Construct background mutation rate (noise) based on the correlation of mutation frequency and expression levels or replication time. It has been shown that longer replication time and lower expression levels imply higher mutation rates among the genome (http://www.nature.com/nature/journal/v499/n7457/full/nature12213.html). Transcription-coupled DNA repair results in high expression levels and low mutation rate. So I ...more »
Longitudinal sequencing: obtain the samples from patients at different time points. For examples, biopsy at diagnosis, pretreatment, post-treatment, and relaps
Provide a series of online short videos and short courses that will help users adopt the new tools and instructors to incorporate into courses. (Maybe this is obvious, but high-quality tutorials and case studies take significant time to develop.)
Subsets of large data sets should be provided for download to test local tools and for development of pipelines before they are uploaded to the cloud.
In addition to clinical data, tie in claims data. Test feasibility of using CMS virtual data center in conjunction with the NCI cloud to link data. Other multipayer claims databases may also offer longitudinal claims histories.
Bring in statistical data, particularly from longitudinal studies (NLSY, HRES, NHANES) and those that have collected biospecimens. (develop standardized re-consent form)
Allow patients and their doctors to access data about them securely
Datasets containing the quantitative inventory of proteins in TCGA tumors are beginning to become available. Both mass spectrometry and affinity-based technologies are generating these data. The cloud should provide a means to connect these data to corresponding TCGA data.
GPU technologies are rapidly becoming useful for speeding up some workflows by orders of magnitude. It would be useful to have some GPU resources available for cloud computing.
Mining cancer data in the cloud is great, but to enable ongoing research there should be a connection to specimens so researchers can pursue followup studies. This will require storing data about specimens from studies such as TCGA - where they are, how they can be accessed and what consent they are governed by. Just as the data from publications should be made available to allow reproduction of results, so should samples ...more »
Please provide a process for the deployment of databases and web applications such as MSKCC cBio, or ISB's Regulome Explorer or GeneSpot.
(You could use Github as a platform for accepting contributions.)
In this main open access article
http://fr.scribd.com/doc/169323556/7401586of18september2013
published in october 2013 by APPLIED MATHEMATICS (BIOMATHEMATICS issue http://www.scirp.org/journal/am/ ) we show how our human genome MUST be considedred as a NUMERICAL WHOLE. The idea is now to run this kind of analysis on complete genomes DNA from CANCER CELLS (LOH) at individual chromosomes and whole genome scales.
Galaxy and GenePattern are examples of systems that could provide access to data sets, pipelines, and publishable, shareable, and reproducible workflows. Ideally, existing familiar and popular platforms such as these would be supported. In addition to improving or enabling interactions between these tools, effort should be directed towards facilitating programmatic access to the underlying data in order to support custom ...more »
For those of us who don't want to use the cloud workflow, please make it so we still have access to all the raw data. Please don't lock us into your analytic approaches!
Most current approaches for BigData analysis involve moving data to a server, HPC infrastructure or cloud where the software tools and reference databases are pre-configured. This is inefficient since this approach requires making redundant copies of data each time and additional costs/time associated with moving data back and forth. Since there is no single tool or workflow to analyze genomic data, multiple copies ...more »
A sample could be analyzed for DNA sequence variations, structural variations, CNVs, Gene or transcript isoform expression, genome-wide methylation patterns, ChIP-seq for specific transcription factors, metabolomic or proteomic analysis, and other molecular profiles. A framework that allows a researcher to readily identify all molecular data types associated with a particular sample and integrate the results of such analyses ...more »