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Related Concept Videos

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Published on: August 3, 2018

DnaSAM: Software to perform neutrality testing for large datasets with complex null models.

Andrew J Eckert1, John D Liechty, Brandon R Tearse

  • 1Section of Evolution and Ecology, University of California at Davis, One Shields Avenue, Davis, CA 95616, USA Center for Population Biology Department of Plant Sciences, University of California at Davis, One Shields Avenue, Davis, CA 95616, USA.

Molecular Ecology Resources
|May 14, 2011
PubMed
Summary
This summary is machine-generated.

DnaSAM is a new program for analyzing DNA sequence diversity and neutrality statistics. It enables high-throughput processing of genetic data and simulations for population genetics research.

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Published on: July 3, 2016

Area of Science:

  • Population genetics
  • Molecular evolution
  • Bioinformatics

Background:

  • DNA sequence polymorphisms reveal population demography and adaptation.
  • High-throughput DNA sequencing is now advanced, but data analysis remains a bottleneck.
  • Existing molecular population genetics software often lacks high-throughput capabilities and simulation flexibility.

Purpose of the Study:

  • To introduce DnaSAM, a novel program for high-throughput analysis of DNA sequence diversity and neutrality statistics.
  • To provide a tool that integrates data analysis with Monte Carlo coalescent simulations for complex demographic scenarios.
  • To address the current computational limitations in population genetics research.

Main Methods:

  • DnaSAM processes multiple sequence alignments for high-throughput estimation of diversity and neutrality statistics.
  • Monte Carlo coalescent simulations are performed using the ms program.
  • The software incorporates genetic parameters like recombination and demographic scenarios such as population bottlenecks.

Main Results:

  • DnaSAM facilitates high-throughput estimation of DNA sequence diversity and neutrality statistics.
  • The program allows for testing statistics against user-specified null models using simulations.
  • Output includes diversity and neutrality statistics with associated probability values in accessible text files.

Conclusions:

  • DnaSAM offers a solution to the computational bottleneck in population genetics data analysis.
  • The program enhances the ability to infer demographic processes and adaptation from DNA sequence data.
  • DnaSAM provides a flexible and efficient tool for molecular population geneticists.