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Genome Annotation and Assembly

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Jun 5, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Identification, characterization, and design of plant genome sequences using deep learning.

Zhenye Wang^1,2,3, Hao Yuan^1,2,3, Jianbing Yan^1,4

¹National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.

The Plant Journal : for Cell and Molecular Biology

|December 12, 2024

Summary

Deep learning significantly advances plant biology by analyzing genomic data for gene expression and epigenetic features. Future applications include intelligent breeding and sequence design using advanced AI models.

Keywords:

deep learning genome sequence intelligent design plants prediction

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Area of Science:

Plant Biology
Bioinformatics
Genomics

Background:

Deep learning excels at processing large datasets and modeling complex relationships.
Its application in plant biology is rapidly expanding across various research areas.

Purpose of the Study:

To review the current applications of deep learning in plant biology.
To discuss advancements in motif mining, functional component design, and protein/genomic prediction.
To provide future prospects for deep learning in plant science.

Main Methods:

Review of deep learning techniques applied to plant genome sequence analysis.
Elaboration on generative adversarial networks, large models, and attention mechanisms for motif mining and design.
Discussion of deep learning for protein structure/function prediction and genomic prediction.

Main Results:

Deep learning accurately predicts gene expression, chromatin interactions, and epigenetic features from plant genome sequences.
Current methods enable motif mining and functional component design using advanced AI.
Progress is evident in protein structure/function and genomic prediction.

Conclusions:

Deep learning is a powerful tool for plant biology research, offering insights into genomics and epigenetics.
Advancements in AI models are driving innovation in sequence design and functional prediction.
Future directions include integrating multi-omics data and developing large language models for intelligent plant breeding.