Webinar – Taking definitive care of the NGS big data deluge

20151401 Live webinar header (website)

 

 

 

 

Go2webinar GENALICE presented in collaboration with deCODE genetics:

Taking definitive care of the NGS big data deluge

A 45-minute webinar on how to make large-scale NGS studies affordable
Watch the recordings and:

  • Learn more about the GAR (GENALICE Aligned Reads) file, with up to a 100-fold storage footprint reduction
  • Gain insight into the methods behind this footprint reduction technique
  • Have an understanding of quality binning and its benefits
  • Be aware of the additional opportunities offered by a small footprint file

Tackling the NGS big data deluge

GENALICE MAP is capable of aligning a complete human genome (37x coverage) in 25 minutes or less and produces an output file of only 4GB, the so-called GAR (GENALICE Aligned Reads) file.  After variant calling a  VCF is produced in 5 minutes, without compromising the quality.

deCODE

GENALICE and deCODE partnered in studies to further substantiate that the choices made to optimize file size reduction have no quality impact. During this live webinar we will show you how we coped with the challenges of the NGS big data deluge and reveal the results of our joined studies. Join our webinar and learn how you can make large-scale NGS projects affordable.

Fill in the form below and watch the recordings

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Presenters

Hans_Karten_roundHans Karten – CEO/CTO, GENALICE

  • Expert in design and development of high-volume data management and processing solutions; both hardware and software
  • Experience in the software industry, in the field of core database development and high-performance computing architectures
  • 14 years career at Oracle Corporation, patentee of 11 patents
  • Co-founder of GENALICE and inventor of GENALICE MAP

 

Bas_tolhuis_roundBas Tolhuis – Bioinformatics lead, GENALICE

  • PhD in molecular biology (2003) at the Erasmus Medical Center (Rotterdam, The Netherlands)
  • Post-doctoral training at the Netherlands Cancer Institute (NKI-AVL, Amsterdam, The Netherlands)
  • Worked with high-throughput datasets, including microarray and next-generation sequencing data
  • Performed and developed molecular biology techniques, as well as experience on the computational side doing statistical and bioinformatics analysis of the data