Sequence Analysis

Analyses of sequences

Requirements

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

• Introduction to Galaxy Analyses

Material

The Basics

These tutorials cover basic operations common to many sequence-bases analyses

Other

Assorted Tutorials

Learning Pathways

Introduction to Galaxy and Sequence analysis

This learning path aims to teach you the basics of Galaxy and analysis of sequencing data. You will learn how to use Galaxy for analysis, and will be guided through the most common first steps of any genome analysis; quality control and a mapping or assembly of your genomic sequences.

beginner introduction sequence-analysis assembly

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:

Yvan Le Bras

Bérénice Batut

Joachim Wolff

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:

Mateo Boudet

Wolfgang Maier

Katherine Cox

Matthias Bernt

Helena Rasche

Anup Kumar

Tristan Reynolds

Alexandre Cormier

William Durand

Maria Doyle

Mehmet Tekman

Saskia Hiltemann

Anika Erxleben

Anne Fouilloux

Simon Bray

Natalie Kucher

Björn Grüning

Erwan Corre

Niall Beard

Robert Meller

Teresa Müller

Elizabeth Humphries

Elischa Berger

Stéphanie Robin

Cameron Hyde

Candace Savonen

Nicola Soranzo

Frederick Tan

Swathi Nataraj

Coline Royaux

Clea Siguret

Verena Moosmann

Cristóbal Gallardo

Ava Hoffman

Deepti Varshney

Eric Tvedte

Anthony Bretaudeau

Ekaterina Polkh

Joachim Wolff

Laura Leroi

Bérénice Batut

Funding

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

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)

ABRomics

MAdLand

Melbourne Bioinformatics

NHGRI ANVIL

Australian BioCommons

NIH

DataPLANT

The University of Melbourne

NHGRI GDS Community Network

Gallantries

This project (2020-1-NL01-KA203-064717) is funded with the support of the Erasmus+ programme of the European Union. Their funding has supported a large number of tutorials within the GTN across a wide array of topics.

References

• SciLifeLab: QCFAIL
  Articles about common next-generation sequencing problems
• Marco-Antonio Mendoza-Parra et al: A quality control system for profiles obtained by ChIP sequencing
  Presents an approach that associates global and local QC indicators to ChIP-seq data sets as well as to a variety of enrichment-based studies by NGS.
• Ana Conesa et al: A survey of best practices for RNA-seq data analysis
  Highlights the challenges associated with each step of RNA-seq data analysis.
• Yuval Benjamini et al: Summarizing and correcting the GC content bias in high-throughput sequencing
  Summarizes the many possible sourced of GC bias for deeply sequenced samples.
• David Sims et al: Sequencing depth and coverage: key considerations in genomic analyses
  Discuss the issue of sequencing depth in the design of next-generation sequencing experiments.
• Frontiers in Genetics: Quality assessment and control of high-throughput sequencing data
  Collection of papers on quality controls for various NGS applications
• Frazer Meacham et al: Identification and correction of systematic error in high-throughput sequence data
  Reviews and proposes correction for systematic base pair errors in deep sequencing
• Guillaume Devailly et al: Heat seq: an interactive web tool for high-throughput sequencing experiment comparison with public data
  Presents a web-tool that allows genome scale comparison of high throughput experiments
• Amores, A., et al. 2011: Genome evolution and meiotic maps by massively parallel DNA sequencing
  
• Catchen, J. M. et al. 2011: Stacks: building and genotyping loci de novo from short-read sequences
  
• Davey, J. W., et al. 2011: Genome-wide genetic marker discovery and genotyping using next-generation sequencing
  
• Etter, P. D., et al. 2011: SNP Discovery and Genotyping for Evolutionary Genetics using RAD sequencing
  
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• Lynch, M. 2009: Estimation of allele frequencies from high-coverage genome-sequencing projects
  
• Nielsen, R., et al. 2005: Genomic scans for selective sweeps using SNP data
  
• Hohenlohe, P. A., et al. 2010: Using population genomics to detect selection in natural populations: Key concepts and methodological considerations
  
• Stapley, J., et al. 2010: Adaptation genomics: the next generation
  
• Altshuler, D., et al. 2000: An SNP map of the human genome generated by reduced representation shotgun sequencing
  
• Baxter, S. W., et al. 2011: Linkage mapping and comparative genomics using next-generation RAD sequencing of a non-model organism
  
• Chutimanitsakun, Y., et al. 2011: Construction and application for QTL analysis of a Restriction Site Associated DNA (RAD) linkage map in barley
  
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• Hohenlohe, P. A., et al. 2011: Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout
  
• Miller, M. R., et al. 2007: Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers
  
• Willing, E. M., et al. 2011: Paired-end RAD-seq for de novo assembly and marker design without available reference
  
• Andolfatto, P., et al. 2011: Multiplexed shotgun genotyping for rapid and efficient genetic mapping
  
• Elshire, R. J., et al. 2011: A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species
  
• Rigola, D., et al. 2009: High-Throughput Detection of Induced Mutations and Natural Variation Using KeyPoint™ Technology
  
• van Orsouw, N. J., et al. 2007: Complexity reduction of polymorphic sequences (CRoPS): a novel approach for large-scale polymorphism discovery in complex genomes
  
• van Tassell, C. P., et al. 2008: SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries
  
• Luikart, G.,et al. 2003: The power and promise of population genomics: from genotyping to genome typing
  
• Nielsen, R., et al. 2011: Genotype and SNP calling from next-generation sequencing data