The understanding of the origin and progression of cancer remains in its infancy. However, due to rapid advances in the ability to accurately read and identify (i.e. sequence) the DNA of cancerous cells, the knowledge in this field is growing rapidly. Several comprehensive sequencing studies have shown that alterations of single base pairs within the DNA, known as Single Nucleotide Variants (SNVs), or duplications, deletions and rearrangements of larger segments of the genome, known as Structural Variations (SVs), are the primary causes of cancer and can influence what drugs will be effective against an individual tumor.
However, one of the major roadblocks hampering progress is the availability of accurate methods for interpreting genome sequence data. Due to the sheer volume of genomics data (the entire genome of just one person produces more than 100 gigabytes of raw data!), the ability to precisely localize a genomic alteration (SNV or SV) and resolve its association with cancer remains a considerable research challenge. Furthermore, preliminary benchmark studies conducted by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) have discovered that different mutation calling software run on the same data can result in detection of different sets of mutations. Clearly, optimization and standardization of mutation detection methods is a prerequisite for realizing personalized medicine applications based on a patient’s own genome.
The ICGC and TCGA are working to address this issue through an open community-based collaborative competition, run in conjunction with leading research institutions: the Ontario Institute for Cancer Research, University of California Santa Cruz, Sage Bionetworks, IBM-DREAM, and Oregon Health and Sciences University. Together, they are running the DREAM Somatic Mutation Calling Challenge, in which researchers from across the world “compete” to find the most accurate SNV and SV detection algorithms. By creating a living benchmark for mutation detection, the DREAM Challenge aims to improve standard methods for identifying cancer-associated mutations and rearrangements in tumor and normal samples from whole-genome sequencing data.
Given Google’s recent partnership with the Global Alliance for Genomics and Health, we are excited to provide cloud computing resources on Google Cloud Platform for competitors in the DREAM Challenge, enabling scientists who do not have ready access to large local computer clusters to participate with open access to contest data as well as credits that can be used for Google Compute Engine virtual machines. By leveraging the power of cloud technologies for genomics computing, contestants have access to powerful computational resources and a platform that allows the sharing of data. We hope to democratize research, foster the open access of data, and spur collaboration.
In addition to the core Google Cloud Platform infrastructure, the Google Genomics team has implemented a simple web-based API to store, process, explore, and share genomic data at scale. We have made the Challenge datasets available through the Google Genomics API. The challenge includes both simulated tumor data for which the correct answers are known and real tumor data for which the correct answers are not known.
 |
| Genomics API Browser showing a particular cancer variant position (highlighted) in dataset in silico #1 that was missed by many challenge participants. |
As an analogy, a "scratched magnifying glass" is used to examine the genome the first time around. The second time around, a "stronger magnifying glass with scratches in different places" is used to look at the specific locations in the genome reported by the challenge participants. By combining the data collected by those two different "magnifying glasses", and then comparing that against the cancer mutations submitted by the contest participants, the winner will then be determined.
We believe we are at the beginning of a transformation in medicine and basic research, driven by advances in genome sequencing and computing at scale. With the DREAM Challenge, we are all excited to be part of bringing researchers around the world to focus on this particular cancer research problem. To learn more about how to participate in the challenge register here.
Related Post:
computer
- Take a better selfie with Lily
- Free Lecture The Psychology of Computer Insecurity
- MOOC Research and Innovation
- Calculating Ada The Countess of Computing
- When can Quantum Annealing win
- Creating a templated Binary Search Tree Class in C
- Projecting without a projector sharing your smartphone content onto an arbitrary display
- Will a robot take your job
- Facebook Introduces ‘Hack ’ the programming language of the future
- High Resolution Scary Haunted House Wallpapers for Desktop
- TYBSC IT Sem V Question Papers 2009 Mumbai University
- Home automation update
- Very easy to download youtube videos audio mp3 format
- HD Dark Desktop Background Wallpapers Download
- Launching the Quantum Artificial Intelligence Lab
- Syrias children learn to code with the Raspberry Pi
- Running omxplayer from the command line easily using alias
- Largest collection of Google Logos on the web Set 7
- Collection of SQL queries with Answer and Output Set 2
- Prevent access to specific partition or drive
- Summer Games Learn to Program
- PiAUISuite Update and Voicecommand v3 1
- Sign in to edx org with Google and Facebook and
- Large Scale Machine Learning for Drug Discovery
- Hacker Tricks from Insiders A Threat to ERP Systems
facilitating
0 comments:
Post a Comment