Big Data: the challenge for small research groups in the era of cancer genomics

Noor, Aisyah Mohd; Holmberg, Lars; Gillett, Cheryl; Grigoriadis, Anita

doi:10.1038/bjc.2015.341

Cited by 46 publications

(26 citation statements)

References 71 publications

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“…However, the challenge remains to develop SOP for preparation of biosamples and for production, storage, management, and quality control of high-throughput data (Börnigen et al, 2015;Dona et al, 2014;Noor et al, 2015). The biobank could need to secure strategic biosample panels with high quality and specialized concept, for integrative analysis of biological big data.…”

Section: Discussionmentioning

confidence: 99%

How Should Biobanks Prioritize and Diversify Biosample Collections? A 40-Year Scientific Publication Trend Analysis by the Type of Biosample

Lee

Kim

2018

OMICS: A Journal of Integrative Biology

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Biobanks are infrastructures for large-scale biology innovation. Governance of biobanks can be usefully informed by studies of publication trends, for example, on the types of biosamples employed in scientific publications. We examined trends in each of the serum, plasma, peripheral blood mononuclear cell (PBMC), buffy coat, tissue, and gut microbiome biosample-related scientific publications over the past 40 years, using data between 1977 and 2016 from the Scopus database. We found that the number of tissue-related publications was the highest in each year of our analysis than other biosamples, but was generally less than the sum of serum- and plasma-related publications. Importantly, the microbiome publications increased greatly starting in the 2010s, and currently overtook the number of publications on PBMC and buffy coat. Among serum-, plasma-, and tissue-related publications, the number of protein- and RNA-related publications was generally higher than cell-free DNA-, DNA-, and metabolite-related publications for the past 40 years. Mass spectrometry- and next-generation sequencing-related publications have increased dramatically since the 2000s and 2010s, respectively. Microbiome- and metabolite-related biosamples can help diversify future biosample collections, while tissue collections appear to maintain their importance in scientific publications. We also report here our observations on the countries that use biosample research (e.g., China, United Kingdom, United States, and others). These publication trends by the type of biosamples illuminate roadmaps by which biobanks might establish and diversify their biosample collections in the future. In addition, we note that biobanks need to secure biosamples appropriate for integrated analysis of multi-omics research data.

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Section: Discussionmentioning

confidence: 99%

How Should Biobanks Prioritize and Diversify Biosample Collections? A 40-Year Scientific Publication Trend Analysis by the Type of Biosample

Lee

Kim

2018

OMICS: A Journal of Integrative Biology

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“…Radiogenomic generation of big data also poses challenges. It is a nontrivial issue to store, manage, extract, analyze, integrate, visualize, and communicate information from the myriad of data representations of cancer (151). Such multifactorial and heterogeneous data must be integrated in a standardized, cost-effective, and secure manner.…”

Section: Challengesmentioning

confidence: 99%

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

et al. 2018

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Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. RSNA, 2018.

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“…Until 2007, the volume of all available data was estimated to be slightly less than 300 EB [2]; but in 2018, more than 2.5 EB of data were generated daily [3] due to the vast number of users of social networks, online business, and other platforms. Big Data applications are also used in many branches of science such as physics, astronomy, omics (like genomics or epigenomics [4] [5] [6]). Omics research is currently tightly bound to the rising number of patient records in medical sciences and the application of Next Generation Sequencing (NGS) technologies ( [7]).…”

Section: Introductionmentioning

confidence: 99%

BigMPI4py: Python module for parallelization of Big Data objects

Araúzo-Bravo

2019

Preprint

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Big Data analysis is a discipline with a growing number of areas where huge amounts of data is extracted and analyzed. Parallelization in Python integrates Message Passing Interface via mpi4py module. Since mpi4py does not support parallelization of objects greater than 2 31 bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and also uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and HPC systems. We validated BigMPI4py on whole genome bisulfite sequencing (WGBS) DNA methylation ENCODE data of 59 samples from 27 human tissues. We categorized them on the three germ layers and developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. We observed a differentiation of the germ layers, and a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores. BigMPI4py is available at https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis

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Big Data: the challenge for small research groups in the era of cancer genomics

Cited by 46 publications

References 71 publications

How Should Biobanks Prioritize and Diversify Biosample Collections? A 40-Year Scientific Publication Trend Analysis by the Type of Biosample

How Should Biobanks Prioritize and Diversify Biosample Collections? A 40-Year Scientific Publication Trend Analysis by the Type of Biosample

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

BigMPI4py: Python module for parallelization of Big Data objects

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