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Related Concept Videos

Next-generation Sequencing03:00

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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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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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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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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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Genetic Variation01:25

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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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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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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Optimized Next-Generation Sequencing Genotype-Haplotype Calling for Genome Variability Analysis.

Javier Navarro1, Bruno Nevado2, Porfidio Hernández1

  • 1Computer Architecture and Operating Systems Department, Universitat Autònoma de Barcelona, Barcelona, Spain.

Evolutionary Bioinformatics Online
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Summary
This summary is machine-generated.

This study presents a new parallel algorithm for accurately estimating nucleotide variability from next-generation sequencing data, especially for nonmodel species. The method improves resource utilization and reduces processing time, aiding population genetics research.

Keywords:
HPCMPIPopulation genomicsSNP callernext-generation sequencingparallelization

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Area of Science:

  • Genomics and Bioinformatics
  • Population Genetics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) technologies generate high error rates, challenging accurate nucleotide variability estimation, particularly in nonmodel species with limited read depth and reference genomes.
  • Existing single-nucleotide polymorphism (SNP) callers prioritize SNP recovery and low false discovery rates, often neglecting variant frequency, which is crucial for precise variability level estimation.

Purpose of the Study:

  • To implement a fast, optimized, and parallel algorithm for unbiased estimation of nucleotide variability from NGS data.
  • To develop a reference-independent method that accounts for variant frequencies, suitable for nonmodel organisms.
  • To improve computational efficiency and resource utilization for population genetics analyses.

Main Methods:

  • Implemented a parallel algorithm incorporating methods from Roesti et al. and Lynch for genotype estimation at each site.
  • The algorithm considers calling single bases, both bases, or no base from the genotype, independent of a reference nucleotide.
  • The pipeline processes BAM files (converted to pileup/mpileup) and outputs FASTA files for downstream analysis.

Main Results:

  • The developed algorithm provides precise estimation of variability levels and patterns by appropriately accounting for variant frequencies.
  • It is reference-independent, mitigating biases associated with reference nucleotide selection.
  • The program significantly reduces running times and optimizes resource usage, enabling analysis on various computing platforms.

Conclusions:

  • The new algorithm offers an efficient and accurate approach for estimating nucleotide variability, particularly beneficial for nonmodel species.
  • Its reference-independent nature and focus on variant frequencies enhance the reliability of population genetics studies.
  • The improved computational performance expands accessibility to advanced population genetics analyses for a broader research community.