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Related Concept Videos

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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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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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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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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DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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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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Related Experiment Video

Updated: Jun 7, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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StableLift: Optimized Germline and Somatic Variant Detection Across Genome Builds.

Nicholas K Wang1,2,3, Nicholas Wiltsie1,2,3, Helena K Winata1,2,3

  • 1Department of Human Genetics, University of California, Los Angeles.

Biorxiv : the Preprint Server for Biology
|November 18, 2024
PubMed
Summary
This summary is machine-generated.

Genome build differences significantly impact variant calling accuracy, affecting up to 49.6% of somatic structural variants. A new algorithm, StableLift, can predict and mitigate these inter-build artifacts in genomic analyses.

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Last Updated: Jun 7, 2025

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Reference genomes are essential for genomic analysis but evolve, leading to new builds with incompatible coordinate systems.
  • This evolution necessitates understanding the impact of genome build changes on variant calling.

Purpose of the Study:

  • To quantify the effect of different human genome builds on germline and somatic variant calling.
  • To develop a method for predicting the stability of variants across genome builds.

Main Methods:

  • Analysis of tumor-normal whole-genome pairs against two major human genome builds.
  • Development and validation of the StableLift algorithm to predict cross-build variant stability.

Main Results:

  • Significant build-discordance observed: 3.8% for germline SNPs, 8.6% for germline SVs, 25.9% for somatic SNVs, and 49.6% for somatic SVs.
  • 47% of build-discordant variants were validated by targeted resequencing, indicating they are not solely false positives.
  • StableLift achieved high predictive performance (AUROC 0.934 ± 0.029) for cross-build variant stability.

Conclusions:

  • Genome build differences introduce substantial artifacts in variant calling, requiring caution in cross-build analyses.
  • StableLift offers an efficient computational solution to mitigate inter-build artifacts and improve genomic data reliability.