Variant Analysis

Genetic differences (variants) between healthy and diseased tissue, between individuals of a population, or between strains of an organism can provide mechanistic insight into disease processes and the natural function of affected genes.

The tutorials in this section show how to detect evidence for genetic variants in next-generation sequencing data, a process termed variant calling.

Of equal importance, they also demonstrate how you can interpret, for a range of different organisms, the resulting sets of variants by predicting their molecular effects on genes and proteins, by annotating previously observed variants with published knowledge, and by trying to link phenotypes of the sequenced samples to their variant genotypes.

Requirements

Before diving into this topic, we recommend you to have a look at:

• Introduction to Galaxy Analyses
• slides Slides: Quality Control
• tutorial Hands-on: Quality Control
• slides Slides: Mapping
• tutorial Hands-on: Mapping

Material

Introduction

Concepts of variant and genotype calling

Model Organisms

Variant calling for studying genomics of model organisms

One Health

Variant calling for monitoring and preventing health threats

Human Genetics and Cancer

Variant calling pipelines for studying human genetic diseases and cancer

Data Management

Variant calling pipelines for interacting with human genetic databases

Frequently Asked Questions

Common questions regarding this topic have been collected on a dedicated FAQ page . Common questions related to specific tutorials can be accessed from the tutorials themselves.

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Community Resources

Editorial Board

This material is reviewed by our Editorial Board:

Bérénice Batut

Wolfgang Maier

Björn Grüning

Anton Nekrutenko

Tristan Reynolds

Are you on the editorial board or want to help out with this topic? View the topic maintainer page for ways to improve this topic!

Contributors

This material was contributed to by:

Björn Grüning

Jasper Ouwerkerk

Krzysztof Poterlowicz

Jörg Wennmann

David Salgado

Anika Erxleben

Torsten Houwaart

Wolfgang Maier

Martin Čech

Ekaterina Polkh

Gildas Le Corguillé

Niall Beard

Armin Dadras

Cristóbal Gallardo

Saskia Hiltemann

Anton Nekrutenko

Dave Clements

Peter van Heusden

Yvan Le Bras

Anna Syme

Katarzyna Kamieniecka

Khaled Jum'ah

Anthony Bretaudeau

Nicola Soranzo

Helena Rasche

Torsten Seemann

Teresa Müller

Jennifer Hillman-Jackson

Verena Moosmann

Daniel Blankenberg

Beatriz Serrano-Solano

Mélanie Petera

Tristan Reynolds

Bérénice Batut

Simon Gladman

Thoba Lose

Nick Stoler

Maria Doyle

Alex Ostrovsky

Tomas Klingström

Marius van den Beek

Linelle Abueg

Funding

These individuals or organisations provided funding support for the development of this resource

UFR

ELIXIR Europe

ELIXIR-CONVERGE

ELIXIR CONVERGE is connecting and align ELIXIR Nodes to deliver sustainable FAIR life-science data management services. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement № 871075

BeYond-COVID

BY-COVID is an EC funded project that tackles the data challenges that can hinder effective pandemic response. This project has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement № 101046203 (BY-COVID)

Melbourne Bioinformatics

DEFEND

DEFEND is Addressing the dual emerging threats of African Swine Fever and Lumpy Skin Disease in Europe. This work has received funding from the DEFEND project (www.defend2020.eu) with funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 773701.

de.NBI

Australian BioCommons

The University of Melbourne

References

• Umadevi Paila, Brad A. Chapman, Rory Kirchner, Aaron R. Quinlan: GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations
  GEMINI query is the most versatile of all the GEMINI tools. You can use it to ask questions in simple SQL
• Rasmus Nielsen, Joshua S. Paul, Anders Albrechtsen & Yun S. Song: Genotype and SNP calling from next-generation sequencing data
  Meaningful analysis of next-generation sequencing (NGS) data, which are produced extensively by genetics and genomics studies, relies crucially on the accurate calling of SNPs and genotypes.
• Erik Garrison and Gabor Marth: Haplotype-based variant detection from short-read sequencing
  The direct detection of haplotypes from short-read DNA sequencing data requires changes to existing small-variant detection methods, such as Bayesian statistical framework.
• Aaron R. Quinlan: Introduction to GEMINI
  GEMINI query is the most versatile of all the GEMINI tools. You can use it to ask questions in simple SQL
• Torsten Seemann: Snippy: Rapid bacterial SNP calling and core genome alignments
  Snippy finds SNPs between a haploid reference genome and your NGS sequence reads. It will find both substitutions (snps) and insertions/deletions (indels).
• Wolfgang Maier: MiModD Documentation
  MiModD is a comprehensive software package for mapping-by-sequencing analyses
• Korbinian Schneeberger: Using next-generation sequencing to isolate mutant genes from forward genetic screens
  Mapping the location of causal mutations using genetic crosses has traditionally been a complex procedure, but next-generation sequencing now allows the rapid identification of causal mutations even in complex genetic backgrounds