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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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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
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Deep learning: new computational modelling techniques for genomics.

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Deep learning models are revolutionizing genomics by enabling the analysis of vast datasets to predict genetic variation impacts on gene regulation, including DNA accessibility and splicing.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Machine Learning Applications

Background:

  • Genomics relies heavily on machine learning for data analysis and hypothesis generation.
  • Increasing data volumes necessitate more sophisticated machine learning models for deeper insights.

Purpose of the Study:

  • To highlight the transformative potential of deep learning in genomics.
  • To showcase deep learning's application in modeling gene regulatory mechanisms.

Main Methods:

  • Leveraging large-scale genomics datasets.
  • Applying deep learning algorithms, inspired by successes in computer vision and natural language processing.

Main Results:

  • Deep learning models are proving effective for complex genomics tasks.
  • Successful application in predicting the impact of genetic variations on gene regulation.

Conclusions:

  • Deep learning is emerging as a preferred method for genomics modeling.
  • This approach enhances the ability to understand genetic variation's role in DNA accessibility and splicing.