Organizer: University of Cambridge

Host institution: University of Cambridge Bioinformatics Training

Start: Thursday, 13 February 2020 @ 09:30

End: Friday, 14 February 2020 @ 17:00

Venue: Craik-Marshall Building

City: Cambridge

Country: United Kingdom

Postcode: CB2 3AR

Scientific topic: Biology, Bioinformatics

Target audience:
  • This is aimed for life scientists with little or no experience in long-read sequencing that are looking at implementing these approaches in their research.
  • Graduate students
  • Postdocs and Staff members from the University of Cambridge
  • Institutions and other external Institutions or individuals
Description:

Analysis of whole genome data unearths a multitude of variants of different classes, which need to be filtered, annotated and validated to arrive at a causative variant for a disease. The current short length sequences, whilst being excellent at identifying single nucleotide variants and short insertions/deletions, struggle to correctly map structural variants (SVs). Long-read sequencing technologies offer improvements in the characterisation of genetic variation and regions that are difficult to assess with short-read sequences.

The aim of this course is to familiarise participants with long read sequencing technologies, their applications and the bioinformatics tools used to assemble this kind of data. Lectures will introduce this technology and provide insight into methods for the analysis of genomic data, while the hands-on sessions will allow participants to run analysis pipelines, focusing on data generated by the Oxford Nanopore Technologies (ONT) platform.

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.''

Event type:
  • Workshops and courses

Keywords: HDRUK

An introduction to long-read sequencing https://tess.elixir-europe.org/events/an-introduction-to-long-read-sequencing Analysis of whole genome data unearths a multitude of variants of different classes, which need to be filtered, annotated and validated to arrive at a causative variant for a disease. The current short length sequences, whilst being excellent at identifying single nucleotide variants and short insertions/deletions, struggle to correctly map structural variants (SVs). Long-read sequencing technologies offer improvements in the characterisation of genetic variation and regions that are difficult to assess with short-read sequences. The aim of this course is to familiarise participants with long read sequencing technologies, their applications and the bioinformatics tools used to assemble this kind of data. Lectures will introduce this technology and provide insight into methods for the analysis of genomic data, while the hands-on sessions will allow participants to run analysis pipelines, focusing on data generated by the Oxford Nanopore Technologies (ONT) platform. The training room is located on the first floor and there is currently no wheelchair or level access available to this level. Please note that if you are not eligible for a University of Cambridge [Raven](http://www.ucs.cam.ac.uk/docs/faq/raven/n5) account you will need to book or register your interest by linking [here](http://bioinfotraining.bio.cam.ac.uk/booking-form/?event-id=3327123&course-title=An%20Introduction%20to%20long-read%20sequencing).'' 2020-02-13 09:30:00 UTC 2020-02-14 17:00:00 UTC University of Cambridge Craik-Marshall Building, Cambridge, United Kingdom Craik-Marshall Building Cambridge United Kingdom CB2 3AR Biology Bioinformatics University of Cambridge Bioinformatics Training [] This is aimed for life scientists with little or no experience in long-read sequencing that are looking at implementing these approaches in their research.Graduate studentsPostdocs and Staff members from the University of CambridgeInstitutions and other external Institutions or individuals workshops_and_courses [] HDRUK