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Course module: BMB508117
BMB508117
Bioinformatics in Neuroscience: a web-based approach
Course info
Course codeBMB508117
EC3
Course goals
After the course the student:
  1. can relate (epi)genetic variations to brain diseases and traits;
  2. knows how to run a GWAS;
  3. can determine the relation between neuronal genes (gene networks and pathways);
  4. has knowledge of the transcriptome and how to utilize it
  5. knows the difference and strengths Mendelian versus complex genetic analyses
  6. has basic knowledge of programming in bioinformatics (R, UNIX).
Content
Period (from – till): 20 November - 1 December 2023 (BMS_P2_A).

Course coordinator: Prof. dr. Roger A.H. Adan and dr. K. van Eijk.

Faculty
Prof. dr. R.A.H. Adan
Dr. K. van Eijk
Prof. dr. J. H. Veldink

Dr. K. Kenna
Dr. G. van Haaften
Dr. O. Basak

Dr. K. Siletti
Dr. J. Flores
Dr. M. Bakker

Course description
This course will give you an insight into applying bioinformatics in neuroscience using different layers of ‘omics data. It’s aimed to give you a broad view of different techniques and different kinds of data with accompanying challenges that are being faced. Every day, lectures are followed by hands-on computer practicals on your own laptop.
In addition, interesting keynote lectures will be given by experts from the field.
Assessment for this course is based on an assignment that you will work on in groups, followed by a final presentation.

General learning objectives
  • To be familiar with big data resources relevant to Neuroscience and how to access them
  • To familiarize yourself how bioinformatics contributes to design (wet lab) experiments in neuroscience
  • To learn some basic programming language and read scripts
  • To gain experience with state-of-the art pipelines for bioinformatics data analysis
  • To identify real world scientific questions that can be answered through computational analyses of “omics” technologies
  • To understand why cleaning data is important
  • To differentiate strengths, limitations and computational methods associated with the most popular types of omics (SNP arrays, DNA sequencing, RNAseq)
  • To give examples of additive scientific gains that can be achieved by combining different bioinformatic workflows
Learning objectives per subject
Adan: Introduction to webtools and Allen Brain Atlas
  1. Be familiar in using genome browsers
  2. Understand how information from different sources is linked to the genome and how it can be read
  3. Understand how spatial information on gene expression is stored in the Allen Brain Atlas
  4. Know how gene sets are extracted from the Allen Brain Atlas
Flores: Introduction to R
  1. Understand and apply the basics of the R syntax
  2. Able to work with Rstudio
  3. Know about different data types and structures
Van Haaften:
  1. Have a broad grasp of the prevalence and impact of orphan diseases (monogenic diseases) 
  2. Know how to use databases relevant for orphan disease diagnosis and research (OMIM, ClinVar and GnomAD) and apply the GnomAD constraint values in relation to different inheritance models of rare diseases. 
  3. Understand the principles of next generation sequencing and be able to detail the key differences between short and long-range sequencing technologies. 
  4. Have basic skills in visualizing next generation sequencing data in IGV for WES/WGS and RNAseq. 
Van Eijk: GWAS
  1. Understand and explain the concept of genome-wide association studies (GWAS), using terms as SNPs, linkage disequilibrium, allele frequency
  2. Describe why applying quality control (QC) to data is important
  3. Have knowledge of the different QC steps (both sample and SNP)
  4. Explain the method for conducting a GWAS
  5. Explain what a principle component analysis (PCA) is
  6. Interpret a QQ-plot and Manhattan plot
  7. Understand and explain the association between statistical power and effect size in a GWAS
  8. Indicate non-genetic factors and their effect on GWAS results (confounding)
Bakker: post-GWAS analyses 
  1. Know what GWAS summary statistics are and how they relate to a trait 
  2. Have a broad idea of what LD-score regression is and what it can be used for 
  3. Understand what partitioned heritability is and how it can help understand a disease 
  4. Understand what Mendelian randomization is and how it can help understand the relationship between a disease and its risk factors 
  5. Conduct basic post-GWAS analysis (LD-score regression and Mendelian randomization) 
Basak: scRNAseq
  1. Know the state-of-the-art and major computational hurdles in the single cell genomic field
  2. Be able to discuss the reasoning behind individual steps of the scRNAseq data analysis
  3. Gain the capacity to run a standard scRNAseq data analysis pipeline independently
  4. Have the ability to interpret the results of single cell data analysis
Kenna: Rare variant association testing
  1. Explain the rationale and methodological approaches behind rare variant association testing
  2. Contrast the strengths and limitations of rare variant association testing with the methods used in GWAS and canonical monogenic disorders
  3. Conduct rare variant association testing analyses of 10,507 ALS patients and 26,040 healthy controls and critically evaluate/interpret your results
Kenna: Regulatory genomics
  1. Detail the key differences between promotors, enhancers, transcription factor binding sites, eQTL, DNA accessibility, bulk tissue analyses, single cell analyses, ATACseq and ChIPseq
  2. Recognize biological questions that can be answered by studying genome regulation across tissues and cell types
  3. Conduct basic analyses of DNA accessibility and eQTL in human brain and critically evaluate / interpret your results
Literature/study material used
Course Modules (all web-based)
  • Expression
  • Networks
  • Genetics
Registration
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Students' site. Maximum participants 20.

Mandatory for students in Master’s programme
No.
 
Optional for students in other GSLS Master’s programme:
Yes.

Prerequisite knowledge:
Biomedical Sciences/Biology
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