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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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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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Deep Omics.

Ngoc Hieu Tran1, Xianglilan Zhang1,2, Ming Li1

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Deep learning is transforming bioinformatics by shifting research from algorithms to data. This approach is revolutionizing fields like genomics and proteomics through innovative applications.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics
  • Proteomics

Background:

  • Deep learning (DL) has significantly advanced various AI fields, including image processing and natural language processing.
  • Its potential impact on biological sciences, particularly genomics and proteomics, is increasingly recognized.

Purpose of the Study:

  • To demonstrate the transformative effect of deep learning on bioinformatics research.
  • To illustrate the shift from algorithm-centric to data-centric methodologies in biological data analysis.

Main Methods:

  • Application of deep learning models to genomics data.
  • Utilizing deep learning for protein structure prediction.
  • Employing deep learning techniques in proteomics research.

Main Results:

  • Deep learning models show significant promise in analyzing complex genomic datasets.
  • Accurate protein structure prediction has been achieved using deep learning approaches.
  • Advancements in proteomics research are driven by data-centric deep learning strategies.

Conclusions:

  • Deep learning is fundamentally changing the landscape of bioinformatics research.
  • The shift towards data-centric approaches powered by deep learning is crucial for future biological discoveries.