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Related Concept Videos

Genomics02:02

Genomics

40.7K
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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Genomic Imprinting and Inheritance02:30

Genomic Imprinting and Inheritance

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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.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...
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Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

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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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Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

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Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes02:16

Comparing Mitochondrial, Chloroplast, and Prokaryotic Genomes

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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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Genomic DNA in Prokaryotes00:46

Genomic DNA in Prokaryotes

48.6K
The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
Genomic Diversity in Bacteria
Although bacterial genomes are much...
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Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Ultra-long Read Sequencing for Whole Genomic DNA Analysis

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Evaluating Clinical Genome Sequence Analysis by Watson for Genomics.

Kota Itahashi1, Shunsuke Kondo1,2, Takashi Kubo3

  • 1Department of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, Japan.

Frontiers in Medicine
|November 27, 2018
PubMed
Summary
This summary is machine-generated.

Watson for Genomics (WfG) accurately analyzed clinical genome sequencing results, identifying gene mutations, amplifications, and fusions. This artificial intelligence tool shows potential for clinical practice in guiding targeted cancer therapies.

Keywords:
artificial intelligenceclinical genome sequencinggenome sequencing interpretationprecision medicinewatson for genomics

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Oncologists increasingly use clinical genome sequencing for targeted therapies.
  • Assessing the validity and utility of artificial intelligence (AI) tools like Watson for Genomics (WfG) is crucial for analyzing sequencing data.
  • This study evaluates WfG's performance in analyzing clinical sequencing results from solid tumors.

Purpose of the Study:

  • To assess the concordance between WfG analysis and expert multidisciplinary specialist evaluation of clinical genome sequencing data.
  • To determine the utility of WfG in identifying actionable gene alterations for targeted therapy in cancer patients.
  • To evaluate the potential of WfG for integration into clinical practice.

Main Methods:

  • Retrospective analysis of genome sequencing data from 198 patients with solid tumors treated between April 2013 and October 2016.
  • Reanalysis of tumor sequencing results (mutations, amplifications, fusions) by WfG and comparison with in-house specialist evaluations.
  • Assessment of concordance rates for pathogenic classifications and identification of targeted therapy options.

Main Results:

  • WfG achieved high concordance with expert evaluations: 89.8% for mutations, 97.5% for amplifications, and 77.3% for fusions.
  • A later WfG version improved mutation concordance to 94.5%.
  • WfG identified 84.6% of expert-proposed targeted therapies and suggested 225 additional options for 249 pathogenic alterations.

Conclusions:

  • Watson for Genomics demonstrates significant potential for analyzing clinical genome sequencing data in oncology.
  • The AI tool effectively identifies gene alterations and potential targeted therapies, supporting clinical decision-making.
  • Further training and validation in clinical trial settings are recommended for optimal WfG application.