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Related Concept Videos

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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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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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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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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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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Sanger Sequencing01:57

Sanger Sequencing

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Updated: Nov 16, 2025

Collection and Extraction of Saliva DNA for Next Generation Sequencing
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Deep Learning Applied on Next Generation Sequencing Data Analysis.

Artem Danilevsky1, Noam Shomron2

  • 1Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

Methods in Molecular Biology (Clifton, N.J.)
|February 19, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning, a powerful AI technique, shows promise in genomic research. Our new platform accurately classifies cancer patients using gut microbiome data, outperforming existing methods.

Keywords:
Cancer detectionComputational techniquesDeep learningGenomic research

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Area of Science:

  • Genomics
  • Computational Biology
  • Artificial Intelligence

Background:

  • Deep learning offers advanced computational techniques for extracting insights from large datasets.
  • Genomic research generates vast amounts of data, presenting opportunities for deep learning applications.
  • Traditional methods in genomics often face limitations with complex, large-scale datasets.

Purpose of the Study:

  • To introduce a novel platform integrating deep learning with genomic sequencing data.
  • To evaluate the efficacy of this platform in analyzing complex biological datasets.
  • To demonstrate the potential of deep learning in advancing genomic research.

Main Methods:

  • Development of a computational platform combining deep learning algorithms and genomic sequencing data analysis.
  • Application of the platform to analyze publicly available gut microbiome sequencing data.
  • Comparative analysis of the platform's classification accuracy against traditional methods.

Main Results:

  • The platform demonstrated high accuracy in classifying cancer patients based on gut microbiome data.
  • Performance exceeded that of several other established analytical methods.
  • Identified specific patterns within genomic data relevant to patient classification.

Conclusions:

  • Deep learning presents a significant frontier for genomic research, offering powerful analytical capabilities.
  • The developed platform serves as a foundational tool for future deep learning-driven genomic studies.
  • Further exploration of deep learning in genomics holds potential for groundbreaking discoveries.