LIST of Trainings

Where

• University of Limpopo Tuesday, 31 May 
• University of the Witwatersrand Thursday, 2 June
• University of Pretoria Friday, 3 June
• University of the Western Cape Monday, 6 June
• University of Cape Town Tuesday, 7 June

What

The Ensembl project www.ensembl.org provides a comprehensive and integrated source of annotation of, mainly vertebrate, genome sequences. This 1-day workshop offers participants the possibility of gaining hands-on experience in the use of the Ensembl genome browser but also provides them with the necessary background information. Our sister project at www.ensemblgenomes.org can also be covered if participants are working with bacteria, plants, fungi, protists or (invertebrate) metazoa.

The workshop is primarily targeted at wetlab researchers, and we customise the course to include beginners through to frequent users. The following modules will be covered in the morning session:

• Introduction to Ensembl: origin, goals and organization of the Ensembl project
• Genes and Transcripts: guided tour of the most important pages of the Ensembl website
• Data retrieval with BioMart: retrieving complex / large datasets using the data retrieval tool BioMart

In the afternoon, two or three of the following modules will be covered, depending on the interest of the participants. To decide which modules will be covered, a pre-workshop survey will be sent out to the participants well in advance of the workshop.

• Comparative genomics: gene trees, orthologues and paralogues, whole genome alignments and syntenic regions
• Variation: sequence variants, structural variants, Variant Effect Predictor 
• Regulation: annotation of regulatory elements using data from ENCODE, Roadmap Epigenomics and BLUEPRINT projects.
• Custom annotation: uploading / attaching your own data (BED, GFF, BAM, VCF files) to Ensembl
• Advanced access: other ways to access Ensembl data (MySQL, Perl API, REST API)

• Presentation of the software
• File format (vcf, gvf, var) / upload process
• Sample annotations (pedigree data, clinical features)
• Create and run an analysis (tumor/normal, trio and other family studies, personal genome, small / large cohorts)

• Data Analysis & Interpretation
  • Quality of the identified variants
  • Rare variants vs. common variants
  • Damaging variants, Loss- and Gain-of-Function variants,
  • Splice-site variants, Promotor loss
  • Homozygous vs. compound heterozygous variants
• Sample annotations (linkage data, expression data)
• Pathway interpretation and potential drug treatment
• Share samples and analyses

• Registration: http://ktp.cpgr.org.za/trainings/
  (To get a 2-week free training license)
• Queries: ktp@cpgr.org.za (Dr. Judit Kumuthini)
• WebEx | January 25 | 10:00 - 12:00 | Register

Ingenuity Variant Analysis combines analytical tools and integrated content to help you rapidly identify and prioritize variants by drilling down to a small, targeted subset of compelling variants based both upon published biological evidence and your own knowledge of disease biology. With Variant Analysis, you can interrogate your variants from multiple biological perspectives, explore different biological hypotheses, and identify the most promising variants for follow-up. Learn more about QIAGEN’s Ingenuity Variant Analysis.

Introduction to Ingenuity Variant Analysis

• Presentation of the software
• File format (vcf, gvf, var) / upload process
• Sample annotations (pedigree data, clinical features)
• Create and run an analysis (tumor/normal, trio and other family studies, personal genome, small / large cohorts)

Data Analysis & Interpretation

• Data Analysis & Interpretation
  • Quality of the identified variants
  • Rare variants vs. common variants
  • Damaging variants, Loss- and Gain-of-Function variants,
  • Splice-site variants, Promotor loss
  • Homozygous vs. compound heterozygous variants
• Sample annotations (linkage data, expression data)
• Pathway interpretation and potential drug treatment
• Share samples and analyses

Presenter: Dr. Elodie DUBUS, Field Application Scientist

• Registration: http://ktp.cpgr.org.za/trainings/
  (To get a 2-week free training license)
• Queries: ktp@cpgr.org.za (Dr. Judit Kumuthini)
• WebEx | Option 1 | January 18 | 10:00 - 12:00 | Register
• WebEx | Option 2 | January 25 | 10:00 - 12:00 | Register

Ingenuity Pathway Analysis Update (1 hour)

• RNA-seq support
• Comparison Analysis Results
• Advanced Analytics

Cause-and-Effect Functionality: Advanced Analysis (2 hours)

• Better understand regulatory mechanisms controlling the gene expression changes in your experiment. Predict upstream molecules, generate plausible signaling cascades, and generate novel causal networks utilizing new analytics in IPA

• Gain insights into your data by integrating Upstream Regulator results with Downstream Effects results; create causal hypotheses that explain what may be occurring upstream to cause particular phenotypic or functional outcomes downstream.

• Quickly determine if canonical pathways, including functional end-point, are increased or decreased based on the pattern of differentially expressed genes or proteins in your dataset.

• Explore IPA’s knowledge base and relate the most recent literature findings to your experimental data
• Create interactive and customized pathways
• Learn to use discovery tools such as species/tissues highlights
• Learn how to use complex searches for hypothesis generation

• Format your own data and learn the best practices to analyze your data in IPA.
• Upload the data to be analyzed.
• Set filter parameters and other core setting options.
• Run analysis.

• Learn how to rapidly understand biological processes most perturbed in your dataset, or across multiple time points or doses using the IPA Core Analysis.
• Learn about functional analysis, significance calculations, and understanding the cause and effect of gene expression changes in your experiment.
• Learn the multiple ways of relating the molecules in your dataset to the body of information in the Knowledge Base.
• Biological functions and diseases over-represented in your data
• Predicted directional effects on functions and diseases.
• Signaling and metabolic canonical pathways enriched in your data.
• Predicted upstream regulators to explain the changes observed in your data.
• Molecular networks and pathways to describe potential molecular interactions in your experimental system