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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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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.
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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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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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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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,...
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Related Experiment Video

Updated: Jun 28, 2025

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TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples.

Shenjie Wang1,2, Xiaoyan Zhu1,2, Xuwen Wang1,2

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.

Briefings in Bioinformatics
|April 18, 2024
PubMed
Summary

TMBstable offers a novel approach to cancer variant calling, improving accuracy for biomarkers like tumor mutation burden (TMB). This method ensures stable performance across diverse samples, crucial for immunotherapy patient selection.

Keywords:
counting-based biomarkerimmunotherapymeta-learning approachsequencing data analysistumor mutation burdenvariant calling

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Traditional variant calling accuracy metrics are insufficient for biomarkers such as tumor mutation burden (TMB).
  • TMB variability across samples impacts immunotherapy patient selection and threshold setting.
  • Existing methods often use uniform strategies, leading to mismatches and performance fluctuations.

Purpose of the Study:

  • Introduce TMBstable, a meta-learning framework for dynamic, region-specific variant calling strategy selection.
  • Address strategy-sample mismatches to enhance the stability and consistency of TMB analysis.
  • Improve biomarker evaluation for immunotherapy patient stratification.

Main Methods:

  • Sample segmentation into windows and meta-feature extraction for clustering.
  • Application of a pre-trained meta-model to select optimal variant calling algorithms per cluster.
  • Evaluation using simulated and real non-small cell lung cancer (NSCLC) and nasopharyngeal carcinoma (NPC) samples.

Main Results:

  • TMBstable demonstrated superior stability compared to advanced callers across 300 simulated and 106 real tumor samples.
  • Achieved the lowest variance and coefficient of variation in false positive/negative rates, precision, and recall.
  • Validated effectiveness in analyzing counting-based biomarkers like TMB.

Conclusions:

  • TMBstable provides a more stable and reliable method for variant calling in cancer genomics.
  • The meta-learning approach effectively optimizes strategies for diverse genomic regions and samples.
  • This enhances the utility of TMB as a biomarker for immunotherapy response prediction.