Whole Exome Sequencing and RNA-sequence data analysis Prerequisites
This course covers state-of-the-art tools and methods for NGS RNA-seq and exome variant data analysis, which are of major relevance in today's genomic and gene expression studies.
It is oriented to experimental researchers, post-doctoral and PhD students who want to learn about the state-of-the-art of genomic variant and transcriptomics data analysis methodologies and carry out their own analysis.
Further information is available here.
Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by linking here.
- Graduate students, Postdocs and Staff members from the University of Cambridge, Affiliated Institutions and other external Institutions or individuals
- Further details regarding eligibility criteria are available here
- Further details regarding the charging policy are available here
- Basic computing skills
- Graduate level in Life Sciences
Number of sessions: 3
# | Date | Time | Venue | Trainers | |
---|---|---|---|---|---|
1 | Wed 17 Jun 2015 09:30 - 17:30 | 09:30 - 17:30 | Bioinformatics Training Room, Craik-Marshall Building | map | David Montaner, Marta Bleda, Joaquin Dopazo |
2 | Thu 18 Jun 2015 09:30 - 17:30 | 09:30 - 17:30 | Bioinformatics Training Room, Craik-Marshall Building | map | David Montaner, Marta Bleda, Joaquin Dopazo |
3 | Fri 19 Jun 2015 09:30 - 17:30 | 09:30 - 17:30 | Bioinformatics Training Room, Craik-Marshall Building | map | David Montaner, Marta Bleda, Joaquin Dopazo |
The aim of this course is to familiarize the students with the latest analysis methodologies and to provide hands-on training on the analytical approaches implemented for RNA-seq data and whole exome variant analysis.
Presentations and practicals
A slightly longer version of this course is regularly offered as a part of "The Gulbenkian Training Programme in Bioinformatics". For further details of how to apply, see: http://gtpb.igc.gulbenkian.pt/bicourses/
3
A number of times per year
Booking / availability