AIMC Topic: Genome, Plant
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Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
May 24, 2020
We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, buil...
DDBJ Data Analysis Challenge: a machine learning competition to predict Arabidopsis chromatin feature annotations from DNA sequences.
Genes & genetic systems
Mar 26, 2020
Recently, the prospect of applying machine learning tools for automating the process of annotation analysis of large-scale sequences from next-generation sequencers has raised the interest of researchers. However, finding research collaborators with ...
Deep learning for plant genomics and crop improvement.
Current opinion in plant biology
Jan 24, 2020
Our era has witnessed tremendous advances in plant genomics, characterized by an explosion of high-throughput techniques to identify multi-dimensional genome-wide molecular phenotypes at low costs. More importantly, genomics is not merely acquiring m...
TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.
Scientific reports
Dec 2, 2019
One of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods a...
Identification of the expressome by machine learning on omics data.
Proceedings of the National Academy of Sciences of the United States of America
Aug 16, 2019
Accurate annotation of plant genomes remains complex due to the presence of many pseudogenes arising from whole-genome duplication-generated redundancy or the capture and movement of gene fragments by transposable elements. Machine learning on genome...
PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution.
BMC genomics
Jul 12, 2019
BACKGROUND: Our understanding of polyploid genomes is limited by our inability to definitively assign sequences to a specific subgenome without extensive prior knowledge like high resolution genetic maps or genome sequences of diploid progenitors. In...
Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data.
Nature communications
Jun 4, 2019
DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifica...
New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.
G3 (Bethesda, Md.)
May 7, 2019
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes...
Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.
G3 (Bethesda, Md.)
Dec 10, 2018
Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of ge...
Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy.
PloS one
Nov 30, 2018
Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge...
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