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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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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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Identification of Single Nucleotide Polymorphism from Insect Genomic Data.

Alejandro Nabor Lozada-Chávez1, Mariangela Bonizzoni2

  • 1Department of Biology and Biotechnology, University of Pavia, Pavia, Italy. nabor.lozada@gmail.com.

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Summary

This study presents a standardized pipeline for identifying single nucleotide polymorphisms (SNPs) from raw sequencing data. The developed method ensures accurate genome-wide SNP discovery, crucial for genetic variation studies in various species.

Keywords:
GATKGenome-wideInsectsPipelineProtocolSNP identificationVariant calling

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Area of Science:

  • Genomics and Bioinformatics
  • Population Genetics
  • Molecular Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are the most common genetic variations, essential for understanding genomic divergence, population structure, and trait associations.
  • Identifying SNPs, especially in non-model organisms or field samples, presents challenges due to complex processing and tool variability.
  • Raw sequencing data analysis requires standardized protocols to ensure reliable SNP identification.

Purpose of the Study:

  • To present a robust and standardized pipeline for genome-wide single nucleotide polymorphism (SNP) identification from raw Illumina sequencing reads.
  • To provide a reproducible workflow that integrates GATK Best Practices for accurate variant calling.
  • To facilitate SNP discovery in non-model species, exemplified by the arboviral vector mosquito, Aedes aegypti.

Main Methods:

  • Developed a three-step pipeline for processing raw Illumina sequencing data for SNP identification.
  • Incorporated GATK Best Practices, including read quality control, reference genome mapping, alignment recalibration, variant calling, and SNP filtering.
  • Utilized publicly available scripts and datasets for pipeline validation using Aedes aegypti genomic data.

Main Results:

  • Successfully implemented a genome-wide SNP identification pipeline from raw sequencing reads.
  • The pipeline ensures rigorous quality control at multiple stages, from raw reads to final SNP calls.
  • Demonstrated the pipeline's efficacy using Aedes aegypti, a significant arboviral vector.

Conclusions:

  • The presented pipeline offers a standardized and efficient method for accurate SNP discovery in diverse species.
  • This approach addresses the challenges of SNP identification in non-model organisms and field samples.
  • The availability of scripts and datasets promotes reproducibility and broader application in population genetics and vector research.