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Related Concept Videos

RNA-seq03:21

RNA-seq

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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Comparing Copy Number Variations and SNPs02:26

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Related Experiment Video

Updated: May 19, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Published on: June 23, 2012

A beginners guide to SNP calling from high-throughput DNA-sequencing data.

André Altmann1, Peter Weber, Daniel Bader

  • 1Statistical Genetics, Max Planck Institute of Psychiatry, Kraepelinstrasse 2-10, 80804 Munich, Germany. altmann@stanford.edu

Human Genetics
|August 14, 2012
PubMed
Summary

High-throughput DNA sequencing (HTS) enables genetic variant identification. This study reviews essential pipeline steps for calling single nucleotide polymorphisms (SNPs) from HTS data, emphasizing tool choice impacts results.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • High-throughput DNA sequencing (HTS) is crucial in life sciences for genome and exome analysis.
  • Identifying genetic variants like single nucleotide polymorphisms (SNPs) is a key application.

Purpose of the Study:

  • To review the essential components of a computational pipeline for calling SNPs from raw HTS data.
  • To analyze the impact of different bioinformatics tools on SNP calling accuracy.

Main Methods:

  • The study outlines a pipeline including quality control, read mapping, alignment post-processing (e.g., base quality recalibration), and SNP calling.
  • A publicly available whole-exome sequencing dataset was analyzed using various alignment and SNP calling tools.

Main Results:

  • The selection of specific alignment programs and SNP calling algorithms significantly influences the final SNP identification results.
  • The pipeline's steps, from quality control to filtering, are critical for accurate variant detection.

Conclusions:

  • A well-defined pipeline is essential for reliable SNP calling from HTS data.
  • Researchers must carefully consider tool selection to optimize genetic variant discovery.