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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Point and Frameshift Mutations01:30

Point and Frameshift Mutations

Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
Sanger Sequencing01:57

Sanger Sequencing

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: Jun 6, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

A transformer based deep learning framework for accurate single nucleotide variant correction in heterogeneous

Xiaonan Wang1,2,3, Shenjie Wang1,2,4, Zhili Chang1,2,3

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Frontiers in Microbiology
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

We developed a deep learning framework to accurately quantify host single nucleotide variants (SNVs) in mixed samples. This method overcomes low DNA purity issues, improving genetic analysis in complex environments.

Keywords:
genomic profilingheterogeneous sampleshost-microbe symbiosissingle nucleotide variant correctiontransformer architecture

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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Host genetic profiling in mixed samples is vital for understanding interactions.
  • Low host DNA purity in sequencing data causes significant biases in variant quantification.

Purpose of the Study:

  • To develop a computational framework to accurately quantify host single nucleotide variants (SNVs) in low-purity samples.
  • To address systematic biases caused by variable host DNA fractions in sequencing data.

Main Methods:

  • Developed a Transformer-based computational framework utilizing group-encoding for multidimensional features.
  • Incorporated variant allele frequency (VAF) distributions, purity estimates, sequencing depth, and genomic context.
  • Modeled sequence context and technical artifacts to neutralize purity-induced biases.

Main Results:

  • Significantly reduced SNV quantification errors across a purity gradient (0.2-1.0).
  • Achieved high concordance between corrected and ground-truth SNV counts.
  • Demonstrated substantial performance gains over conventional callers, especially in ultra-low purity conditions (0.2-0.3).

Conclusions:

  • The deep learning pipeline offers a robust solution for accurate host SNV quantification in complex biological mixtures.
  • Enables reliable downstream genetic analyses in heterogeneous microenvironments.
  • Overcomes limitations of traditional methods in low-purity samples.