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Related Concept Videos

Genomics02:02

Genomics

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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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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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.
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Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Updated: Dec 15, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Deep learning models in genomics; are we there yet?

Lefteris Koumakis1

  • 1Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Heraklion, Crete, Greece.

Computational and Structural Biotechnology Journal
|July 9, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning models are revolutionizing genomics by analyzing big data for disease prediction and treatment. These advanced machine learning techniques offer higher accuracy, accelerating precision medicine advancements.

Keywords:
BioinformaticsComputational biologyDeep learningGene expression and regulationGenomicsPrecision medicine

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput sequencing generates vast amounts of genomics data, necessitating advanced analytical methods.
  • Bioinformatics algorithms increasingly rely on machine learning and deep learning for pattern identification and disease modeling.

Purpose of the Study:

  • To review prominent deep learning models used in genomics research.
  • To highlight potential challenges and future research directions for deep learning in this field.

Main Methods:

  • Review of current deep learning architectures applied to genomics data.
  • Analysis of the performance and accuracy of deep learning models compared to existing methodologies.

Main Results:

  • Deep learning models demonstrate superior accuracy in specific genomics tasks.
  • The application of deep learning is rapidly expanding within bioinformatics and computational biology.

Conclusions:

  • Deep learning is poised to significantly advance genomics research, particularly in analyzing complex, multi-scale, and multimodal data.
  • The integration of deep learning is crucial for the future of precision medicine.