Advanced Omics

Advanced Omics for Life Sciences

The correct analysis and integration of omics data has become a major component of biomedical research. The advances in technology have allowed for more sophisticated and unbiased approaches to assess the different omics data types. Large collaborative projects combined with databasing efforts have led to invaluable resources like ENCODE [https://www.encodeproject.org/], Expression Atlas [https://www.ebi.ac.uk/gxa/home], the Human Protein Atlas [http://www.proteinatlas.org/] and KEGG [http://www.genome.jp/kegg/]. These resources can provide valuable insights into your omics data and serve as a validation or quality control set when used appropriately. The challenge is to effectively analyse omics data and these large online resources after performing an experiment or getting clinical results.

For example, when analysing tumours derived from a set of patients, the question is: how to correctly analyse your OMICs data and leverage public data by comparing these against your own data. The Cancer Genome Atlas alone numbers over 50,000 files from 3 different OMICs types. What are the correct and feasible strategies to utilise these data?

In this course a scientist (active within the respective OMICs field) starts the morning with a lecture, the accompanying scientific article will be available for prior reading. The presenter will introduce a recent study performed within their group and outline the data mining and data integration opportunities and issues they encountered. The lecture is followed by a discussion on how to conduct this research and possible approaches to expand on the current work or solve one of the encountered issues.  Topics covered will include mutation analysis, expression profiling, protein abundance and metabolic pathways. In the afternoon students will be tasked with finding a solution to a challenge set by the presenter. Solving such problems can only be done through writing (small) computer programs and integrating relevant data sources.

This course is suitable for students who take an interest in informatics and biomedical application of informatics. The course builds on the skills acquired in introduction programming courses; having completed one of these is a hard prerequisite.  Following the “Introduction to Bioinformatics for Molecular Biologists” course is highly recommended.

The goal of this course is to outline current omics analyses methods and the challenges and value of integrating public data in life science research. We will discuss state-of-the-art approaches for tackling these challenges. Students from other disciplines and other universities are invited to attend this course. The topic is suitable for all students in the life sciences dealing with OMICs data.

Literature/study material used:
Lectures, Scientific articles, Course laptop (students can bring their own), Online resources and documentation, Online tutorials, Unix operating system, Online discussion and Q&A platform.

Registration:
Please register online on the CS&D website: www.CSnD.nl/courses. CS&D students have priority in registration until 3 weeks before the start of the course. Thereafter, registration is on ‘first-come-first-serve’ basis until the maximum number of 25 participants is reached.

Advanced Bioinformatics

Advanced Bioinformatics for Life Sciences

Effective mining of data and integrating data is one of the major challenges in biomedical research. Decennia of research have led to an accumulation of databases world-wide, including important resources, such as NCBI, KEGG, ENCODE, SWISS-PROT etc. Lately, new data acquisition technologies, especially next generation sequencing (NGS), are rapidly increasing the amount of information available online, from data published with papers all the way to large scale collaborations, such as The Genome Cancer Atlas (TCGA) involving a wide range of  hospitals and research groups offering information on patients, diagnostics, treatments together with data on sequenced tumors, gene expression, methylation, etc. For an inspiring example see: http://www.cbioportal.org/public-portal/tumormap.do?case_id=TCGA-A2-A0CX&cancer_study_id=brca_tcga_pub.

The challenge is to effectively mine resources, such as the TCGA, after performing an experiment or getting clinical results.  For example, if you are sequencing cancer tumors of patients, the question is: how to mine this public data and compare the results against your own data and results. TCGA alone numbers over 50,000 files, there is no way to mine this data by hand. Likewise we have access to 1,000 public genomes and the genome of the Netherlands (GoNL). What are feasible strategies for using this data?

In this course the morning is started with a lecture by a leading biomedical scientist. The topic can be in cancer research, for example, diagnostics or personalised medicine. The presenter will tell us about his/her research and the short term data mining and data integration issues he or she is facing. The lecture is followed by a discussion on possible approaches in solving one or more of these issues.  Topics covered will include parsing tabular data, SQL databases, web services and the semantic web. The rest of the day the students will be tasked with finding a solution to a particular problem. Solving such problems can only be done through writing (small) computer programs. This course is suitable for students who take an interest in informatics and biomedical application of informatics. The course builds on the skills acquired in introductionary programming courses; having completed one of these is a hard prerequisite.  The introduction to bioinformatics course is not a prerequisite but is highly recommended.

The goal of this course is to outline current data integration challenges in biology and biomedical research and discuss state-of-the-art approaches for tackling these challenges. Students from other disciplines and other universities are invited to attend this course. The topic is suitable for all students in the life sciences dealing with NGS data.

Literature/study material used:
Lectures, Scientific articles, Course laptop (students can bring their own), Online resources and documentation, Online tutorials, Unix operating system, Online discussion and Q&A platform.

Registration:
Please register online on the CS&D website: www.CSnD.nl/courses. CS&D students have priority in registration until 3 weeks before the start of the course. Thereafter, registration is on ‘first-come-first-serve’ basis until the maximum number of 25 participants is reached.

HPC training

A High Performance Compute (HPC) facility is hosted within the UMC Utrecht to facilitate computational research.

Details about the HPC can be found on the wiki: https://wiki.bioinformatics.umcutrecht.nl/HPC

 

We provide basic training on how to make the most out of the HPC infrastructure.

In our monthly course we provide training to researchers on the following topics;

  • Job submission and optimalisation
  • Proper generation and usage of test sets
  • Error logging and fixing
  • Shared software
  • Shared resources