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Next Generation Bioinformatics for Clinical Genomics: using de novo assembly in personalized medicine

  • Project Leaders: Inanc Birol, Aly Karsan, Steven Jones
  • Institutions: BC Cancer (Previously BC Cancer Agency (BCCA))
  • Budget: $999867
  • Program/Competition: Bioinformatics and Computational Biology Competitions
  • Genome Centre(s): Genome Canada
  • Fiscal Year: 2013
  • Status: Closed

Substantial advancements in cancer care and healthcare economics can be realized through the development of genomics technologies to detect variations and mutations in DNA and RNA in a manner that allows: i) effective preventative care, and/or ii) efficient diagnosis and treatment. One technology that would enable this vision is high throughput DNA and RNA sequencing. Yet, without proper downstream data analysis and interpretation, it would fall short of reaching its potential. Further, building bioinformatics tools to provide highly sensitive and specific results for clinical use is far from being a trivial task. This project responded to this challenge by aiming to develop (1) improved accuracy and quality of DNA sequencing data analysis pipelines, (2) streamlined analysis turnaround times, and (3) standard analysis pipelines and clinical reports.

By the end of the project, they developed and published eight new software tools and released new versions of their principal computer algorithms. Their flagship DNA sequencing data processing software, ABySS, improved five-fold in speed, and an order of magnitude in memory use, which won the inaugural International Bioinformatics Resource Award from the Swiss Institute of Bioinformatics. ABySS and its downstream analysis tools are being implemented in a personalized oncogenomics clinical trial at BCCA. These improvements are also implemented at the Centre for Clinical Genomics (CCG), which is a CAP-accredited lab performing clinical sequencing for the BCCA.

They improved CCG analysis pipeline from analyzing 14 genes of interest in an hour to a turnaround time of 30 minutes to analyze 609 known cancer genes. Using the tools developed, they also analyzed 10,668 TCGA (the Cancer Genome Atlas) RNA-seq datasets in 5 days, which would not have been possible using alternative methods. All the software has been made available free of charge to other academic users, and downloaded more than 3,000 times by external users in the first 10 month of 2016. They have secured a provisional US patent for one of the algorithms, and have licensed another to a US organization; $2M funding has been leveraged.