ArticleThe plant genome2019
Machine Learning as an Effective Method for Identifying True Single Nucleotide Polymorphisms in Polyploid Plants.
Article in The plant genome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 85 citations in OpenAlex.
- The curious case of sporadic nematode susceptibility in "Tifguard" peanut (Arachis hypogaea): seed mixture or genetic instability?G3 (Bethesda, Md.) · 2026Article
- Construction of an introgression line population for cultivated peanut (Frontiers in plant science · 2026Article
- Aerial imagery and Segment Anything Model for architectural trait phenotyping to support genetic analysis in peanut breeding.Plant phenomics (Washington, D.C.) · 2025Article
- A single hybrid origin of cultivated peanut.The Plant journal : for cell and molecular biology · 2025Article
- From bottleneck to boom: Polyploidy, genetic instability and response to artificial selection resolve the peanut paradox.The Plant journal : for cell and molecular biology · 2025Article
- Surprisingly frequent chromosomal instability in cultivated peanut.The Plant journal : for cell and molecular biology · 2025Article
- Genetic mapping of time-to-maturity trait in hypogaea x fastigiata peanut background reveals a significant effect of pod-related and flowering pattern.BMC plant biology · 2025Article
- A slow march along the South American Dry Diagonal: a multi-gene molecular phylogeny and biogeographical history of the peanut genus, Arachis L.Annals of botany · 2025Article
- Using cross-country datasets for association mapping in Arachis hypogaea L.The plant genome · 2024Article
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- Genetic characterization and mapping of the shell-strength trait in peanut.BMC plant biology · 2024Article
- Mapping QTLs for early leaf spot resistance and yield component traits using an interspecific AB-QTL population in peanut.Frontiers in plant science · 2024Article
- Harnessing the power of machine learning for crop improvement and sustainable production.Frontiers in plant science · 2024Review
- Understanding the impacts of drought on peanutsFrontiers in genetics · 2024Review
- The groundnut improvement network for Africa (GINA) germplasm collection: a unique genetic resource for breeding and gene discovery.G3 (Bethesda, Md.) · 2023Article
- Demographic history inference and the polyploid continuum.Genetics · 2023Article
- Genome-wide association studies reveal novel loci for resistance to groundnut rosette disease in the African core groundnut collection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Marker-assisted introgression of wild chromosome segments conferring resistance to fungal foliar diseases into peanut (Frontiers in plant science · 2023Article
- Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding.International journal of molecular sciences · 2022Review
- A first insight into the genetics of maturity trait in Runner × Virginia types peanut background.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single nucleotide polymorphisms (SNPs) have many advantages as molecular markers since they are ubiquitous and codominant. However, the discovery of true SNPs in polyploid species is difficult. Peanut ( L.) is an allopolyploid, which has a very low rate of true SNP calling. A large set of true and false SNPs identified from the Axiom_ 58k array was leveraged to train machine-learning models to enable identification of true SNPs directly from sequence data to reduce ascertainment bias. These models achieved accuracy rates above 80% using real peanut RNA sequencing (RNA-seq) and whole-genome shotgun (WGS) resequencing data, which is higher than previously reported for polyploids and at least a twofold improvement for peanut. A 48K SNP array, Axiom_2, was designed using this approach resulting in 75% accuracy of calling SNPs from different tetraploid peanut genotypes. Using the method to simulate SNP variation in several polyploids, models achieved >98% accuracy in selecting true SNPs. Additionally, models built with simulated genotypes were able to select true SNPs at >80% accuracy using real peanut data. This work accomplished the objective to create an effective approach for calling highly reliable SNPs from polyploids using machine learning. A novel tool was developed for predicting true SNPs from sequence data, designated as SNP machine learning (SNP-ML), using the described models. The SNP-ML additionally provides functionality to train new models not included in this study for customized use, designated SNP machine learner (SNP-MLer). The SNP-ML is publicly available.
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.