Snyder, and Michael Q. Zhang. "ChIA-PET2: a versatile and flexible pipeline for ChIA-PET data analysis." Nucleic Acids Research 45, no. 1 (2016): e4-e4. http://dx.doi.org/10.1093/nar/gkw809.

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Zhang, Jingyao, Huay Mei Poh, Su Qin Peh, et al. "ChIA-PET analysis of transcriptional chromatin interactions." Methods 58, no. 3 (2012): 289–99. http://dx.doi.org/10.1016/j.ymeth.2012.08.009.

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9

He, Chao, Guipeng Li, Diekidel M. Nadhir, Yang Chen, Xiaowo Wang, and Michael Q. Zhang. "Advances in computational ChIA-PET data analysis." Quantitative Biology 4, no. 3 (2016): 217–25. http://dx.doi.org/10.1007/s40484-016-0080-3.

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10

Phanstiel, Douglas H., Alan P. Boyle, Nastaran Heidari, and Michael P. Snyder. "Mango: a bias-correcting ChIA-PET analysis pipeline." Bioinformatics 31, no. 19 (2015): 3092–98. http://dx.doi.org/10.1093/bioinformatics/btv336.

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11

Lou, Shuyuan, and Shili Lin. "An in silico procedure for generating protein-mediated chromatin interaction data and comparison of significant interaction calling methods." PLOS ONE 19, no. 1 (2024): e0287521. http://dx.doi.org/10.1371/journal.pone.0287521.

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The ability to simulate high-throughput data with high fidelity to real experimental data is fundamental for benchmarking methods used to detect true long-range chromatin interactions mediated by a specific protein. Yet, such tools are not currently available. To fill this gap, we develop an in silico experimental procedure, ChIA-Sim, which imitates the experimental procedures that produce real ChIA-PET, Hi-ChIP, or PLAC-seq data. We show the fidelity of ChIA-Sim to real data by using guiding characteristics of several real datasets to generate data using the simulation procedure. We also used
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12

Huang, Weichun, Mario Medvedovic, Jingwen Zhang, and Liang Niu. "ChIAPoP: a new tool for ChIA-PET data analysis." Nucleic Acids Research 47, no. 7 (2019): e37-e37. http://dx.doi.org/10.1093/nar/gkz062.

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13

Capurso, Dan, Zhonghui Tang, and Yijun Ruan. "Methods for comparative ChIA-PET and Hi-C data analysis." Methods 170 (January 2020): 69–74. http://dx.doi.org/10.1016/j.ymeth.2019.09.019.

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14

Chandratre, Khyati. "Abstract A054: Accurate prediction of cohesin-mediated 3D genome organization using 2D chromatin features." Cancer Research 83, no. 11_Supplement (2023): A054. http://dx.doi.org/10.1158/1538-7445.prca2023-a054.

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Abstract The three-dimensional (3D) genome organization influences diverse nuclear processes. Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and High-throughput chromatin conformation capture (Hi-C) are powerful methods used to study the 3D genome organization. However, these two experiments are costly, time-consuming, require tens to hundreds of millions of cells, and challenging to optimize and analyze. Predicting ChIA-PET/Hi-C data using cheaper ChIP-Seq data and other easily obtainable features is a useful alternative. Also, it is well-established that the cohesin p
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15

Zhang, Henry B., Minji Kim, Jeffrey H. Chuang, and Yijun Ruan. "pyBedGraph: a python package for fast operations on 1D genomic signal tracks." Bioinformatics 36, no. 10 (2020): 3234–35. http://dx.doi.org/10.1093/bioinformatics/btaa061.

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Abstract Motivation Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq and ChIA-PET that generate coverage files for transcription factor binding, as well as DHS and ATAC-seq that yield coverage files for chromatin accessibility. Such files are in a bedGraph text format or a bigWig binary format. Obtaining summary statistics in a given region is a fundamental task in analyzing protein binding intensity or chromatin accessibility. However, the existing Python package for operating on coverage files is not optimized for speed. Results We developed pyBedG
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Orlov, Y. L., O. Thierry, A. G. Bogomolov, et al. "Computer methods of analysis of chromosome contacts in the cell nucleus based on sequencing technology data." Biomeditsinskaya Khimiya 63, no. 5 (2017): 418–22. http://dx.doi.org/10.18097/pbmc20176305418.

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The study spatial chromosome structure and chromosome folding in the interphase cell nucleus is an important challenge of world science. Detection of eukaryotic genome regions that physically interact with each other could be done by modern sequencing technologies. A basic method of chromosome folding by total sequencing of contacting DNA fragments is HI-C. Long-range chromosomal interactions play an important role in gene transcription and regulation. The study of chromosome interactions, 3D (three-dimensional) genome structure and its effect on gene transcription allows revealing fundamental
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Li, Guoliang, Melissa J. Fullwood, Han Xu, et al. "ChIA-PET tool for comprehensive chromatin interaction analysis with paired-end tag sequencing." Genome Biology 11, no. 2 (2010): R22. http://dx.doi.org/10.1186/gb-2010-11-2-r22.

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18

Buisine, Nicolas, Xiaoan Ruan, Yijun Ruan, and Laurent M. Sachs. "Chromatin Interaction Analysis Using Paired-End-Tag (ChIA-PET) Sequencing in Tadpole Tissues." Cold Spring Harbor Protocols 2018, no. 8 (2018): pdb.prot104620. http://dx.doi.org/10.1101/pdb.prot104620.

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19

Fan, Xiucheng. "The Role of Transcription Factor Tfii-I/GTF2I in the Response to Cellular Stress in Hematopoietic Cells." Blood 124, no. 21 (2014): 2915. http://dx.doi.org/10.1182/blood.v124.21.2915.2915.

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The transcription factor ATF3 and the heme regulated eIF2α kinase (HRI) have previously been shown to be involved in the stress response in hematopoietic cells. Many stress-responsive genes are regulated at the level of transcription elongation and are characterized by paused RNA polymerase II (Pol II) at the promoter region. Indeed, analyzing data from the public ENCODE project revealed that the HRI and the ATF3 genes contained paused Pol II at the promoter and at upstream intergenic regulatory elements. In this study we performed a genomic and proteomic analysis of transcription factor TFII-
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20

Krismer, Konstantin, Yuchun Guo, and David K. Gifford. "IDR2D identifies reproducible genomic interactions." Nucleic Acids Research 48, no. 6 (2020): e31-e31. http://dx.doi.org/10.1093/nar/gkaa030.

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Abstract Chromatin interaction data from protocols such as ChIA-PET, HiChIP and Hi-C provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce an extension of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an o
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21

Li, Xingwang, Oscar Junhong Luo, Ping Wang, et al. "Long-read ChIA-PET for base-pair-resolution mapping of haplotype-specific chromatin interactions." Nature Protocols 12, no. 5 (2017): 899–915. http://dx.doi.org/10.1038/nprot.2017.012.

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22

Paulsen, Jonas, Einar A. Rødland, Lars Holden, Marit Holden, and Eivind Hovig. "A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions." Nucleic Acids Research 42, no. 18 (2014): e143-e143. http://dx.doi.org/10.1093/nar/gku738.

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23

Lun, Aaron T. L., Malcolm Perry, and Elizabeth Ing-Simmons. "Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments." F1000Research 5 (May 20, 2016): 950. http://dx.doi.org/10.12688/f1000research.8759.1.

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The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along wit
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Lun, Aaron T. L., Malcolm Perry, and Elizabeth Ing-Simmons. "Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments." F1000Research 5 (June 28, 2016): 950. http://dx.doi.org/10.12688/f1000research.8759.2.

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The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along wit
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Buisine, Nicolas, Xiaoan Ruan, Yijun Ruan, and Laurent M. Sachs. "Chromatin Immunoprecipitation for Chromatin Interaction Analysis Using Paired-End-Tag (ChIA-PET) Sequencing in Tadpole Tissues." Cold Spring Harbor Protocols 2018, no. 8 (2018): pdb.prot097725. http://dx.doi.org/10.1101/pdb.prot097725.

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26

Kulakova, Ekaterina, Anastasia Spitsina, Anton Bogomolov, et al. "Program for analysis of genome distribution of chromosome contacts in cell nucleus by the data obtained using ChIA-PET and Hi-C technologies." Program Systems: Theory and Applications 8, no. 1 (2017): 219–42. http://dx.doi.org/10.25209/2079-3316-2017-8-1-219-242.

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27

Buisine, Nicolas, Xiaoan Ruan, Yijun Ruan, and Laurent M. Sachs. "Corrigendum: Chromatin Immunoprecipitation for Chromatin Interaction Analysis Using Paired-End-Tag (ChIA-PET) Sequencing in Tadpole Tissues." Cold Spring Harbor Protocols 2020, no. 1 (2020): pdb.corr106765. http://dx.doi.org/10.1101/pdb.corr106765.

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Kulakova, Ye, A. Spitsina, N. Orlova, et al. "Supercomputer analysis of genomics and transcriptomics data revealed by high-throughput DNA sequencing." Program Systems: Theory and Applications 6, no. 2 (2015): 129–48. http://dx.doi.org/10.25209/2079-3316-2015-6-2-129-148.

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Buisine, Nicolas, Xiaoan Ruan, Patrice Bilesimo, et al. "Xenopus tropicalis Genome Re-Scaffolding and Re-Annotation Reach the Resolution Required for In Vivo ChIA-PET Analysis." PLOS ONE 10, no. 9 (2015): e0137526. http://dx.doi.org/10.1371/journal.pone.0137526.

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30

Wang, Siguo, Qinhu Zhang, Ying He, et al. "DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes." PLOS Computational Biology 18, no. 10 (2022): e1010572. http://dx.doi.org/10.1371/journal.pcbi.1010572.

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In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide contact interactions is crucial for dissecting three-dimensional(3D) genome structure and function. Here, we present a deep learning method to predict genome-wide chromatin loops, called DLoopCaller, by combining accessible chromatin landscapes and raw Hi-C contact maps. Some available orthogonal data ChIA-PET/HiChIP and Capture Hi-C were used to genera
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Mills, Caitlin, Anushya Muruganujan, Dustin Ebert, et al. "PEREGRINE: A genome-wide prediction of enhancer to gene relationships supported by experimental evidence." PLOS ONE 15, no. 12 (2020): e0243791. http://dx.doi.org/10.1371/journal.pone.0243791.

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Enhancers are powerful and versatile agents of cell-type specific gene regulation, which are thought to play key roles in human disease. Enhancers are short DNA elements that function primarily as clusters of transcription factor binding sites that are spatially coordinated to regulate expression of one or more specific target genes. These regulatory connections between enhancers and target genes can therefore be characterized as enhancer-gene links that can affect development, disease, and homeostatic cellular processes. Despite their implication in disease and the establishment of cell ident
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Gitto, Sarah B., Austin R. Pantel, Mehran Makvandi, et al. "Abstract 5610: [18F]FluorThanatrace ([18F]FTT) PET Imaging of PARP-inhibitor drug-target engagement as a biomarker of response in ovarian cancer." Cancer Research 83, no. 7_Supplement (2023): 5610. http://dx.doi.org/10.1158/1538-7445.am2023-5610.

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Abstract Purpose: Poly(ADP-ribose) polymerase enzyme inhibitors (PARPi) have become the standard-of-care treatment for homologous recombination deficient (HRD) high-grade serous ovarian cancer (HGSOC). However, not all HRD tumors respond to PARPi and biomarkers to predict response are needed. [18F]FluorThanatrace (FTT) is a PARPi-analog PET radiotracer that non-invasively measures PARP-1 expression. Herein, we evaluate the ability of FTT uptake to serve as a biomarker to predict response to PARPi in patient-derived xenograft (PDX) models and patients with HGSOC. Patients and Methods: In PDX mo
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Yousif, Faris H., Bakhtiar Q. Aziz, and Ezaddin N. Baban. "Subsurface Imaging of the Fatha Formation Utilizing 3D Seismic Data in Chia Surkh Area, Kurdistan Region, Iraq." Iraqi Geological Journal 55, no. 2B (2022): 35–46. http://dx.doi.org/10.46717/igj.55.2b.4ms-2022-08-20.

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The current study aims to detect subsurface geologic features using 3D dense sampling seismic data in the Fatha Formation, in the Chia Surkh area, Kurdistan Region,Iraq. A3D cube seismic data covering 75 Km2 were used to image the Fatha Formation subsurface geologic structures. The seismic data and appropriate information were gathered from Pet Oil Company with the permission of the Ministry of Natural Resources of the Kurdistan Region, Iraq. A cube of seismic data was used to image the three units of the Fatha Formation. In this study, forty seismic sections with the direction of NE-SW and 30
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Scala, Giovanni, Francesca Gorini, Susanna Ambrosio, et al. "8-oxodG accumulation within super-enhancers marks fragile CTCF-mediated chromatin loops." Nucleic Acids Research 50, no. 6 (2022): 3292–306. http://dx.doi.org/10.1093/nar/gkac143.

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Abstract 8-Oxo-7,8-dihydro-2′-deoxyguanosine (8-oxodG), a major product of the DNA oxidization process, has been proposed to have an epigenetic function in gene regulation and has been associated with genome instability. NGS-based methodologies are contributing to the characterization of the 8-oxodG function in the genome. However, the 8-oxodG epigenetic role at a genomic level and the mechanisms controlling the genomic 8-oxodG accumulation/maintenance have not yet been fully characterized. In this study, we report the identification and characterization of a set of enhancer regions accumulati
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Sati, Satish, Parker Jones, Hali S. Kim, Linda A. Zhou, Emmanuel Rapp-Reyes, and Thomas H. Leung. "HiCuT: An efficient and low input method to identify protein-directed chromatin interactions." PLOS Genetics 18, no. 3 (2022): e1010121. http://dx.doi.org/10.1371/journal.pgen.1010121.

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3D genome organization regulates gene expression, and disruption of these long-range (>20kB) DNA-protein interactions results in pathogenic phenotypes. Chromosome conformation methods in conjunction with chromatin immunoprecipitation were used to decipher protein-directed chromatin interactions. However, these methods required abundant starting material (>500,000 cells), sizable number of sequencing reads (>100 million reads), and elaborate data processing methods to reduce background noise, which limited their use in primary cells. Hi-C Coupled chromatin cleavage and Tagmentation (Hi
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White, Shannon M., Michael P. Snyder, and Chunling Yi. "Master lineage transcription factors anchor trans mega transcriptional complexes at highly accessible enhancer sites to promote long-range chromatin clustering and transcription of distal target genes." Nucleic Acids Research 49, no. 21 (2021): 12196–210. http://dx.doi.org/10.1093/nar/gkab1105.

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Abstract The term ‘super enhancers’ (SE) has been widely used to describe stretches of closely localized enhancers that are occupied collectively by large numbers of transcription factors (TFs) and co-factors, and control the transcription of highly-expressed genes. Through integrated analysis of >600 DNase-seq, ChIP-seq, GRO-seq, STARR-seq, RNA-seq, Hi-C and ChIA-PET data in five human cancer cell lines, we identified a new class of autonomous SEs (aSEs) that are excluded from classic SE calls by the widely used Rank Ordering of Super-Enhancers (ROSE) method. TF footprint analysis reve
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Wlasnowolski, Michal, Michal Sadowski, Tymon Czarnota, et al. "3D-GNOME 2.0: a three-dimensional genome modeling engine for predicting structural variation-driven alterations of chromatin spatial structure in the human genome." Nucleic Acids Research 48, W1 (2020): W170—W176. http://dx.doi.org/10.1093/nar/gkaa388.

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Abstract Structural variants (SVs) that alter DNA sequence emerge as a driving force involved in the reorganisation of DNA spatial folding, thus affecting gene transcription. In this work, we describe an improved version of our integrated web service for structural modeling of three-dimensional genome (3D-GNOME), which now incorporates all types of SVs to model changes to the reference 3D conformation of chromatin. In 3D-GNOME 2.0, the default reference 3D genome structure is generated using ChIA-PET data from the GM12878 cell line and SVs data are sourced from the population-scale catalogue o
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Chen, Dijun, Liang-Yu Fu, Zhao Zhang, et al. "Dissecting the chromatin interactome of microRNA genes." Nucleic Acids Research 42, no. 5 (2013): 3028–43. http://dx.doi.org/10.1093/nar/gkt1294.

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Abstract Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II–associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We obs
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Pande, Amit, Wojciech Makalowski, Jürgen Brosius, and Carsten A. Raabe. "Enhancer occlusion transcripts regulate the activity of human enhancer domains via transcriptional interference: a computational perspective." Nucleic Acids Research 48, no. 7 (2020): 3435–54. http://dx.doi.org/10.1093/nar/gkaa026.

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Abstract Analysis of ENCODE long RNA-Seq and ChIP-seq (Chromatin Immunoprecipitation Sequencing) datasets for HepG2 and HeLa cell lines uncovered 1647 and 1958 transcripts that interfere with transcription factor binding to human enhancer domains. TFBSs (Transcription Factor Binding Sites) intersected by these ‘Enhancer Occlusion Transcripts’ (EOTrs) displayed significantly lower relative transcription factor (TF) binding affinities compared to TFBSs for the same TF devoid of EOTrs. Expression of most EOTrs was regulated in a cell line specific manner; analysis for the same TFBSs across cell l
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Li, Peng, Suman Mitra, Rosanne Spolski, et al. "STAT5-mediated chromatin interactions in superenhancers activate IL-2 highly inducible genes: Functional dissection of the Il2ra gene locus." Proceedings of the National Academy of Sciences 114, no. 46 (2017): 12111–19. http://dx.doi.org/10.1073/pnas.1714019114.

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Cytokines critically control immune responses, but how regulatory programs are altered to allow T cells to differentially respond to distinct cytokine stimuli remains poorly understood. Here, we have globally analyzed enhancer elements bound by IL-2–activated STAT5 and IL-21–activated STAT3 in T cells and identified Il2ra as the top-ranked gene regulated by an IL-2–activated STAT5-bound superenhancer and one of the top genes regulated by STAT3-bound superenhancers. Moreover, we found that STAT5 binding was rapidly superenriched at genes highly induced by IL-2 and that IL-2–activated STAT5 bind
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Yu, Longtao, Hengxiang Shen, and Xiaowen Lyu. "Roles of Polycomb Complexes in the Reconstruction of 3D Genome Architecture during Preimplantation Embryonic Development." Genes 13, no. 12 (2022): 2382. http://dx.doi.org/10.3390/genes13122382.

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The appropriate deployment of developmental programs depends on complex genetic information encoded by genomic DNA sequences and their positioning and contacts in the three-dimensional (3D) space within the nucleus. Current studies using novel techniques including, but not limited to, Hi-C, ChIA-PET, and Hi-ChIP reveal that regulatory elements (Res), such as enhancers and promoters, may participate in the precise regulation of expression of tissue-specific genes important for both embryogenesis and organogenesis by recruiting Polycomb Group (PcG) complexes. PcG complexes usually poise the tran
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42

Võ, Tá Đương. "Khốn Thân Tôi, Nếu Tôi Không Loan Báo Tin Mừng". Khoa Học Công Giáo và Đời Sống 4, № 4 (2024): 1–7. https://doi.org/10.54855/csl.24441.

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Bài viết “Khốn Thân Tôi, Nếu Tôi Không Loan Báo Tin Mừng” nhấn mạnh sứ vụ loan báo Tin Mừng của các tu sĩ trong bối cảnh xã hội hiện đại. Tác giả, Ts. Pet. Võ Tá Đương, OP, kêu gọi các tu sĩ nhìn lại đời sống Thánh hiến để làm mới lại tinh thần và nhiệt huyết truyền giáo. Đặt trong bối cảnh xã hội đang đối mặt với nhiều khủng hoảng về đạo đức và tình thương, bài viết nhấn mạnh vai trò của tu sĩ trong việc sống và thực thi đức ái Phúc Âm bằng những hành động cụ thể. Sứ vụ này không chỉ giới hạn trong việc giảng thuyết mà còn thông qua đời sống chứng tá, chia sẻ lòng Chúa thương xót và xây dựng
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Hovenga, Van, and Oluwatosin Oluwadare. "CBCR: A Curriculum Based Strategy For Chromosome Reconstruction." International Journal of Molecular Sciences 22, no. 8 (2021): 4140. http://dx.doi.org/10.3390/ijms22084140.

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In this paper, we introduce a novel algorithm that aims to estimate chromosomes’ structure from their Hi-C contact data, called Curriculum Based Chromosome Reconstruction (CBCR). Specifically, our method performs this three dimensional reconstruction using cis-chromosomal interactions from Hi-C data. CBCR takes intra-chromosomal Hi-C interaction frequencies as an input and outputs a set of xyz coordinates that estimate the chromosome’s three dimensional structure in the form of a .pdb file. The algorithm relies on progressively training a distance-restraint-based algorithm with a strategy we r
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44

Pyfrom, Sarah, Olivia Koues, Rodney Kowalewski, Eugene M. Oltz, and Jacqueline Payton. "Correlative Recurrent Expression of Predicted Elements (CREPE): A Novel Computational Approach to Predict LncRNA Function." Journal of Immunology 200, no. 1_Supplement (2018): 167.10. http://dx.doi.org/10.4049/jimmunol.200.supp.167.10.

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Abstract Long non-coding RNAs (lncRNAs) act as transcriptional regulators, scaffolds, and signaling modulators in development, immune response, and oncogenesis. Our -omics study of >100 human Non-Hodgkin Lymphoma (NHL) and normal B cell samples revealed altered expression of lncRNAs in NHL. LncRNAs have not been characterized in NHL or B cells, and there are few guidelines for functional prediction. To address this gap, we developed a novel computational approach: Correlative Recurrent Expression of Predicted Elements (CREPE). This method calculates and tracks the following for each lnc
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45

Caudai, Claudia, Monica Zoppè, Anna Tonazzini, Ivan Merelli, and Emanuele Salerno. "Integration of Multiple Resolution Data in 3D Chromatin Reconstruction Using ChromStruct." Biology 10, no. 4 (2021): 338. http://dx.doi.org/10.3390/biology10040338.

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The three-dimensional structure of chromatin in the cellular nucleus carries important information that is connected to physiological and pathological correlates and dysfunctional cell behaviour. As direct observation is not feasible at present, on one side, several experimental techniques have been developed to provide information on the spatial organization of the DNA in the cell; on the other side, several computational methods have been developed to elaborate experimental data and infer 3D chromatin conformations. The most relevant experimental methods are Chromosome Conformation Capture a
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Shih, Han-Yu, Chunhong Liu, ping wang, et al. "A critical CTCF binding site of the Ifng-Il22 locus specifies cytokine expression and finetunes immune response." Journal of Immunology 206, no. 1_Supplement (2021): 53.13. http://dx.doi.org/10.4049/jimmunol.206.supp.53.13.

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Abstract Precise control of cytokine milieu plays an essential role in homeostasis and diseases, and dysregulated cytokine production leads to undesired inflammation and autoimmunity. Interferon gamma (IFN-γ) and interleukin-22 (IL-22), two key cytokines for against intracellular and extracellular pathogens, respectively, evolutionarily resides in close genomic proximity. Notably, these genes are exclusively expressed in type I and type III lymphoid cells via complex epigenomic regulation that remains largely unknown. Our ATAC-seq, ChIP-seq and PollI ChIA-PET datasets revealed a CTCF binding s
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Zhu, Hao, Tong Liu, and Zheng Wang. "C2c: Predicting Micro-C from Hi-C." Genes 15, no. 6 (2024): 673. http://dx.doi.org/10.3390/genes15060673.

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Motivation: High-resolution Hi-C data, capable of detecting chromatin features below the level of Topologically Associating Domains (TADs), significantly enhance our understanding of gene regulation. Micro-C, a variant of Hi-C incorporating a micrococcal nuclease (MNase) digestion step to examine interactions between nucleosome pairs, has been developed to overcome the resolution limitations of Hi-C. However, Micro-C experiments pose greater technical challenges compared to Hi-C, owing to the need for precise MNase digestion control and higher-resolution sequencing. Therefore, developing compu
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Pagin, Miriam, Mattias Pernebrink, Simone Giubbolini, et al. "Sox2 Controls Neural Stem Cell Self-Renewal Through a Fos-Centered Gene Regulatory Network." Stem Cells 39, no. 8 (2021): 1107–19. http://dx.doi.org/10.1002/stem.3373.

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Abstract The Sox2 transcription factor is necessary for the long-term self-renewal of neural stem cells (NSCs). Its mechanism of action is still poorly defined. To identify molecules regulated by Sox2, and acting in mouse NSC maintenance, we transduced, into Sox2-deleted NSC, genes whose expression is strongly downregulated following Sox2 loss (Fos, Jun, Egr2), individually or in combination. Fos alone rescued long-term proliferation, as shown by in vitro cell growth and clonal analysis. Furthermore, pharmacological inhibition by T-5224 of FOS/JUN AP1 complex binding to its targets decreased c
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Arega, Yibeltal, Hao Jiang, Shuangqi Wang, Jingwen Zhang, Xiaohui Niu, and Guoliang Li. "ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments." Frontiers in Genetics 11 (December 14, 2020). http://dx.doi.org/10.3389/fgene.2020.616160.

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Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the
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"ChIA-PET Elution Buffer." Cold Spring Harbor Protocols 2018, no. 8 (2018): pdb.rec104851. http://dx.doi.org/10.1101/pdb.rec104851.

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