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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.
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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. 
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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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Maxam-Gilbert Sequencing01:05

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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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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Updated: Oct 27, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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A deep learning model for predicting next-generation sequencing depth from DNA sequence.

Jinny X Zhang1,2, Boyan Yordanov3,4, Alexander Gaunt3

  • 1Department of Bioengineering, Rice University, Houston, TX, USA.

Nature Communications
|July 20, 2021
PubMed
Summary
This summary is machine-generated.

A new deep learning model predicts DNA sequencing depth from probe sequences, improving Next-Generation Sequencing (NGS) efficiency for genomics and DNA data storage. This model enhances accuracy and reduces costs in targeted sequencing applications.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Diagnostics

Background:

  • Targeted high-throughput DNA sequencing is crucial for genomics and diagnostics.
  • Oligonucleotide probe hybridization kinetics cause non-uniform sequencing coverage, increasing costs and reducing sensitivity.

Purpose of the Study:

  • Develop a deep learning model (DLM) to predict Next-Generation Sequencing (NGS) depth from DNA probe sequences.
  • Improve uniformity and efficiency in targeted DNA sequencing applications.

Main Methods:

  • A deep learning model incorporating a bidirectional recurrent neural network was developed.
  • The model utilizes DNA nucleotide identities and predicted unpaired probabilities as input.
  • The DLM was applied to human SNP, lncRNA, and non-human DNA information storage panels.

Main Results:

  • The DLM achieved 93% accuracy for SNP panels and 99% accuracy for non-human panels in cross-validation.
  • Independent testing showed 89% accuracy for the lncRNA panel when trained on the SNP panel.
  • The model also effectively predicted DNA hybridization and strand displacement kinetic rate constants.

Conclusions:

  • The developed deep learning model accurately predicts NGS sequencing depth from DNA probe sequences.
  • This approach has the potential to optimize targeted sequencing, reduce costs, and enhance sensitivity in various applications.
  • The model's effectiveness extends to predicting DNA hybridization kinetics, indicating broad applicability in molecular biology.