Searching journal content for articles similar to Ghanbari and Ohler 30 (2): 214.

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  1. Method: Systematic identification of interchromosomal interaction networks supports the existence of specialized RNA factories
    • Borislav Hrisimirov Hristov,
    • William Stafford Noble,
    • and Alessandro Bertero
    Genome Res. October 2024 34: 1610-1623; Published in Advance September 25, 2024, doi:10.1101/gr.278327.123
    ..., we used the most deeply sequenced Hi-C data set reported to date: an ultra-deep Hi-C map of human GM12878 lymphoblastoid cells (Harris et al. 2023). The ENCODE Project produced chromatin immunoprecipitation sequencing (ChIP-seq) data for 110 DBPs in this cell line (The ENCODE Project Consortium 2012...
  2. Method: Diffusion-enhanced characterization of 3D chromatin structure reveals its linkage to gene regulatory networks and the interactome
    • Yueying He,
    • Yue Xue,
    • Jingyao Wang,
    • Yupeng Huang,
    • Lu Liu,
    • Yanyi Huang,
    • and Yi Qin Gao
    Genome Res. August 2023 33: 1354-1368; Published in Advance July 25, 2023, doi:10.1101/gr.277737.123
    .... On the other hand, the distinct epigenetic hallmarks affect the accessibility and TF and RNA binding preference to DNA of specific genomic regions and introduce distinct GGIs over a similar 1D DNA sequence for different tissues. These interactions are all likely to participate in the establishment of tissue...
  3. Method: Domain-adaptive neural networks improve cross-species prediction of transcription factor binding
    • Kelly Cochran,
    • Divyanshi Srivastava,
    • Avanti Shrikumar,
    • Akshay Balsubramani,
    • Ross C. Hardison,
    • Anshul Kundaje,
    • and Shaun Mahony
    Genome Res. March 2022 32: 512-523; Published in Advance January 18, 2022, doi:10.1101/gr.275394.121
    .... These auPRCs also show that our neural network approach can indeed outperform related methods on this task.Next, we sought to assess the cross-species performance of another state-of-the-art deep learning model trained on a related TF binding prediction task, distinct from our binary classification setup...
  4. Method: Cross-species analysis of enhancer logic using deep learning
    • Liesbeth Minnoye,
    • Ibrahim Ihsan Taskiran,
    • David Mauduit,
    • Maurizio Fazio,
    • Linde Van Aerschot,
    • Gert Hulselmans,
    • Valerie Christiaens,
    • Samira Makhzami,
    • Monika Seltenhammer,
    • Panagiotis Karras,
    • Aline Primot,
    • Edouard Cadieu,
    • Ellen van Rooijen,
    • Jean-Christophe Marine,
    • Giorgia Egidy,
    • Ghanem-Elias Ghanem,
    • Leonard Zon,
    • Jasper Wouters,
    • and Stein Aerts
    Genome Res. December 2020 30: 1815-1834; Published in Advance July 30, 2020, doi:10.1101/gr.260844.120
    ...identity. A better understanding of how enhancers work will improve the interpretation of noncoding variation and empower the generation of cell type–specific drivers for gene therapy. Here, we explore the combination of deep learning and cross-species chromatin accessibility profiling to build explainable...
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  5. Method: Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks
    • David R. Kelley,
    • Jasper Snoek,
    • and John L. Rinn
    Genome Res. July 2016 26: 990-999; Published in Advance May 3, 2016, doi:10.1101/gr.200535.115
    ...deep neural networks predict accurately, the principles that they learn are not trivially interpretable as they are in simpler linear models. However, substantial information can be extracted from themodel by examining its parameters, modulating the flow of information through components of the network...
    OPEN ACCESS ARTICLE
  6. Method: Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
    • Zeynep Kalender Atak,
    • Ibrahim Ihsan Taskiran,
    • Jonas Demeulemeester,
    • Christopher Flerin,
    • David Mauduit,
    • Liesbeth Minnoye,
    • Gert Hulselmans,
    • Valerie Christiaens,
    • Ghanem-Elias Ghanem,
    • Jasper Wouters,
    • and Stein Aerts
    Genome Res. June 2021 31: 1082-1096; Published in Advance April 8, 2021, doi:10.1101/gr.260851.120
    ...IP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin...
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  7. Method: Deep profiling and custom databases improve detection of proteoforms generated by alternative splicing
    • Laura M. Agosto,
    • Matthew R. Gazzara,
    • Caleb M. Radens,
    • Simone Sidoli,
    • Josue Baeza,
    • Benjamin A. Garcia,
    • and Kristen W. Lynch
    Genome Res. December 2019 29: 2046-2055; Published in Advance November 14, 2019, doi:10.1101/gr.248435.119
    ...peptides, which ultimately increases the probability of detecting AEPs and EJPs (see Supplemental Fig. S2). To achieve deep, comprehensive proteome coverage, we used a workflow with multiple fractionation steps designed to decrease sample complexity and increase peptide and protein identification (Fig. 2A...
  8. Research: Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences
    • Josh T. Cuperus,
    • Benjamin Groves,
    • Anna Kuchina,
    • Alexander B. Rosenberg,
    • Nebojsa Jojic,
    • Stanley Fields,
    • and Georg Seelig
    Genome Res. December 2017 27: 2015-2024; Published in Advance November 2, 2017, doi:10.1101/gr.224964.117
    ...was performed as described (Sanger et al. 1977). Deep sequencing of plasmid DNA was performed on an Illumina NextSeq after purifying plasmid DNA using the Zymoprep yeast plasmid prep II (Zymo Research) and PCR amplification for 12 to 20 cycles.Library selectionCells from the input population were collected...
  9. Method: SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site
    • Liyang Zhang,
    • Gabriella D. Martini,
    • H. Tomas Rube,
    • Judith F. Kribelbauer,
    • Chaitanya Rastogi,
    • Vincent D. FitzPatrick,
    • Jon C. Houtman,
    • Harmen J. Bussemaker,
    • and Miles A. Pufall
    Genome Res. January 2018 28: 111-121; Published in Advance December 1, 2017, doi:10.1101/gr.222844.117
    ...substitutions ill defined. Finally, a recent algorithm based on deep learning (Alipanahi et al. 2015) performed well on a classification task but was not trained in a way that was designed to make quantitative predictions.In view of the above concerns, it is desirable that an algorithm for inferring an accurate...
  10. 12 Drosophila Genomes/Methods: Reliable prediction of regulator targets using 12 Drosophila genomes
    • Pouya Kheradpour,
    • Alexander Stark,
    • Sushmita Roy,
    • and Manolis Kellis
    Genome Res. December 2007 17: 1919-1931; Published in Advance November 7, 2007, doi:10.1101/gr.7090407
    ...with regulatory potential generally do not allow identification of regulatory targets for individual factors or miRNAs ( Elnitski et al. 2003 ; Taylor et al. 2006 ). Lastly, the comparative prediction of miRNA binding sites in 3′ UTRs proved successful (for reviews, see Lai 2004 ; Rajewsky 2006 ) but has relied...
    OPEN ACCESS ARTICLE
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