AIMC Topic: Genome, Plant
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Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes.
Briefings in bioinformatics
Jan 19, 2023
LTR-retrotransposons are the most abundant repeat sequences in plant genomes and play an important role in evolution and biodiversity. Their characterization is of great importance to understand their dynamics. However, the identification and classif...
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...
Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2021
As the interest in genetic resequencing increases, so does the need for effective mathematical, computational, and statistical approaches. One of the difficult problems in genome annotation is determination of precise positions of transcription start...
Revisiting CRISPR/Cas-mediated crop improvement: Special focus on nutrition.
Journal of biosciences
Jan 1, 2020
Genome editing (GE) technology has emerged as a multifaceted strategy that instantaneously popularised the mechanism to modify the genetic constitution of an organism. The clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-a...
Computational aspects underlying genome to phenome analysis in plants.
The Plant journal : for cell and molecular biology
Jan 1, 2019
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promisi...
Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.
The plant genome
Jul 1, 2018
New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive natur...
microRPM: a microRNA prediction model based only on plant small RNA sequencing data.
Bioinformatics (Oxford, England)
Apr 1, 2018
MOTIVATION: MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine ...
Gramene 2018: unifying comparative genomics and pathway resources for plant research.
Nucleic acids research
Jan 4, 2018
Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species. The current release, #54, includes over 1.7 million genes from 44 reference genomes, most of which were organized into 62,...
Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.
GigaScience
Nov 1, 2017
The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses...
TSSPlant: a new tool for prediction of plant Pol II promoters.
Nucleic acids research
May 5, 2017
Our current knowledge of eukaryotic promoters indicates their complex architecture that is often composed of numerous functional motifs. Most of known promoters include multiple and in some cases mutually exclusive transcription start sites (TSSs). M...
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