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{\bf Background}: Several features are known to correlate with the GC-content in the human genome, including recombination rate, gene density and distance to telomere. However, by testing for pairwise correlation only, it is impossible to…

Genomics · Quantitative Biology 2012-05-07 Jan Freudengerb , Mingyi Wang , Yaning Yang , Wentian Li

The GC-content is very variable in different genome regions and species but although many hypothesis we still do not know the reason why. Here we show that a relationship exists with the mutation rate, in particular we noticed a new…

Populations and Evolution · Quantitative Biology 2013-07-24 Valentina Agoni

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the…

Genomics · Quantitative Biology 2013-03-12 John A. Capra , Melissa J. Hubisz , Dennis Kostka , Katherine S. Pollard , Adam Siepel

Pangenome variation graphs (PVGs) allow for the representation of genetic diversity in a more nuanced way than traditional reference-based approaches. Here we focus on how PVGs are a powerful tool for studying genetic variation in viruses,…

Genomics · Quantitative Biology 2025-06-18 Tim Downing

Relation of genome sizes to organisms complexity is still described rather equivocally. Neither the number of genes (G-value), nor the total amount of DNA (C-value) correlates consistently with phenotype complexity. Using information theory…

Genomics · Quantitative Biology 2007-05-23 Dmitri V. Parkhomchuk

In this paper we propose a method and discuss its computational implementation as an integrated tool for the analysis of viral genetic diversity on data generated by high-throughput sequencing. Most methods for viral diversity estimation…

This study presents the first global, 1 Mbp level analysis of patterns of nucleotide substitutions along the human lineage. The study is based on the analysis of a large amount of repetitive elements deposited into the human genome since…

Genomics · Quantitative Biology 2007-05-23 Peter F Arndt , Terence Hwa , Dmitri A Petrov

Motivation: Genome-wide association studies (GWASs), which assay more than a million single nucleotide polymorphisms (SNPs) in thousands of individuals, have been widely used to identify genetic risk variants for complex diseases. However,…

Computational Engineering, Finance, and Science · Computer Science 2015-01-27 Ben Teng , Can Yang , Jiming Liu , Zhipeng Cai , Xiang Wan

Gene-sharing networks provide a powerful framework to study the evolution of viruses and mobile genetic elements. These bipartite networks, which link genes to the genomes that contain them, exhibit characteristic degree distributions: a…

Populations and Evolution · Quantitative Biology 2026-04-16 Jaime Iranzo , Pedro Jódar , Eugene V. Koonin , Susanna Manrubia , José A. Cuesta

The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains unclear why this universal code has arisen…

Populations and Evolution · Quantitative Biology 2023-05-10 Yuji Omachi , Nen Saito , Chikara Furusawa

Genetic regulatory networks (GRNs) have been widely studied, yet there is a lack of understanding with regards to the final size and properties of these networks, mainly due to no network currently being complete. In this study, we analyzed…

Molecular Networks · Quantitative Biology 2019-03-13 Adrian I. Campos-González , Julio A. Freyre-González

Genomes in living organisms consist of the nucleotides adenine (A), guanine (G), cytosine (C) and thymine (T). All prokaryotes have genomes consisting of double-stranded DNA, where the A's and G's (purines) of one strand bind respectively…

Populations and Evolution · Quantitative Biology 2019-08-27 Jon Bohlin , Brittany Rose , Ola Brynildsrud , Birgitte Freiesleben De Blasio

The advent of high-throughput sequencing technologies has lead to vast comparative genome sequences. The construction of gene-gene interaction networks or dependence graphs on the genome scale is vital for understanding the regulation of…

Methodology · Statistics 2023-03-06 Xinyao Fan , Harry Joe , Yongjin Park

The phenotype of any organism on earth is, in large part, the consequence of interplay between numerous gene products encoded in the genome, and such interplay between gene products affects the evolutionary fate of the genome itself through…

Molecular Networks · Quantitative Biology 2012-01-04 Pan-Jun Kim , Nathan D. Price

Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Vector autoregressive models (VAR) are simple and easy to fit, but have limited application because of their…

Machine Learning · Computer Science 2025-12-09 Malik Shahid Sultan , Hernando Ombao , Maurizio Filippone

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological…

Computer Vision and Pattern Recognition · Computer Science 2024-09-13 Hadar Hezi , Daniel Shats , Daniel Gurevich , Yosef E. Maruvka , Moti Freiman

Viruses represent the most abundant biological entities on Earth and play a pivotal role in microbial ecosystems, yet, as prominent human pathogens, they are closely linked to human morbidity and mortality. Accurate identification of viral…

Databases · Computer Science 2026-04-30 Wenxi Zhu , Wensheng Gan , Zhenlian Qi

A model is presented relating the evolution of genomic GC content over time to AT$\rightarrow$GC and GC$\rightarrow$AT mutation rates. By employing It\^o calculus it is shown that if mutation rates in asexually reproducing organisms are…

Quantitative Methods · Quantitative Biology 2022-01-11 Jon Bohlin

There are a number of existing studies analysing the convergence behaviour of graph neural networks on large random graphs. Unfortunately, the majority of these studies do not model correlations between node features, which would naturally…

Machine Learning · Computer Science 2026-02-19 Mohammed Zain Ali Ahmed

We study properties of Graph Convolutional Networks (GCNs) by analyzing their behavior on standard models of random graphs, where nodes are represented by random latent variables and edges are drawn according to a similarity kernel. This…

Machine Learning · Statistics 2020-10-26 Nicolas Keriven , Alberto Bietti , Samuel Vaiter
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