ArticleNature communications2021
Machine-learning predicts genomic determinants of meiosis-driven structural variation in a eukaryotic pathogen.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Development and internal validation of a clinical prediction model for hemodialysis-related headache using LASSO and Boruta feature selection.Scientific reports · 2026Article
- Recurrent Chromosome Destabilization Through Repeat-Mediated Rearrangements in a Fungal Pathogen.Genome biology and evolution · 2026Article
- Machine learning-based prediction of human structural variation and characterization of associated sequence determinants.bioRxiv : the preprint server for biology · 2026Article
- Patterns and mechanisms of fungal genome plasticity.Current biology : CB · 2025Review
- Recombination and transposition drive genomic structural variation potentially impacting life history traits in a host-generalist fungal plant pathogen.BMC biology · 2025Article
- Development of an early prediction model for vomiting during hemodialysis using LASSO regression and Boruta feature selection.Scientific reports · 2025Article
- Copy number variation introduced by a massive mobile element facilitates global thermal adaptation in a fungal wheat pathogen.Nature communications · 2024Article
- The landscape and predicted roles of structural variants in Fusarium graminearum genomes.G3 (Bethesda, Md.) · 2024Article
- Distinct genomic contexts predict gene presence-absence variation in different pathotypes of Magnaporthe oryzae.Genetics · 2024Article
- Recent reactivation of a pathogenicity-associated transposable element is associated with major chromosomal rearrangements in a fungal wheat pathogen.Nucleic acids research · 2024Article
- Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen.PLoS pathogens · 2023Article
- Reliable genotyping of recombinant genomes using a robust hidden Markov model.Plant physiology · 2023Article
- Development and assessment of novel machine learning models to predict the probability of postoperative nausea and vomiting for patient-controlled analgesia.Scientific reports · 2023Article
- Fungal Strains with Identical Genomes Were Found at a Distance of 2000 Kilometers after 40 Years.Journal of fungi (Basel, Switzerland) · 2022Article
- Telomere-to-Telomere Genome Sequences across a Single Genus Reveal Highly Variable Chromosome Rearrangement Rates but Absolute Stasis of Chromosome Number.Journal of fungi (Basel, Switzerland) · 2022Article
- Giant Starship Elements Mobilize Accessory Genes in Fungal Genomes.Molecular biology and evolution · 2022Article
- Domestication Shapes Recombination Patterns in Tomato.Molecular biology and evolution · 2022Article
- A machine learning-based risk warning platform for potentially inappropriate prescriptions for elderly patients with cardiovascular disease.Frontiers in pharmacology · 2022Article
- Population-level deep sequencing reveals the interplay of clonal and sexual reproduction in the fungal wheat pathogenMicrobial genomics · 2021Article
- A population-level invasion by transposable elements triggers genome expansion in a fungal pathogen.eLife · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Species harbor extensive structural variation underpinning recent adaptive evolution. However, the causality between genomic features and the induction of new rearrangements is poorly established. Here, we analyze a global set of telomere-to-telomere genome assemblies of a fungal pathogen of wheat to establish a nucleotide-level map of structural variation. We show that the recent emergence of pesticide resistance has been disproportionally driven by rearrangements. We use machine learning to train a model on structural variation events based on 30 chromosomal sequence features. We show that base composition and gene density are the major determinants of structural variation. Retrotransposons explain most inversion, indel and duplication events. We apply our model to Arabidopsis thaliana and show that our approach extends to more complex genomes. Finally, we analyze complete genomes of haploid offspring in a four-generation pedigree. Meiotic crossover locations are enriched for new rearrangements consistent with crossovers being mutational hotspots. The model trained on species-wide structural variation accurately predicts the position of >74% of newly generated variants along the pedigree. The predictive power highlights causality between specific sequence features and the induction of chromosomal rearrangements. Our work demonstrates that training sequence-derived models can accurately identify regions of intrinsic DNA instability in eukaryotic genomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.