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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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.
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%...
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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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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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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Single Nucleotide Polymorphisms-SNPs01:05

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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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Updated: Dec 8, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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DeepVariant-on-Spark: Small-Scale Genome Analysis Using a Cloud-Based Computing Framework.

Po-Jung Huang1,2,3, Jui-Huan Chang2, Hou-Hsien Lin4

  • 1Department of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.

Computational and Mathematical Methods in Medicine
|September 21, 2020
PubMed
Summary
This summary is machine-generated.

DeepVariant-on-Spark accelerates genetic variant identification using deep learning and cloud computing. This optimization makes advanced genomic analysis accessible for researchers, enabling faster and scalable whole-genome sequencing studies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying genetic variants from whole-genome sequencing data presents computational challenges for many researchers.
  • GATK is a standard but DeepVariant offers superior accuracy via deep neural networks, though it is computationally intensive.
  • Existing tools often require significant computing resources or bioinformatics expertise, limiting accessibility.

Purpose of the Study:

  • To optimize the DeepVariant pipeline for enhanced computational efficiency and accessibility.
  • To enable scalable and accelerated processing of whole-genome sequencing data for genetic variant identification.
  • To provide a cloud-based solution for researchers to conduct genomic analyses.

Main Methods:

  • Developed DeepVariant-on-Spark, integrating DeepVariant with Apache Spark for optimized resource allocation and multi-GPU support.
  • Deployed DeepVariant-on-Spark on the Google Cloud Platform (GCP) for user accessibility.
  • Provided instructions for rapid deployment and analysis of whole-genome sequencing datasets using GCP free credits.

Main Results:

  • DeepVariant-on-Spark significantly accelerates the DeepVariant pipeline processing.
  • The platform enables efficient resource allocation and multi-GPU utilization for deep learning-based variant calling.
  • Successful deployment on GCP allows users to analyze multiple whole-genome sequencing datasets with provided free credits.

Conclusions:

  • DeepVariant-on-Spark democratizes advanced genetic variant identification by overcoming computational barriers.
  • The cloud-based framework offers a scalable and flexible solution for both small-scale pilot studies and large-scale sequencing projects.
  • This approach enhances the accessibility and efficiency of genomic data analysis for a wider research community.