Automated and reproducible analysis of NGS data

   Deadline for applications: August 27th 2012
   Notification of acceptance dates:
        EARLY: August 15th 2012
        NORMAL: September 3rd 2012
   Course date: September 11th - September 14th 2012


Rutger Vos studied biology at the University of Amsterdam, where he graduated in 2000. He then embarked on his PhD research under professor Arne Mooers at Simon Fraser University in Vancouver, Canada, where he defended his thesis on phyloinformatic problems in 2006. As a self-taught programmer he then became involved in several open-source scientific software development projects while continuing his research career through a postdoctoral fellowship at the University of British Columbia (Vancouver, Canada) and a Marie Curie research fellowship at the University of Reading (Reading, UK). In Spring of 2012 he commenced his employment as the bioinformaticist of the Naturalis Biodiversity Center in a role where he combines novel research with bioinformatics contributions to various research programmes within the organization. In his spare time he also contributes to various open source software projects (TreeBASE, Bio::Phylo, NeXML) and is co-PI of the PhyloTastic project. In addition Rutger has taught bioinformatics workshops in the US, Japan, China, Kenya and once before at GTPB (PHYLOINF09).

Affiliation: Naturalis Biodiversity Center, Leiden, the Netherlands

Darin London studied biology at Texas Tech University in the United States, graduating with a Masters of Science in 1999. He has been supporting biological research with computational support for 12 years, starting at GlaxoSmithKline in 2000, then moving to the European Bioinformatics Institute to work on the Biomart system, and finally coming to the Institute for Genome Sciences and Policy at Duke University, Durham, USA in 2005. He has developed automated analysis pipelines to help researchers analyze over 25Tb of Vertebrate NGS data (GAII and HiSeq), mostly involving research work on the Encyclopedia of DNA Elements (ENCode). Darin has taught numerous workshops on programming and automation with the Perl programming language, including the phyloinformatics workshops at the NESCent, Durham, USA, and the GTPB (PHYLOINF09).

Affiliation: Institute for Genome Sciences and Policy, Duke University Medical Center, Durham (NC), USA.

Course Description


Next generation sequencing (NGS) technologies for DNA have resulted in a yet a bigger deluge of data. Researchers are learning that analysing such data sets is becoming the bottleneck in their work. In many cases, several steps in these analyses are fairly generic (e.g. quality control filtering, alignment to reference sequences, typing) so that off-the-shelf pipelines can be applied. In other cases, novel research approaches require development of new analysis pipelines. Either way, all analysis steps should be repeatable and any changes made to the data (e.g. renaming, annotation, alignment) should be recorded so that the provenance of the results is clear and inferences are reproducible. In this brief workshop we will establish several best practices of reproducibility and provenance recording in the (comparative) analysis of data obtained by NGS. In doing so we will encounter the commonly used technologies that enable these best practices by working through use cases that illustrate the underlying principles. Workflows will be manipulated using graphical platforms (e.g. Galaxy, Taverna, etc.).

Best practices

  • Standardized project organization
  • Projects fully 'runnable' without user intervention
  • No loss of data, metadata or source code through versioning


  • Next generation sequencing platforms
  • File formats
  • Command-line executables, command line scripting and batching
  • High-level programming with domain-specific toolkits
  • Revision control systems
  • Workflow environments (both visual and command line)

Use cases

  • Phylogenetic placement of metagenomic data
  • Typing of pathogens
  • Comparative analysis of multicellular genomic data

Target audience

This course is aimed at researchers who've recently embarked on NGS projects and now, faced with large amounts of data, would like to learn how to automate generic analysis steps and develop new ones in a reproducible (and shareable) way. The course will be illustrated with examples from specific single-celled and multicellular taxa but the concepts are applicable to any species not explicitly referred to in the course. The ideal attendee is a scientist who is not afraid to get his/her hands dirty to acquire the computer-literacy skills for dealing with the informatics side of data analysis.


This course assumes that attendees understand the science behind DNA sequencing and sequence analysis, and will not go deeply into underlying theory (or the mechanics of given algorithms, for example) as such. What will be taught are technical solutions for automating and scaling up such analyses, which will include (but is not limited to) beginner-level programming. Some previous experience with running scripts in any programming language (e.g. R, Perl, Python, batch scripts) will be helpful but not strictly required. General computer literacy, (e.g. editing plain text data files, navigating the command line) will be assumed.

Detailed Program

Instituto Gulbenkian de Ciência,

Apartado 14, 2781-901 Oeiras, Portugal

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Last updated:   July 30th 2012