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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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Related Experiment Video

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Statistical method to compare massive parallel sequencing pipelines.

M H Elsensohn1,2,3,4, N Leblay5,6,7,8, S Dimassi6,7,9,10

  • 1Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France. mad-helenie.elsensohn@chu-lyon.fr.

BMC Bioinformatics
|March 3, 2017
PubMed
Summary

A new statistical method enables effective comparison of Massive Parallel Sequencing (MPS) pipelines. This approach aids in selecting optimal bioinformatic tools for variant detection, enhancing genetic analysis accuracy.

Keywords:
Massive parallel sequencingNext-generation sequencingPipeline comparisonSensitivitySpecificityStatistical methods

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Massive Parallel Sequencing (MPS) has reduced sequencing time and cost, but Sanger sequencing remains a key validation method.
  • Analyzing MPS data requires robust bioinformatic pipelines, with both academic and commercial options available.
  • Comparing the performance of different MPS analysis pipelines is crucial for accurate variant detection.

Purpose of the Study:

  • To introduce a novel statistical method for comparing Massive Parallel Sequencing (MPS) pipelines.
  • To evaluate and compare an academic (BWA-GATK) and a commercial (TMAP-NextGENe®) pipeline using this statistical method.
  • To assess pipeline performance with and without a gold standard (Sanger sequencing) in epilepsy patient data.

Main Methods:

  • Developed a statistical method using log-linear models to analyze pairwise agreements between MPS pipelines based on variant counts.
  • Employed four log-linear models to assess margin heterogeneity and agreement odds ratios.
  • Utilized a log-linear mixed model to account for biological variability as a random effect.

Main Results:

  • TMAP-NextGENe® identified more variants (2253.49) than BWA-GATK (1857.14) per gene panel.
  • Both pipelines demonstrated similar sensitivity (approx. 63.4%) but significantly different specificities against the gold standard.
  • When considering only single nucleotide variants, both pipelines showed high specificity (approx. 99.98%) and sensitivity (approx. 76.81%).

Conclusions:

  • The developed statistical method is effective for comparing and selecting MPS analysis pipelines.
  • The method is generalizable to various types of MPS data and different bioinformatic pipelines.
  • This approach can improve the reliability and selection of tools for genetic variant analysis.