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Research: Confounding factors in assessing the enriched expression of somatic mutant allele in bulk tumor samples
- Kohei Hagiwara,
- Andrew Thrasher,
- Nadezhda V Terekhanova,
- and Jinghui Zhang
Genome Res. March 4, 2026 gr.281003.125; Published in Advance March 4, 2026, doi:10.1101/gr.281003.125
...analysis on tumor RNA-seq BAM files used the default parameters except for the parallelism 610 option which was set to 12. In the output VCF files, putative indels were classified as somatic, 611 germline, or artifactual based on the machine-learning model implemented in RNAIndel. Those 612 classified...
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Method: Graph-based deep reinforcement learning for haplotype assembly with Ralphi
- Enzo Battistella,
- Anant Maheshwari,
- Barış Ekim,
- Bonnie Berger,
- and Victoria Popic
Genome Res. December 2025 35: 2617-2625; Published in Advance November 14, 2025, doi:10.1101/gr.280569.125
...the complex sources of variability inherent to this problem domain (e.g., the nonuniformity of read coverage and variant density within the and across different populations or read length and error profiles of different sequencing platforms).Learning-based approaches, on the other hand, can detect complex...
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Review: Artificial intelligence and machine learning in cell-free-DNA-based diagnostics
- W.H. Adrian Tsui,
- Spencer C. Ding,
- Peiyong Jiang,
- and Y.M. Dennis Lo
Genome Res. January 2025 35: 1-19; doi:10.1101/gr.278413.123
...on artificial intelligence (AI) and machine learning (ML) can exploit these features holistically to augment the performance of cfDNA-based diagnostics (Fig. 1). In this review, we particularly elaborate on AI and ML technologies that are applied in cfDNA-based diagnostics, such as noninvasive prenatal testing...
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Corrigendum: Corrigendum: Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer
- Milad Razavi-Mohseni,
- Weitai Huang,
- Yu A. Guo,
- Dustin Shigaki,
- Shamaine Wei Ting Ho,
- Patrick Tan,
- Anders J. Skanderup,
- and Michael A. Beer
Genome Res. February 2026 36: 432; doi:10.1101/gr.281294.125
...-seq noted in the original article, and strengthens some P-value and RNA correlations slightly, as noted in the corrected text. SNU484 is now classified as a Mes-like cell line based on RNA-seq. None of the main conclusions of the article are changed. The only notable exception is in Figure 2A, where SNU484...
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Research: Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer
- Milad Razavi-Mohseni,
- Weitai Huang,
- Yu A. Guo,
- Dustin Shigaki,
- Shamaine Wei Ting Ho,
- Patrick Tan,
- Anders J. Skanderup,
- and Michael A. Beer
Genome Res. May 2024 34: 680-695; Published in Advance May 22, 2024, doi:10.1101/gr.278565.123
...trained on accessibility and single-cell transcriptional profiles.ResultsSequence-based machine learning model of ATAC-seq profiles identifies distinct regulatory programs driving GC heterogeneityTo understand differences in epigenomic state profiles in gastric-cancer derived cell lines, we generated ATAC...
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Research: Multisite long-read sequencing reveals the early contributions of somatic structural variations to HBV-related hepatocellular carcinoma tumorigenesis
- Tianfu Zeng,
- Haotian Liao,
- Lin Xia,
- Siyao You,
- Yanqun Huang,
- Jiaxun Zhang,
- Yahui Liu,
- Xuyan Liu,
- and Dan Xie
Genome Res. April 2025 35: 671-685; Published in Advance March 4, 2025, doi:10.1101/gr.279617.124
...the distribution of somatic SV lengths are categorized into two ranges: (50 bp, 1 kb) and (1 kb, 10 kb), stratified based on SV types. (D) The proportion of different SV size ranges within each somatic SV type.We generated long-read whole- sequencing data from all samples using the PromethION (ONT) platform...
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Research: A naturally occurring variant of MBD4 causes maternal germline hypermutation in primates
- Alexandra M. Stendahl,
- Rashesh Sanghvi,
- Samuel Peterson,
- Karina Ray,
- Ana C. Lima,
- Raheleh Rahbari,
- and Donald F. Conrad
Genome Res. December 2023 33: 2053-2059; Published in Advance November 20, 2023, doi:10.1101/gr.277977.123
...Center, we identified a rhesus macaque with a rare homozygous frameshift mutation in the gene methyl-CpG binding domain 4, DNA glycosylase (MBD4). MBD4 is responsible for the repair of C > T deamination mutations at CpG dinucleotides and has been linked to somatic hypermutation and cancer predisposition...
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Method: De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data
- Arthur Dondi,
- Nico Borgsmüller,
- Pedro F. Ferreira,
- Brian J. Haas,
- Francis Jacob,
- Viola Heinzelmann-Schwarz,
- Tumor Profiler Consortium,
- and Niko Beerenwinkel
Genome Res. April 2025 35: 900-913; Published in Advance March 19, 2025, doi:10.1101/gr.279281.124
...high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variant calling, LongSom reannotates marker gene-based cell types...
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Method: Iterative improvement of deep learning models using synthetic regulatory genomics
- André M. Ribeiro-dos-Santos and
- Matthew T. Maurano
Genome Res. November 2025 35: 2539-2549; Published in Advance October 22, 2025, doi:10.1101/gr.280540.125
...), but direct biochemical assessment of each variant in every relevant cell type and state remains impractical.An alternative approach is to train machine learning models on reference genomic sequence and then apply them to genetic variation data. Recent machine learning models including gkmSVM (Lee et al. 2015...
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Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
- Fabien Wehbe,
- Levi Adams,
- Jordan Babadoudou,
- Samantha Yuen,
- Yoon-Seong Kim,
- and Yoshiaki Tanaka
Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
...(Rittman et al. 2016). Therefore, to robustly uncover disease-associated molecular elements from single-cell data, it is critical to functionally segregate cells based on disease progression levels.Impediment of supervised deep learning in single-cell analysisTo segregate the diseased cells from the early...
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