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.
You can view the tutorial materials in different languages by clicking the dropdown icon next to the slides (slides) and tutorial (tutorial) buttons below.Requirements
Before diving into this topic, we recommend you to have a look at:
- Introduction to Galaxy Analyses
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Sequence analysis
- Quality Control: slides slides - tutorial hands-on
- Mapping: slides slides - tutorial hands-on
Material
Galaxy instances
You can use a public Galaxy instance which has been tested for the availability of the used tools. They are listed along with the tutorials above.
You can also use the following Docker image for these tutorials:
docker run -p 8080:80 quay.io/galaxy/variant-analysis-training
NOTE: Use the -d flag at the end of the command if you want to automatically download all the data-libraries into the container.
It will launch a flavored Galaxy instance available on http://localhost:8080. This instance will contain all the tools and workflows to follow the tutorials in this topic. Login as admin with password password to access everything.
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.Maintainers
This material is maintained by:
Bérénice Batut Wolfgang Maier Björn Grüning Anton NekrutenkoFor any question related to this topic and the content, you can contact them or visit our Gitter channel.
Contributors
This material was contributed to by:
Bérénice Batut Yvan Le Bras Anton Nekrutenko Nicola Soranzo Alex Ostrovsky Nick Stoler Wolfgang Maier Torsten Houwaart Anika Erxleben Björn Grüning Marius van den Beek Dave Clements Daniel Blankenberg Peter van Heusden Simon Gladman Thoba Lose Anna Syme Torsten Seemann Khaled Jum'ah Katarzyna Murat David Salgado Krzysztof Poterlowicz Jasper Ouwerkerk Helena Rasche Saskia HiltemannReferences
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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