Searching journal content for articles similar to Lyons-Weiler et al. 13 (3): 503.

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  1. Method: MCHelper automatically curates transposable element libraries across eukaryotic species
    • Simon Orozco-Arias,
    • Pío Sierra,
    • Richard Durbin,
    • and Josefa González
    Genome Res. December 2024 34: 2256-2268; Published in Advance December 9, 2024, doi:10.1101/gr.278821.123
    .... Genome-wide TE annotations are also improved, including larger unfragmented insertions. Moreover, MCHelper is an easy-to-install and easy-to-use tool.After two decades of sequencing projects, reference s for thousands of eukaryotic species are already available and many more are currently being sequenced...
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  2. Method: Identification of human silencers by correlating cross-tissue epigenetic profiles and gene expression
    • Di Huang,
    • Hanna M. Petrykowska,
    • Brendan F. Miller,
    • Laura Elnitski,
    • and Ivan Ovcharenko
    Genome Res. April 2019 29: 657-667; Published in Advance March 18, 2019, doi:10.1101/gr.247007.118
    ...of silencers identifies putative regulatory mechanisms at the foundation of silencer activity across different cell lines and provides directions for follow-up biochemical analyses of these sequences.A machine learning model can accurately identify silencer sequences de novoFor the identification of silencers...
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  3. Perspective: Applications of single-cell genomics and computational strategies to study common disease and population-level variation
    • Benjamin J. Auerbach,
    • Jian Hu,
    • Muredach P. Reilly,
    • and Mingyao Li
    Genome Res. October 2021 31: 1728-1741; doi:10.1101/gr.275430.121
    ...and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies...
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  4. LETTER: Assessing the limits of genomic data integration for predicting protein networks
    • Long J. Lu,
    • Yu Xia,
    • Alberto Paccanaro,
    • Haiyuan Yu,
    • and Mark Gerstein
    Genome Res. July 2005 15: 945-953; doi:10.1101/gr.3610305
    ...groups as follows: (1) learning to infer the causal structure of cellular networks from quantitative measurements ( Friedman 2004 ); (2) classification based on a set of probabilistic rules. Here, we focus on the second classification aspect of Bayesian approaches. In addition to protein...
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