Past Meetup

SVAI Genomics Hackathon: Renal Cell Carcinoma

This Meetup is past

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Details

See Full Event Details: https://svai.co/p1rcc-main
Apply to Participate: https://svai.co/research-apply

**YOU MUST APPLY TO PARTICIPATE IN THIS EVENT** All levels of Engineering and Biology experience encouraged to apply.

See the video from our last Genomics Hackathon: http://svai.co/Hacking-a-Cure

INTRODUCTION
We are excited to announce the second computational cancer genomics event in SVAI's Collaborative Research Series. This event will focus on papillary renal-cell carcinoma type 1 (p1RCC), in partnership with RareKidneyCancer.org, Salesforce, Google, NIH, and NCBI.

We will invite over 100 researchers, engineers and enthusiasts to join us at Salesforce in San Francisco for an intense weekend of exploration in computational biomedicine. Interdisciplinary teams will work to further understand, develop potential interventions and advance the standard of care for p1RCC. In addition to sequencing a patient for this event, we will use genomic datasets for p1RCC through the NIH's Cancer Genome Atlas.

BACKGROUND
Papillary renal-cell carcinoma, accounts for between 15 to 20% of all kidney cancers. It occurs in the cells lining the small tubules in the kidney that filter waste from the blood and make urine. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.

See Full Event Details: https://svai.co/p1rcc-main
Apply to Participate: https://svai.co/research-apply

PURPOSE
-Advance papillary renal-cell carcinoma research.
-Contribute to real, ongoing patient case.
-Create interdisciplinary opportunities for computer scientists and biologists.
-Learn and develop skills in AI/ML, computational biology and cancer genomics.
-Build an open community for collaborative biomedicine discovery.

DATASETS
SVAI facilitated sequencing for one p1RCC patient (paid for by UCSF Health): DNA Whole Genome Sequencing for Tumor and Blood samples, sequenced at 90x using a BGISEQ-500. The data will be available as .bam and .vcf files.
NIH Cancer Genome Atlas (TCGA) for data for Papillary Renal Cell Carcinoma which includes: RNA-Seq gene expression, identifiable germ-line mutations and some clinical information:

Cases (291)

Genes (12,063)

Mutations (25,723)

WHY YOU SHOULD COME
-Advanced learning in computational biology and cancer genomics.
-Great mentoring sessions.
-Make new friends.
-Connect with us and our event partners.
-Our first research event was amazing, and this one will be even better.

See Full Event Details: https://svai.co/p1rcc-main
Apply to Participate: https://svai.co/research-apply

Attendees (1)

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