Limits...
From big data analysis to personalized medicine for all: challenges and opportunities.

Alyass A, Turcotte M, Meyre D - BMC Med Genomics (2015)

Bottom Line: Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries.This is mirrored by an increasing lag between our ability to generate and analyze big data.This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, Canada. alyassa@math.mcmaster.ca.

ABSTRACT
Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

No MeSH data available.


An interdisciplinary cloud-based model to implement personalized medicine. The consecutive knowledge and service swapping between modeling and software experts in research and development units is essential for the management, integration, and analysis of omics data. Thorough software and model development will derive updates upon knowledge bases for complex diseases, in addition to clinical utilities, commercial applications, privacy and access control, user-friendly interfaces, and advanced software for fast computations within the cloud. This translates into personalized medicine via personal clouds that upload wellness indices into personal devices, electronic databases for health professionals, and innovative medical devices
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4482045&req=5

Fig3: An interdisciplinary cloud-based model to implement personalized medicine. The consecutive knowledge and service swapping between modeling and software experts in research and development units is essential for the management, integration, and analysis of omics data. Thorough software and model development will derive updates upon knowledge bases for complex diseases, in addition to clinical utilities, commercial applications, privacy and access control, user-friendly interfaces, and advanced software for fast computations within the cloud. This translates into personalized medicine via personal clouds that upload wellness indices into personal devices, electronic databases for health professionals, and innovative medical devices

Mentions: Major investments need to be made in bioinformatics, biomathematics, and biostatistics by the scientific community to accelerate the transition to personalized medicine. Classic research laboratories do not possess sufficient storage and computational resources for processing omics data. Laboratory-hosted servers require investments in informatics support for configuring and using software. Such servers are not only expensive to setup and maintain, but do not meet the dynamic requirements of different workflows for processing omics data, leading to either extravagant or sub-optimal servers. One promising technology to close the gap between generation and handling of omics data is cloud computing [46, 47]. It is an adaptive storage and computing service that exploits the full potential of multiple computers together as a virtual resource via the Internet [48]. Examples include the EasyGenomics cloud in Beijing Genomics Institute (BGI), and “Embassy” clouds as part of ELIXIR project in collaboration with multiple European countries (UK, Sweden, Switzerland, Czech Republic, Estonia, Norway, the Netherlands, and Denmark) [49]. The focus is currently placed on developing cloud-based toolkits and workflow platforms for high-throughput processing and analysis of omics data [50, 51, 49, 52]. More recently, Graphics Processing Units (GPUs) have been proposed for general-purpose computing in a cloud environment [53]. GPUs provide faster computations as accelerators by one or two orders of magnitudes compared to general Central Processing Units (CPUs) and have been exploited to cope with exponentially growing data [54–56]. MUMmerGPU for example, processes queries in parallel on a graphics card, achieves more than a 10-fold speedup over a CPU version of the sequence alignment kernel, and outperforms the CPU version of MUMmer by 3.5-fold in total application time when aligning reads [57]. However, a significant amount of work will be required for developing parallelization algorithms considering the heterogeneous framework of omics data that present challenges in communications and synchronizations [37]. There are tradeoffs between computational cost (floating-point operations), synchronization, and communications to consider while developing parallelization algorithms [58]. Moreover, developing error-free and secure applications is a challenging and labor-intensive, yet critically important task. Examples of programming errors and studies outlining wrongly mapped SNPs in commercial SNP chips have been reported in literature [59–61]. There is a need to validate the reliability of research platforms before considering the clinical utility of omics data. For instance, ToolShed, a feature of the Galaxy project that draws in software developers worldwide to upload and validate software tools, aims to enhance the reliability of bioinformatics tools. Novel tools and workflows with demonstrated usefulness and instructions are publically available (http://toolshed.g2.bx.psu.edu/) [62]. Both storage and computing platform such as Bioimbus [63], Bioconductor [64], CytoScape [65], are made available by scientists to exchange algorithms and data. There are many questions and methodologies that researchers may wish to consider, and this continuously drives on novel bioinformatics tools. Ultimately, lightweight programing environments and supporting programs with diverse cloud-based utilities are essential to enable those without or with limited programing skills to investigate biological networks [66]. Figure 3 illustrates a cloud-based framework that may help to implement personalized medicine. Much more programing efforts are still needed for the integration and interpretation of omics data in the transition to personalized medicine. Potential downstream applications are not always apparent when data are generated, promoting sophisticated flexible programs that may be regularly updated [67].Fig. 3


From big data analysis to personalized medicine for all: challenges and opportunities.

Alyass A, Turcotte M, Meyre D - BMC Med Genomics (2015)

An interdisciplinary cloud-based model to implement personalized medicine. The consecutive knowledge and service swapping between modeling and software experts in research and development units is essential for the management, integration, and analysis of omics data. Thorough software and model development will derive updates upon knowledge bases for complex diseases, in addition to clinical utilities, commercial applications, privacy and access control, user-friendly interfaces, and advanced software for fast computations within the cloud. This translates into personalized medicine via personal clouds that upload wellness indices into personal devices, electronic databases for health professionals, and innovative medical devices
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4482045&req=5

Fig3: An interdisciplinary cloud-based model to implement personalized medicine. The consecutive knowledge and service swapping between modeling and software experts in research and development units is essential for the management, integration, and analysis of omics data. Thorough software and model development will derive updates upon knowledge bases for complex diseases, in addition to clinical utilities, commercial applications, privacy and access control, user-friendly interfaces, and advanced software for fast computations within the cloud. This translates into personalized medicine via personal clouds that upload wellness indices into personal devices, electronic databases for health professionals, and innovative medical devices
Mentions: Major investments need to be made in bioinformatics, biomathematics, and biostatistics by the scientific community to accelerate the transition to personalized medicine. Classic research laboratories do not possess sufficient storage and computational resources for processing omics data. Laboratory-hosted servers require investments in informatics support for configuring and using software. Such servers are not only expensive to setup and maintain, but do not meet the dynamic requirements of different workflows for processing omics data, leading to either extravagant or sub-optimal servers. One promising technology to close the gap between generation and handling of omics data is cloud computing [46, 47]. It is an adaptive storage and computing service that exploits the full potential of multiple computers together as a virtual resource via the Internet [48]. Examples include the EasyGenomics cloud in Beijing Genomics Institute (BGI), and “Embassy” clouds as part of ELIXIR project in collaboration with multiple European countries (UK, Sweden, Switzerland, Czech Republic, Estonia, Norway, the Netherlands, and Denmark) [49]. The focus is currently placed on developing cloud-based toolkits and workflow platforms for high-throughput processing and analysis of omics data [50, 51, 49, 52]. More recently, Graphics Processing Units (GPUs) have been proposed for general-purpose computing in a cloud environment [53]. GPUs provide faster computations as accelerators by one or two orders of magnitudes compared to general Central Processing Units (CPUs) and have been exploited to cope with exponentially growing data [54–56]. MUMmerGPU for example, processes queries in parallel on a graphics card, achieves more than a 10-fold speedup over a CPU version of the sequence alignment kernel, and outperforms the CPU version of MUMmer by 3.5-fold in total application time when aligning reads [57]. However, a significant amount of work will be required for developing parallelization algorithms considering the heterogeneous framework of omics data that present challenges in communications and synchronizations [37]. There are tradeoffs between computational cost (floating-point operations), synchronization, and communications to consider while developing parallelization algorithms [58]. Moreover, developing error-free and secure applications is a challenging and labor-intensive, yet critically important task. Examples of programming errors and studies outlining wrongly mapped SNPs in commercial SNP chips have been reported in literature [59–61]. There is a need to validate the reliability of research platforms before considering the clinical utility of omics data. For instance, ToolShed, a feature of the Galaxy project that draws in software developers worldwide to upload and validate software tools, aims to enhance the reliability of bioinformatics tools. Novel tools and workflows with demonstrated usefulness and instructions are publically available (http://toolshed.g2.bx.psu.edu/) [62]. Both storage and computing platform such as Bioimbus [63], Bioconductor [64], CytoScape [65], are made available by scientists to exchange algorithms and data. There are many questions and methodologies that researchers may wish to consider, and this continuously drives on novel bioinformatics tools. Ultimately, lightweight programing environments and supporting programs with diverse cloud-based utilities are essential to enable those without or with limited programing skills to investigate biological networks [66]. Figure 3 illustrates a cloud-based framework that may help to implement personalized medicine. Much more programing efforts are still needed for the integration and interpretation of omics data in the transition to personalized medicine. Potential downstream applications are not always apparent when data are generated, promoting sophisticated flexible programs that may be regularly updated [67].Fig. 3

Bottom Line: Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries.This is mirrored by an increasing lag between our ability to generate and analyze big data.This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

View Article: PubMed Central - PubMed

Affiliation: Department of Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, Canada. alyassa@math.mcmaster.ca.

ABSTRACT
Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

No MeSH data available.