Searching journal content for articles similar to Sadhuka et al. 33 (7): 1101.

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Method: Quantifying pathological progression from single-cell transcriptomic data with scPSS
- Samin Rahman Khan,
- M Saifur Rahman,
- M. Sohel Rahman,
- and Md Abul Hassan Samee
Genome Res. February 2026 36: 375-386; Published in Advance January 14, 2026, doi:10.1101/gr.280411.125
...modeling choices in scPSS, such as using PCA and Euclidean distance, were deliberate. In the absence of clearly labeled diseased cells, attempting to learn more complex embedding or distance functions risks overfitting to irrelevant patterns and failing to identify true disease states. The simplicity of sc...
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Method: Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases
- Prashant S. Emani,
- Maya N. Geradi,
- Gamze Gürsoy,
- Monica R. Grasty,
- Andrew Miranker,
- and Mark B. Gerstein
Genome Res. December 2023 33: 2156-2173; Published in Advance December 14, 2023, doi:10.1101/gr.278322.123
.... Striking a balance requires clear reidentification and inference risk quantification, relative to the proposed benefit of data release. In response to this, we provide a computational tool that assesses the degree to which a set of released SNPs could lead to genotypic and phenotypic inferences, using...
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Research: Confounding factors in assessing the enriched expression of somatic mutant alleles in bulk tumor samples
- Kohei Hagiwara,
- Andrew Thrasher,
- Nadezhda V. Terekhanova,
- and Jinghui Zhang
Genome Res. March 4, 2026 ; Published in Advance March 4, 2026, doi:10.1101/gr.281003.125
...between tumor and normal cells. In the assessment with bulk data, we surrogated the e as the TPM ratio between tumor and tissue-matched normal controls adjusted for CNAs to evaluate differential expression (Methods) (Fig. 3C). Consistent with our model, ewas higher for genes carrying elevated AEV indels...
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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
...with SRS, need for customized pipelines, rapidly updating software, and incipient scalability continue to present challenges for adopting ONT in standard clinical practice. Here we assess the performance of ONT (R9 and R10 chemistries) in comparison to Illumina and MGI across 17 well...
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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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Research: Transcriptomic landscape of transposable elements reveals LTR7-PLAAT4 as a potential oncogene and therapeutic target in pancreatic adenocarcinoma
- Meilong Shi,
- Chuanqi Teng,
- Shan Zhang,
- Xiaobo He,
- Lingyun Xu,
- Fengxian Han,
- Rongqi Wen,
- Ganjun Yu,
- Jingwen Liu,
- Yang Feng,
- Yanfeng Wu,
- Yan Ren,
- Gang Jin,
- and Jing Li
Genome Res. February 2026 36: 275-290; Published in Advance January 8, 2026, doi:10.1101/gr.280528.125
...), and more recently, (spatial) single-cell sequencing approaches (Moncada et al. 2020; Ma and Zhou 2022). Recent advances in organoid models represent a transformative development in cancer research. Organoids, cultured in vitro, retain self-renewal and self-organizing capabilities that mimic the structure...
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Method: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
- Quanyou Cai,
- Lihui Lin,
- Xin Liu,
- and Jiekai Chen
Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
...'s performance across different spatial transcriptomic platforms and data sets.Metrics for benchmarking performanceTo quantitatively evaluate the performance of spatial reconstruction, we employed the following metrics, each assessing a specific aspect of accuracy or similarity between the true and predicted...
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Research: Transcriptomic and proteomic effects of gene deletion are not evolutionarily conserved
- Yang Li and
- Jianzhi Zhang
Genome Res. March 2025 35: 512-521; Published in Advance February 18, 2025, doi:10.1101/gr.280008.124
...sensitivity of these effects to the genetic background, explaining why these effects are not evolutionarily conserved. Together, our results suggest that most transcriptomic and proteomic effects of gene deletion do not inform selected-effect function. This finding has important implications for assessing and...
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Research: Enhanced detection of RNA modifications and read mapping with high-accuracy nanopore RNA basecalling models
- Gregor Diensthuber,
- Leszek P. Pryszcz,
- Laia Llovera,
- Morghan C. Lucas,
- Anna Delgado-Tejedor,
- Sonia Cruciani,
- Jean-Yves Roignant,
- Oguzhan Begik,
- and Eva Maria Novoa
Genome Res. November 2024 34: 1865-1877; Published in Advance September 13, 2024, doi:10.1101/gr.278849.123
...C, hm5C, ac4C, Ψ, m1Ψ, and m5U) and the three RNA basecalling models benchmarked (default, IVT, and SUP). To assess the performance of tested RNA basecalling models, error signatures (mismatch, deletion, and insertion frequency) were used. (B) Comparison of basecalling accuracies obtained from each...
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Method: Matrix sketching framework for linear mixed models in association studies
- Myson Burch,
- Aritra Bose,
- Gregory Dexter,
- Laxmi Parida,
- and Petros Drineas
Genome Res. September 2024 34: 1304-1311; Published in Advance September 4, 2024, doi:10.1101/gr.279230.124
...equally to this work. Corresponding author: pdrineas@purdue.eduAbstractLinear mixed models (LMMs) have been widely used in -wide association studies to control for population stratification and cryptic relatedness. However, estimating LMM parameters is computationally expensive, necessitating large...
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