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Research: Asian wild rice is a hybrid swarm with extensive gene flow and feralization from domesticated rice
- Hongru Wang,
- Filipe G. Vieira,
- Jacob E. Crawford,
- Chengcai Chu,
- and Rasmus Nielsen
Genome Res. June 2017 27: 1029-1038; Published in Advance April 6, 2017, doi:10.1101/gr.204800.116
.../feralization is substantial and not limited to only a subset of the wild rice subgroups (Supplemental Fig. S19). Morphologically, domesticated rice has closed floret, making cross pollination difficult and keeping them largely self-fertilized. Wild rice, however, typically has open floret with exerted stigma, resulting...
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Research: The making of a new pathogen: Insights from comparative population genomics of the domesticated wheat pathogen Mycosphaerella graminicola and its wild sister species
- Eva H. Stukenbrock,
- Thomas Bataillon,
- Julien Y. Dutheil,
- Troels T. Hansen,
- Ruiqiang Li,
- Marcello Zala,
- Bruce A. McDonald,
- Jun Wang,
- and Mikkel H. Schierup
Genome Res. December 2011 21: 2157-2166; Published in Advance October 12, 2011, doi:10.1101/gr.118851.110
...The making of a new pathogen: Insights from comparative population genomics of the domesticated wheat pathogen Mycosphaerella graminicola and its wild sister species Eva H. Stukenbrock 1 , 5 , 6 , Thomas Bataillon 1 , Julien Y. Dutheil 1 , Troels T...
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Research: Assessing the readiness of Oxford Nanopore sequencing for clinical genomics applications
- Judith Arres,
- Santosh Elavalli,
- Shalini Behl,
- Daniel Matias Sanchez,
- Ayesha Al Ali,
- Abdelrahman Ahmed Yehia Abdelaziz Saad,
- Azza Attia,
- Cyla Minas,
- Sharika Pariyachery,
- Shariq Ahmed,
- Fatmah Aldhuhoori,
- Nitu Thulasidharan,
- Gurunath Katagi,
- Omar Soliman,
- Shilp Purohit,
- Vinay Kusuma,
- Raony Cardenas,
- Thyago Cardoso,
- Luis F. Paulin,
- Philippe Sanio,
- Joseph Mafofo,
- Haiguo Wu,
- Val Zvereff,
- Albarah El-Khani,
- Fahed Al Marzooqi,
- Tiago R. Magalhães,
- Fritz J. Sedlazeck,
- and Javier Quilez
Genome Res. March 2026 36: 460-471; Published in Advance February 11, 2026, doi:10.1101/gr.280134.124
...: jquilez@m42.aeAbstractLong-read sequencing (LRS) technologies, namely, Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio), have emerged as promising solutions to overcome the limitations of short-read sequencing (SRS). Nevertheless, the still higher sequencing error rates compared...
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Method: Real-time analysis and visualization of nanopore metagenomic samples with MARTi
- Ned Peel,
- Samuel Martin,
- Darren Heavens,
- Douglas W. Yu,
- Matthew D. Clark,
- and Richard M. Leggett
Genome Res. November 2025 35: 2488-2500; Published in Advance October 27, 2025, doi:10.1101/gr.280550.125
..., visualization, and exploration of metagenomic data. MARTi supports various classification methods, including BLAST, Centrifuge, and Kraken2, letting users customize parameters and utilize their own databases for taxonomic classification and antimicrobial resistance analysis. With a user-friendly, browser...
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ARTICLE: Limitations of Quantitative Gene Regulation Models: A Case Study
- Philip M. Kim and
- Bruce Tidor
Genome Res. November 2003 13: 2391-2395; doi:10.1101/gr.1207003
...Limitations of Quantitative Gene Regulation Models: A Case Study Philip M. Kim 1 , 2 and Bruce Tidor 1 , 3 , 4 , 5 1 Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA...
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Review: A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest
- Andrew D. Bates,
- Dawid Grzela,
- Maciej Studzian,
- Louise Brennan,
- Moli Williams,
- Conor Fawcett,
- Beth Hammond,
- Manreen Grewal,
- Marcin Ratajewski,
- Lukasz Pulaski,
- and Urszula L. McClurg
Genome Res. March 2026 36: 433-459; Published in Advance February 17, 2026, doi:10.1101/gr.281154.125
..., and cell state–dependent protein dynamics. In this review, we dissect the strengths, limitations, and biological relevance of current approaches for studying direct protein–DNA interactions, distinguishing between protein-centric and DNA-centric methodologies. We introduce a conceptual matrix of biological...
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Method: Strain-level metagenomic profiling using pangenome graphs with PanTax
- Wenhai Zhang,
- Yuansheng Liu,
- Guangyi Li,
- Jialu Xu,
- Enlian Chen,
- Alexander Schönhuth,
- and Xiao Luo
Genome Res. February 2026 36: 405-420; Published in Advance January 14, 2026, doi:10.1101/gr.280858.125
...limitations, including their reliance solely on species resolution, support for either short or long reads, or their confinement to a given single species. Most notably, most existing strain-level taxonomic classifiers rely on the sequence representation of multiple linear reference s, which fails to capture...
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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
...Model (Edge and Bansal 2019). While statistics-based tools offer faster runtimes, machine learning methods like DeepVariant (Poplin et al. 2018) and Clair (Zheng et al. 2022) are now preferred for both short- and long-read variant calling due to their higher accuracy. However, these machine learning...
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Method: Ultra-long sequencing for contiguous haplotype resolution of the human immunoglobulin heavy-chain locus
- Mari B. Gornitzka,
- Egil Røsjø,
- Uddalok Jana,
- Easton E. Ford,
- Alan Tourancheau,
- William D. Lees,
- Zachary Vanwinkle,
- Melissa L. Smith,
- Corey T. Watson,
- and Andreas Lossius
Genome Res. October 2025 35: 2240-2251; Published in Advance August 21, 2025, doi:10.1101/gr.280400.125
...and adaptive sampling with a bioinformatic pipeline to produce haplotype-resolved, annotated IGH assemblies. Notably, our strategy overcomes prior limitations in phasing resolution, enabling single-contig haplotype assemblies that span the entire IGH locus. We apply this method to four individuals and validate...
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Method: Automated interpretable artificial intelligence genomic prediction with AIGP
- Lei Wei,
- Ziqin Jiang,
- Baoliang Fan,
- Yidan Yan,
- Zhenqiang Xu,
- Xiaoxiang Hu,
- and Yuzhe Wang
Genome Res. March 5, 2026 gr.281006.125; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
...remains a major genetic challenge. 21 Traditional statistical methods (such as GBLUP and BayesR) have limitations, including 22 reliance on artificial prior assumptions, and hard to capture epistatic effects. Machine learning 23 (ML) has emerged as a powerful alternative for genomic prediction; however...
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