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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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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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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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A primer on deep learning in genomics.

James Zou1,2,3, Mikael Huss4,5, Abubakar Abid6

  • 1Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA. jamesz@stanford.edu.

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This summary is machine-generated.

Deep learning methods excel at finding complex patterns in large genomic datasets. This primer guides researchers on applying these powerful machine learning techniques for genome analysis, including regulatory genomics and variant calling.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Genomic data analysis presents challenges due to its complexity and scale.
  • Machine learning, particularly deep learning, offers advanced pattern recognition capabilities.
  • There is a growing need for accessible resources on deep learning in genomics.

Purpose of the Study:

  • To provide a comprehensive primer on deep learning applications in genome analysis.
  • To highlight successful deep learning use cases in genomics.
  • To offer practical guidance and resources for researchers.

Main Methods:

  • Review of deep learning methodologies applicable to genomic data.
  • Discussion of established applications in regulatory genomics, variant calling, and pathogenicity scoring.
  • Compilation of practical tools, resources, and guidance for implementation.

Main Results:

  • Demonstration of deep learning's efficacy in identifying complex patterns within large genomic datasets.
  • Successful applications detailed in regulatory genomics, variant calling, and pathogenicity prediction.
  • A practical guide and interactive tutorial are provided for user engagement.

Conclusions:

  • Deep learning methods are highly effective for complex genome analysis.
  • This primer serves as a valuable resource for researchers entering the field.
  • Accessible tools and guidance facilitate the adoption of deep learning in genomics research.