Searching journal content for articles similar to Vernikos and Parkhill 18 (2): 331.

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Method: Resolving the chromatin impact of mosaic variants with targeted Fiber-seq
- Stephanie C. Bohaczuk,
- Zachary J. Amador,
- Chang Li,
- Benjamin J. Mallory,
- Elliott G. Swanson,
- Jane Ranchalis,
- Mitchell R. Vollger,
- Katherine M. Munson,
- Tom Walsh,
- Morgan O. Hamm,
- Yizi Mao,
- Andre Lieber,
- and Andrew B. Stergachis
Genome Res. December 2024 34: 2269-2278; Published in Advance December 9, 2024, doi:10.1101/gr.279747.124
...information with the ability to evaluate these features within challenging genomic regions that are known to play a pivotal role in many human diseases (Cooper et al. 2011). We hypothesized that the single-molecule long-read nature of Fiber-seq would be well-suited for investigating the chromatin impact...
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Mini-Review: A Hitchhiker's Guide to long-read genomic analysis
- Medhat Mahmoud,
- Daniel P. Agustinho,
- and Fritz J. Sedlazeck
Genome Res. April 2025 35: 545-558; doi:10.1101/gr.279975.124
...-seq (including epigenetic modifications). We recommend that readers interested in more details about the two platforms read the review by Logsdon et al. (2020).Both PacBio and ONT excel in resolving repetitive elements and identifying complex genomic variants, including structural variants (SVs), which have...
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Mini-Review: Large-scale genomic analysis of the domestic dog informs biological discovery
- Reuben M. Buckley and
- Elaine A. Ostrander
Genome Res. June 2024 34: 811-821; Published in Advance July 2, 2024, doi:10.1101/gr.278569.123
...Reuben M. Buckley and Elaine A. Ostrander National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA Corresponding author: eostrand@mail.nih.govAbstractRecent advances in genomics, coupled with a unique population structure and remarkable levels...
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Method: Graph-based self-supervised learning for repeat detection in metagenomic assembly
- Ali Azizpour,
- Advait Balaji,
- Todd J. Treangen,
- and Santiago Segarra
Genome Res. September 2024 34: 1468-1476; Published in Advance July 19, 2024, doi:10.1101/gr.279136.124
...in red.Previous studies have employed prespecified graph features in combination with machine learning techniques to address the challenge of detecting repeats, treating it as a node classification problem (Ghurye and Pop 2016; Ghurye et al. 2019). In this context, the nodes of the graph represent DNA...
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Perspective: Unraveling the hidden complexity of cancer through long-read sequencing
- Qiuhui Li,
- Ayse G. Keskus,
- Justin Wagner,
- Michal B. Izydorczyk,
- Winston Timp,
- Fritz J. Sedlazeck,
- Alison P. Klein,
- Justin M. Zook,
- Mikhail Kolmogorov,
- and Michael C. Schatz
Genome Res. April 2025 35: 599-620; Published in Advance March 20, 2025, doi:10.1101/gr.280041.124
...by optimally weighting diverse genomic features (Shihab et al. 2015). To streamline the annotation process, PhD-SNPg, a lightweight, sequence-based machine learning tool, enables efficient analysis of SNPs and indels across coding and noncoding regions (Capriotti and Fariselli 2023). With advancements in SV...
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LETTER: Comparative Genomic Sequence Analysis of the Human Chromosome 21 Down Syndrome Critical Region
- Atsushi Toyoda,
- Hideki Noguchi,
- Todd D. Taylor,
- Takehiko Ito,
- Mathew T. Pletcher,
- Yoshiyuki Sakaki,
- Roger H. Reeves,
- and Masahira Hattori
Genome Res. September 1, 2002 12: 1323-1332; doi:10.1101/gr.153702
...in the Human DSCR and the Corresponding Mouse Region Landscape of Genomic Structure The GC-contents of mouse and human were 45.35% and 42.4%, respectively, in good agreement with results for the whole in each case ( Hattori et al. 2000 ; Mallon et al. 2000 ; Lander et al. 2001 ). CpG islands were predicted...
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Method: DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools
- Anupama Jha,
- Stephanie C. Bohaczuk,
- Yizi Mao,
- Jane Ranchalis,
- Benjamin J. Mallory,
- Alan T. Min,
- Morgan O. Hamm,
- Elliott Swanson,
- Danilo Dubocanin,
- Connor Finkbeiner,
- Tony Li,
- Dale Whittington,
- William Stafford Noble,
- Andrew B. Stergachis,
- and Mitchell R. Vollger
Genome Res. November 2024 34: 1976-1986; Published in Advance June 7, 2024, doi:10.1101/gr.279095.124
...and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.Highly accurate long-read single-molecule DNA sequencing has revolutionized the comprehensive assembly of phased genetic architectures, enabling the first complete human assemblies (Wenger et al. 2019; Vollger...
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Mini-Review: The paradox of R-loops: guardians of the genome or drivers of disease?
- Shachinthaka D. Dissanayaka Mudiyanselage,
- Phillip Wulfridge,
- and Kavitha Sarma
Genome Res. September 2025 35: 1919-1928; Published in Advance August 6, 2025, doi:10.1101/gr.278992.124
...organisms (Jenjaroenpun et al. 2015, 2017). More recently, deep learning prediction models were trained to identify R-loops by training on an integrated cohort with numerous experimental data sets (Hu et al. 2024). Continued development of machine learning and artificial intelligence in research may shed...
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Resource: CGC1, a new reference genome for Caenorhabditis elegans
- Kazuki Ichikawa,
- Massa J. Shoura,
- Karen L. Artiles,
- Dae-Eun Jeong,
- Chie Owa,
- Haruka Kobayashi,
- Yoshihiko Suzuki,
- Manami Kanamori,
- Yu Toyoshima,
- Yuichi Iino,
- Ann E. Rougvie,
- Lamia Wahba,
- Andrew Z. Fire,
- Erich M. Schwarz,
- and Shinichi Morishita
Genome Res. August 2025 35: 1902-1918; Published in Advance July 15, 2025, doi:10.1101/gr.280274.124
...organization, and the functions of highly repetitive genomic loci. Genetic polymorphism in wild isolates of C. elegans should be more reliably assigned to SNVs and genomic islands of elevated diversity in the CGC1 assembly than in N2 (Crombie et al. 2019; Lee et al. 2021). Even for putatively identical wild...
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Method: Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data
- Yasin Uzun,
- Hao Wu,
- and Kai Tan
Genome Res. January 2021 31: 101-109; Published in Advance November 20, 2020, doi:10.1101/gr.267047.120
...2016). In MAPLE, we used CpG frequency at higher resolution, as a vector of CpG frequencies of multiple genomic bins tiled across the promoter. This step generates the gene-dependent but cell-independent feature set to be used in the learning model. For the gene- and cell-dependent feature set, we...
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