Latest Articles
The convergence of artificial intelligence (AI) and biomedical data is transforming precision medicine by enabling the use of genetic risk factors (GRFs) for customized healthcare services based on individual needs. Although GRFs play an essential role in disease susceptibility, progression, and ...
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, plastics, petroleum, and pharmacological products ...
Gene duplication is the primary source of new genes, resulting in most genes having identifiable paralogs. Over time, paralog pairs may diverge in some respects but many retain the ability to perform the same functional role. Protein sequence identity is often used as a proxy for functional ...
Ribonucleic acid (RNA) structural motif identification is a crucial step for understanding RNA structure and functionality. Due to the complexity and variations of RNA 3D structures, identifying RNA structural motifs is challenging and time-consuming. Particularly, discovering new RNA structural ...
Understanding the governing rules of complex biological systems remains a significant challenge due to the nonlinear, high-dimensional nature of biological data. In this study, we present CLERA, a novel end-to-end computational framework designed to uncover parsimonious dynamical models and ...
Editor's Choice Articles
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, plastics, petroleum, and pharmacological products ...
Gene duplication is the primary source of new genes, resulting in most genes having identifiable paralogs. Over time, paralog pairs may diverge in some respects but many retain the ability to perform the same functional role. Protein sequence identity is often used as a proxy for functional ...
Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete ...
The scale of modern datasets has necessitated innovations to solve even the most traditional and fundamental of computational problems. Set membership and set cardinality are both examples of simple queries that, for large enough datasets, quickly become challenging using traditional approaches. ...
Accessory chromosomes, found in some but not all individuals of a species, play an important role in pathogenicity and host specificity in fungal plant pathogens. However, their variability complicates reference-based analysis, especially when these chromosomes are missing in the reference genome. ...
Editor-in-Chief
Cedric Notredame
Centre for Genomic Regulation, Barcelona, Spain
Editorial Board
About the journal
NAR Genomics and Bioinformatics is an interdisciplinary journal focused on genomics and bioinformatics large-scale data analysis. It aims at providing the community with high quality results, analysis and methods in all aspects of genomics and bioinformatics. Reproducibility is a strong focus of the journal, and all entries will have to comply with strict guidelines ensuring the perfect reproducibility of both experimental and bioinformatics analysis.
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Editor's Choice Articles
Explore articles that have been hand-picked from Editor-in-Chief Cedric Notredame, as the best research from NAR Genomics and Bioinformatics.
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High-Impact Research Collection
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Compositional Data Analysis and Related Methods Applied to Genomics
It is our great pleasure to present this collection of articles on compositional data analysis (CoDA) to the readers of NAR Genomics and Bioinformatics.
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