Power-law penalties correct distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions [0.03%]
幂律惩罚可校正单细胞共访问和染色质相互作用深度学习预测中的距离偏置
Luca Schlegel,Fabio Gómez Cano,Alexandre P Marand et al.
Luca Schlegel et al.
Scalable proxies for 3D genome contacts-such as single-cell co-accessibility and deep learning predictions-have emerged as powerful alternatives to chromatin capture-based methods, but predictions systematically overestimate long-range inte...
Dimitri Desvillechabrol,Rania Ouazahrou,Juliana Pipoli da Fonseca et al.
Dimitri Desvillechabrol et al.
Genome assembly from long-read sequencing data has become a standard approach for resolving complex genomic regions and producing high-contiguity assemblies. However, the diversity of available assemblers, their varying performance across s...
Correction to 'Genome sequence assembly and annotation of MATA and MATB strains of Yarrowia lipolytica' [0.03%]
对“MATA和MATB菌株的Yarrowia lipolytica基因组序列组装与注释”的勘误
[This corrects the article DOI: 10.1093/nargab/lqaf175.]. © The Author(s) 2026. Published by Oxford University Press.
Published Erratum
NAR genomics and bioinformatics. 2026 Jun 22;8(2):lqag064. DOI:10.1093/nargab/lqag064 2026
Comprehensive mapping of identical sequences across human proteins emphasizes the widespread issue of shared epitopes in self-antigens [0.03%]
人类蛋白质中相同序列的全面映射强调了自身抗原中共有表位的普遍性问题
Guillaume Kellermann,Olivier Croce,Baharia Mograbi et al.
Guillaume Kellermann et al.
Shared epitopes pose safety and efficacy issues for T-cell immunotherapy. To characterize the extent of this problem, we performed a computational analysis establishing a complete atlas of shared identical sequences across the human and mur...
PolyGenie: a reproducible Nextflow pipeline for phenome-wide association studies using polygenic risk scores [0.03%]
基于多基因风险评分的表型全关联研究的可重复Nextflow管线PolyGenie
Xavier Farré,Mireia Gasco,Natalia Blay et al.
Xavier Farré et al.
Phenome-wide association studies (PheWAS) using polygenic risk scores (PRS) offer a powerful framework for exploring the shared genetic architecture of complex traits across diverse phenotypic domains. However, no standardized, portable pip...
Exploring molecular signatures of senescence with marker, an R Toolkit for evaluating gene sets as phenotypic markers [0.03%]
Marker：一种用于评估基因集作为表型标志的R工具包——利用分子标志探索衰老现象
Rita Martins-Silva,Alexandre Kaizeler,Nuno L Barbosa-Morais
Rita Martins-Silva
Many biological processes, including cellular senescence, manifest as diverse phenotypes across cell types and conditions. Lacking definitive markers, researchers often rely on the expression of sets of genes to identify these complex state...
Predicting isocitrate dehydrogenase mutation status in acute myeloid leukemia from gene expression profiles by machine learning [0.03%]
基于基因表达谱用机器学习预测急性髓系白血病的异柠檬酸脱氢酶突变状态
Ina Jung,Anne-Laure Vitte,Florent Chuffart et al.
Ina Jung et al.
We developed machine-learning models to predict isocitrate dehydrogenase (IDH) mutation status in acute myeloid leukemia (AML) from gene expression profiles and to reconstruct missing IDH annotations across public datasets. Transcriptomic d...
TSProm: deep learning framework to predict tissue-specific regulatory logic [0.03%]
基于深度学习的预测组织特异性调控逻辑框架
Pallavi Surana,Pratik Dutta,Nimisha Papineni et al.
Pallavi Surana et al.
Characterizing tissue-specific (TSp) gene expression is crucial for understanding development and disease; however, traditional expression-based methods often overlook the latent "regulatory grammar" embedded in non-coding DNA, particularly...
Mitsu Raval,Yancheng Zhou,Miki Lynch et al.
Mitsu Raval et al.
Protein translation is a highly regulated process influenced by multiple factors at the initiation, elongation, and termination stages. One notable regulatory element of the ribosome is the CAR interaction surface, a three-residue motif in ...
TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification [0.03%]
基于张量融合的有向加权图神经网络的癌症亚型分类方法
Tiantian Yang,Zhiqian Chen
Tiantian Yang
Integration and analysis of multi-omics data provide valuable insights for improving cancer subtype classification. However, such data are inherently heterogeneous, high-dimensional, and exhibit complex intra- and inter-modality dependencie...