Dataset Information

Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API.

ABSTRACT: The NCI Genomic Data Commons (GDC) was launched in 2016 and makes available over 4 petabytes (PB) of cancer genomic and associated clinical data to the research community. This dataset continues to grow and currently includes over 14,500 patients. The GDC is an example of a biomedical data commons, which collocates biomedical data with storage and computing infrastructure and commonly used web services, software applications, and tools to create a secure, interoperable, and extensible resource for researchers. The GDC is (i) a data repository for downloading data that have been submitted to it, and also a system that (ii) applies a common set of bioinformatics pipelines to submitted data; (iii) reanalyzes existing data when new pipelines are developed; and (iv) allows users to build their own applications and systems that interoperate with the GDC using the GDC Application Programming Interface (API). We describe the GDC API and how it has been used both by the GDC itself and by third parties. Cancer Res; 77(21); e15-18. ©2017 AACR.

SUBMITTER: Wilson S

PROVIDER: S-EPMC5683428 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API.

Wilson Shane S Fitzsimons Michael M Ferguson Martin M Heath Allison A Jensen Mark M Miller Josh J Murphy Mark W MW Porter James J Sahni Himanso H Staudt Louis L Tang Yajing Y Wang Zhining Z Yu Christine C Zhang Junjun J Ferretti Vincent V Grossman Robert L RL

Cancer research 20171101 21

The NCI Genomic Data Commons (GDC) was launched in 2016 and makes available over 4 petabytes (PB) of cancer genomic and associated clinical data to the research community. This dataset continues to grow and currently includes over 14,500 patients. The GDC is an example of a biomedical data commons, which collocates biomedical data with storage and computing infrastructure and commonly used web services, software applications, and tools to create a secure, interoperable, and extensible resource f ...[more]

PMID: 29092930

Similar Datasets

Project description:BackgroundPenstemon's unique phenotypic diversity, hardiness, and drought-tolerance give it great potential for the xeric landscaping industry. Molecular markers will accelerate the breeding and domestication of drought tolerant Penstemon cultivars by, creating genetic maps, and clarifying of phylogenetic relationships. Our objectives were to identify and validate interspecific molecular markers from four diverse Penstemon species in order to gain specific insights into the Penstemon genome.ResultsWe used a 454 pyrosequencing and GR-RSC (genome reduction using restriction site conservation) to identify homologous loci across four Penstemon species (P. cyananthus, P. davidsonii, P. dissectus, and P. fruticosus) representing three diverse subgenera with considerable genome size variation. From these genomic data, we identified 133 unique interspecific markers containing SSRs and INDELs of which 51 produced viable PCR-based markers. These markers produced simple banding patterns in 90% of the species × marker interactions (~84% were polymorphic). Twelve of the markers were tested across 93, mostly xeric, Penstemon taxa (72 species), of which ~98% produced reproducible marker data. Additionally, we identified an average of one SNP per 2,890 bp per species and one per 97 bp between any two apparent homologous sequences from the four source species. We selected 192 homologous sequences, meeting stringent parameters, to create SNP markers. Of these, 75 demonstrated repeatable polymorphic marker functionality across the four sequence source species. Finally, sequence analysis indicated that repetitive elements were approximately 70% more prevalent in the P. cyananthus genome, the largest genome in the study, than in the smallest genome surveyed (P. dissectus).ConclusionsWe demonstrated the utility of GR-RSC to identify homologous loci across related Penstemon taxa. Though PCR primer regions were conserved across a broadly sampled survey of Penstemon species (93 taxa), DNA sequence within these amplicons (12 SSR/INDEL markers) was highly diverse. With the continued decline in next-generation sequencing costs, it will soon be feasible to use genomic reduction techniques to simultaneously sequence thousands of homologous loci across dozens of Penstemon species. Such efforts will greatly facilitate our understanding of the phylogenetic structure within this important drought tolerant genus. In the interim, this study identified thousands of SNPs and over 50 SSRs/INDELs which should provide a foundation for future Penstemon phylogenetic studies and breeding efforts.

Project description:Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

Dataset Information

Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API.

Publications

Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets