AIMC Topic: Arabidopsis
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Predictive Models of Genetic Redundancy in Arabidopsis thaliana.
Molecular biology and evolution
Jul 29, 2021
Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studi...
A self-attention model for inferring cooperativity between regulatory features.
Nucleic acids research
Jul 21, 2021
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information fr...
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.
Briefings in bioinformatics
Jul 20, 2021
A promoter is a region in the DNA sequence that defines where the transcription of a gene by RNA polymerase initiates, which is typically located proximal to the transcription start site (TSS). How to correctly identify the gene TSS and the core prom...
Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning.
Briefings in bioinformatics
May 20, 2021
MOTIVATION: The genome-wide discovery of microRNAs (miRNAs) involves identifying sequences having the highest chance of being a novel miRNA precursor (pre-miRNA), within all the possible sequences in a complete genome. The known pre-miRNAs are usuall...
Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.
Briefings in bioinformatics
May 20, 2021
DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understandi...
The Gene Ontology resource: enriching a GOld mine.
Nucleic acids research
Jan 8, 2021
The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The n...
m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.
Nucleic acids research
Jan 8, 2021
N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and disease pathogenesis. We present here a comprehensive knowledgebase, m6A-Atlas, for unraveling the ...
Prediction of condition-specific regulatory genes using machine learning.
Nucleic acids research
Jun 19, 2020
Recent advances in genomic technologies have generated data on large-scale protein-DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has bec...
DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis.
GigaScience
Mar 1, 2020
BACKGROUND: High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addres...
The Genome3D Consortium for Structural Annotations of Selected Model Organisms.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2020
Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Or...
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