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Updated: Jun 28, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Population genetic inference from resequencing data.

Rong Jiang¹, Simon Tavaré, Paul Marjoram

¹Department of Oncology, University of Cambridge, Cambridge CB2 0RE, UK.

|November 6, 2008

Summary

This study introduces statistical models for shotgun resequencing data, enabling population genetic inference and evolutionary parameter estimation from new sequencing technologies. Methods are robust to errors and can be used without a reference genome.

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Pyrosequencing: A Simple Method for Accurate Genotyping

Pyrosequencing: A Simple Method for Accurate Genotyping

Published on: January 8, 2008

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Pyrosequencing: A Simple Method for Accurate Genotyping

Pyrosequencing: A Simple Method for Accurate Genotyping

Published on: January 8, 2008

Area of Science:

Genomics
Population Genetics
Bioinformatics

Background:

Shotgun resequencing technologies like Solexa, 454, and polony are increasingly used.
These technologies generate vast amounts of data for genetic studies.

Purpose of the Study:

To develop statistical models for analyzing shotgun resequencing data.
To enable population genetic inference and estimate evolutionary parameters using this data.
To address limitations of current sequencing data, such as incomplete or unaligned sequences.

Main Methods:

Statistical modeling of sequencing-by-synthesis data.
Estimation of evolutionary parameters (mutation and recombination rates).
Development of methods robust to sequencing errors and absence of a reference genome.

Main Results:

Demonstrated the utility of shotgun resequencing data for estimating evolutionary parameters.
Presented refined methods that improve robustness to sequencing errors.
Developed an approach applicable even without a reference genome.

Conclusions:

Statistical modeling provides a powerful framework for population genetic inference from shotgun resequencing data.
The developed methods enhance the utility of next-generation sequencing data for evolutionary studies.
These advancements facilitate broader applications of population genetics in research.