Managing, analysing, and integrating big data in medical bioinformatics: open problems and future perspectives

Seminal plasma proteomics as putative biomarkers for male infertility diagnosis

Male infertility represents a significant global public health issue that is currently emerging as a prominent research focus. Presently, laboratories adhere to the guidelines outlined by the World Health Organization (WHO) manuals for conducting routine semen analysis to diagnose male infertility. However, the accuracy of results in predicting sperm quality and fertility is limited because some individuals with a normal semen analysis report, an unremarkable medical history, and a physical examination may still experience infertility. As a result, the importance of employing more advanced techniques to investigate sperm function and male fertility in the treatment of male infertility and/or subfertility becomes apparent. The standard test for evaluating human semen has been improved by more complex tests that look at things like reactive oxygen species (ROS) levels, total antioxidant capacity (TAC), sperm DNA fragmentation levels, DNA compaction, apoptosis, genetic testing, and the presence and location of anti-sperm antibodies. Recent discoveries of novel biomarkers have significantly enriched our understanding of male fertility. Moreover, the notable biological diversity among samples obtained from the same individual complicates the efficacy of routine semen analysis. Therefore, unraveling the molecular mechanisms involved in fertilization is pivotal in expanding our understanding of factors contributing to male infertility. By understanding how these proteins work and what role they play in sperm activity, we can look at the expression profile in men who can't have children to find diagnostic biomarkers. This review examines the various sperm and seminal plasma proteins associated with infertility, as well as proteins that are either deficient or exhibit aberrant expression, potentially contributing to male infertility causes.

Anesthesia decision analysis using a cloud-based big data platform

Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.

Strokecopilot: a literature-based clinical decision support system for acute ischemic stroke treatment

BACKGROUND

Acute ischemic stroke (AIS) is an immediate emergency whose management is becoming more and more personalized while facing a limited number of neurologists with high expertise. Clinical decision support systems (CDSS) are digital tools leveraging information and artificial intelligence technologies. Here, we present the Strokecopilot project, a CDSS for the management of the acute phase of AIS. It has been designed to support the evidence-based medicine reasoning of neurologists regarding the indications of intravenous thrombolysis (IVT) and endovascular treatments (ET).

METHODS

Reference populations were manually extracted from the field's main guidelines and randomized clinical trials (RCT). Their characteristics were harmonized in a computerized reference database. We developed a web application whose algorithm identifies the reference populations matching the patient's characteristics. It returns the latter's outcomes in a graphical user interface (GUI), whose design has been driven by real-world practices.

RESULTS

Strokecopilot has been released at www.digitalneurology.net . The reference database includes 25 reference populations from 2 guidelines and 15 RCTs. After a request, the reference populations matching the patient characteristics are displayed with a summary and a meta-analysis of their results. The status regarding IVT and ET indications are presented as "in guidelines", "in literature", or "outside literature references". The GUI is updated to provide several levels of explanation. Strokecopilot may be updated as the literature evolves by loading a new version of the reference populations' database.

CONCLUSION

Strokecopilot is a literature-based CDSS, developed to support neurologists in the management of the acute phase of AIS.

A guide to the BRAIN Initiative Cell Census Network data ecosystem

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Multiple sclerosis and computational biology (Review)

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS.

A Healthcare Quality Assessment Model Based on Outlier Detection Algorithm

With the extremely rapid growth of data in various industries, big data is gradually recognized and valued by people. Medical big data, which can best reflect the significance of big data value, has also received attention from various parties. In Saudi Arabia, healthcare quality assessment is mostly based on human experience and basic statistical methods. In this paper, we proposed a healthcare quality assessment model based on medical big data in a region of Saudi Arabia, which integrated traditional evaluation methods and machine learning based techniques. Healthcare data has been accurate and effective after noise processing, and the outliers could reflect certain medical quality information. An improved k-nearest neighbors (KNN) algorithm has been proposed and its time complexity have been reduced to be more suitable for big data processing. An outlier indicator has been established based on statistical methods and the improved KNN algorithm. Experimental results showed that the proposed approach has good potential for detecting hospitals with financial fraud and poor-quality medical care.

A novel mind-set in primate experimentation: Implications for primate welfare

We emphasize the importance of studying the primate brain in cognitive neuroscience and suggest a new mind-set in primate experimentation within the boundaries of animal welfare regulations. Specifically, we list the advantages of investigating both genes and neural mechanisms and processes in the emergence of behavioral and cognitive functions, and propose the establishment of an open field of primate research. The latter may be conducted by implementing and harmonizing experimental practices with ethical guidelines that regulate (1) management of natural parks with free-moving populations of target nonhuman primates, (2) establishment of indoor-outdoor labs for both system genetics and neuroscience investigations, and (3) hotel space and technologies which remotely collect and dislocate information regarding primates geographically located elsewhere.

0s and 1s in marine molecular research: a regional HPC perspective

High-performance computing (HPC) systems have become indispensable for modern marine research, providing support to an increasing number and diversity of users. Pairing with the impetus offered by high-throughput methods to key areas such as non-model organism studies, their operation continuously evolves to meet the corresponding computational challenges. Here, we present a Tier 2 (regional) HPC facility, operating for over a decade at the Institute of Marine Biology, Biotechnology, and Aquaculture of the Hellenic Centre for Marine Research in Greece. Strategic choices made in design and upgrades aimed to strike a balance between depth (the need for a few high-memory nodes) and breadth (a number of slimmer nodes), as dictated by the idiosyncrasy of the supported research. Qualitative computational requirement analysis of the latter revealed the diversity of marine fields, methods, and approaches adopted to translate data into knowledge. In addition, hardware and software architectures, usage statistics, policy, and user management aspects of the facility are presented. Drawing upon the last decade's experience from the different levels of operation of the Institute of Marine Biology, Biotechnology, and Aquaculture HPC facility, a number of lessons are presented; these have contributed to the facility's future directions in light of emerging distribution technologies (e.g., containers) and Research Infrastructure evolution. In combination with detailed knowledge of the facility usage and its upcoming upgrade, future collaborations in marine research and beyond are envisioned.

GenoVault: a cloud based genomics repository

GenoVault is a cloud-based repository for handling Next Generation Sequencing (NGS) data. It is developed using OpenStack-based private cloud with various services like keystone for authentication, cinder for block storage, neutron for networking and nova for managing compute instances for the Cloud. GenoVault uses object-based storage, which enables data to be stored as objects instead of files or blocks for faster retrieval from different distributed object nodes. Along with a web-based interface, a JavaFX-based desktop client has also been developed to meet the requirements of large file uploads that are usually seen in NGS datasets. Users can store files in their respective object-based storage areas and the metadata provided by the user during file uploads is used for querying the database. GenoVault repository is designed taking into account future needs and hence can scale both vertically and horizontally using OpenStack-based cloud features. Users have an option to make the data shareable to the public or restrict the access as private. Data security is ensured as every container is a separate entity in object-based storage architecture which is also supported by Secure File Transfer Protocol (SFTP) for data upload and download. The data is uploaded by the user in individual containers that include raw read files (fastq), processed alignment files (bam, sam, bed) and the output of variation detection (vcf). GenoVault architecture allows verification of the data in terms of integrity and authentication before making it available to collaborators as per the user’s permissions. GenoVault is useful for maintaining the organization-wide NGS data generated in various labs which is not yet published and submitted to public repositories like NCBI. GenoVault also provides support to share NGS data among the collaborating institutions. GenoVault can thus manage vast volumes of NGS data on any OpenStack-based private cloud.

Computational methods for chromosome-scale haplotype reconstruction

High-quality chromosome-scale haplotype sequences of diploid genomes, polyploid genomes, and metagenomes provide important insights into genetic variation associated with disease and biodiversity. However, whole-genome short read sequencing does not yield haplotype information spanning whole chromosomes directly. Computational assembly of shorter haplotype fragments is required for haplotype reconstruction, which can be challenging owing to limited fragment lengths and high haplotype and repeat variability across genomes. Recent advancements in long-read and chromosome-scale sequencing technologies, alongside computational innovations, are improving the reconstruction of haplotypes at the level of whole chromosomes. Here, we review recent and discuss methodological progress and perspectives in these areas.

A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

Healthcare information systems can reduce the expenses of treatment, foresee episodes of pestilences, help stay away from preventable illnesses, and improve personal life satisfaction. As of late, considerable volumes of heterogeneous and differing medicinal services data are being produced from different sources covering clinic records of patients, lab results, and wearable devices, making it hard for conventional data processing to handle and manage this amount of data. Confronted with the difficulties and challenges facing the process of managing healthcare big data such as volume, velocity, and variety, healthcare information systems need to use new methods and techniques for managing and processing such data to extract useful information and knowledge. In the recent few years, a large number of organizations and companies have shown enthusiasm for using semantic web technologies with healthcare big data to convert data into knowledge and intelligence. In this paper, we review the state of the art on the semantic web for the healthcare industry. Based on our literature review, we will discuss how different techniques, standards, and points of view created by the semantic web community can participate in addressing the challenges related to healthcare big data.

Big data analytics in medical engineering and healthcare: methods, advances and challenges

Big data analytics are gaining popularity in medical engineering and healthcare use cases. Stakeholders are finding big data analytics reduce medical costs and personalise medical services for each individual patient. Big data analytics can be used in large-scale genetics studies, public health, personalised and precision medicine, new drug development, etc. The introduction of the types, sources, and features of big data in healthcare as well as the applications and benefits of big data and big data analytics in healthcare is key to understanding healthcare big data and will be discussed in this article. Major methods, platforms and tools of big data analytics in medical engineering and healthcare are also presented. Advances and technology progress of big data analytics in healthcare are introduced, which includes artificial intelligence (AI) with big data, infrastructure and cloud computing, advanced computation and data processing, privacy and cybersecurity, health economic outcomes and technology management, and smart healthcare with sensing, wearable devices and Internet of things (IoT). Current challenges of dealing with big data and big data analytics in medical engineering and healthcare as well as future work are also presented.

Enhancing efficiency and scientific impact of a clinical trials network: the Pediatric Heart Network Integrated CARdiac Data and Outcomes (iCARD) Collaborative

Recent years have seen an exponential increase in the variety of healthcare data captured across numerous sources. However, mechanisms to leverage these data sources to support scientific investigation have remained limited. In 2013 the Pediatric Heart Network (PHN), funded by the National Heart, Lung, and Blood Institute, developed the Integrated CARdiac Data and Outcomes (iCARD) Collaborative with the goals of leveraging available data sources to aid in efficiently planning and conducting PHN studies; supporting integration of PHN data with other sources to foster novel research otherwise not possible; and mentoring young investigators in these areas. This review describes lessons learned through the development of iCARD, initial efforts and scientific output, challenges, and future directions. This information can aid in the use and optimisation of data integration methodologies across other research networks and organisations.

Development of an Interactive Web Application "Shiny App for Frequency Analysis on Homo sapiens Genome (SAFA-HsG)"

The web application "Shiny App for Frequency Analysis on Homo sapiens Genome (SAFA-HsG)" was developed using R programming-based bioconductor packages and shiny framework. Through the app, preliminary descriptive data analysis on nucleotide frequency, and CpG island, CpG non-island, and CpG island shores and shelves (downstream and upstream) of human reference genome can be carried out, which will help biologists to work on human epigenomics. Table view of these analyses of all chromosomes can be visualized and downloaded by the end users. Similarly, the respective comparative plots can be used for CpG sites comparison. In addition, to introduce the personal genome project, the present study has done a preliminary work on few raw data and are included in the app, which will create interest on personal genome information. The app is hosted on https://SAFA-HsG.shinyapps.io/home/. It is a multi-platform application and can be initiated locally from any computer that has or has not installed R. It is a user-friendly interface, which will allow a biologist, even who has little computer knowledge to access and analyze further.

A two-tiered curriculum to improve data management practices for researchers

BACKGROUND

Better research data management (RDM) provides the means to analyze data in new ways, effectively build on another researcher's results, and reproduce the results of an experiment. Librarians are recognized by many as a potential resource for assisting researchers in this area, however this potential has not been fully realized in the biomedical research community. While librarians possess the broad skill set needed to support RDM, they often lack specific knowledge and time to develop an appropriate curriculum for their research community. The goal of this project was to develop and pilot educational modules for librarians to learn RDM and a curriculum for them to subsequently use to train their own research communities.

MATERIALS AND METHODS

We created online modules for librarians that address RDM best practices, resources and regulations, as well as the culture and practice of biomedical research. Data was collected from librarians through questions embedded in the online modules on their self-reported changes in understanding of and comfort level with RDM using a retrospective pre-post design. We also developed a Teaching Toolkit which consists of slides, a script, and an evaluation form for librarians to use to teach an introductory RDM class to researchers at their own institutions. Researchers' satisfaction with the class and intent to use the material they had learned was collected. Actual changes in RDM practices by researchers who attended was assessed with a follow-up survey administered seven months after the class.

RESULTS AND DISCUSSION

The online curriculum increased librarians' self-reported understanding of and comfort level with RDM. The Teaching Toolkit, when employed by librarians to teach researchers in person, resulted in improved RDM practices. This two-tiered curriculum provides concise training and a ready-made curriculum that allows working librarians to quickly gain an understanding of RDM, and translate this knowledge to researchers through training at their own institutions.

Responsible Data Governance of Neuroscience Big Data

Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of "responsible data governance," applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP).

DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles

Vast amounts of clinical and biomedical research data are produced daily. These data can help enable data driven healthcare through novel biomedical discoveries, improved diagnostics processes, epidemiology, and education. However, finding, and gaining access to these data and relevant metadata that are necessary to achieve these goals remains a challenge. Furthermore, data management and enabling widespread, albeit controlled, use poses a major challenge for data producers. These data sources are often geographically distributed, with diverse characteristics, and are controlled by a host of logistical and legal factors that require appropriate governance and access control guarantees. To overcome these obstacles, a set of guiding principles under the term FAIR has been previously introduced. The primary desirable dataset properties are thus that the data should be Findable, Accessible, Interoperable, and Reusable (FAIR). In this paper, we introduce and describe an abstract framework that models these ideal goals, and could be a step toward supporting data driven research. We also develop a system instantiated on our framework called the Data integration and indexing System (DiiS). The system provides an integration model for making healthcare data available on a global scale. Our research work describes the challenges inhibiting data producers, data stewards, and data brokers in achieving FAIR goals for sharing biomedical data. We attempt to address some of the key challenges through the proposed system. We evaluated our framework using the software architecture testing technique and also looked at how different challenges in data integration are addressed by our system. Our evaluation shows that the DiiS framework is a user friendly data integration system that would greatly contribute to biomedical research.

Cardiac Networks United: an integrated paediatric and congenital cardiovascular research and improvement network

Optimising short- and long-term outcomes for children and patients with CHD depends on continued scientific discovery and translation to clinical improvements in a coordinated effort by multiple stakeholders. Several challenges remain for clinicians, researchers, administrators, patients, and families seeking continuous scientific and clinical advancements in the field. We describe a new integrated research and improvement network - Cardiac Networks United - that seeks to build upon the experience and success achieved to-date to create a new infrastructure for research and quality improvement that will serve the needs of the paediatric and congenital heart community in the future. Existing gaps in data integration and barriers to improvement are described, along with the mission and vision, organisational structure, and early objectives of Cardiac Networks United. Finally, representatives of key stakeholder groups - heart centre executives, research leaders, learning health system experts, and parent advocates - offer their perspectives on the need for this new collaborative effort.

Advances in distributed computing with modern drug discovery

INTRODUCTION

Computational chemistry dramatically accelerates the drug discovery process and high-performance computing (HPC) can be used to speed up the most expensive calculations. Supporting a local HPC infrastructure is both costly and time-consuming, and, therefore, many research groups are moving from in-house solutions to remote-distributed computing platforms. Areas covered: The authors focus on the use of distributed technologies, solutions, and infrastructures to gain access to HPC capabilities, software tools, and datasets to run the complex simulations required in computational drug discovery (CDD). Expert opinion: The use of computational tools can decrease the time to market of new drugs. HPC has a crucial role in handling the complex algorithms and large volumes of data required to achieve specificity and avoid undesirable side-effects. Distributed computing environments have clear advantages over in-house solutions in terms of cost and sustainability. The use of infrastructures relying on virtualization reduces set-up costs. Distributed computing resources can be difficult to access, although web-based solutions are becoming increasingly available. There is a trade-off between cost-effectiveness and accessibility in using on-demand computing resources rather than free/academic resources. Graphics processing unit computing, with its outstanding parallel computing power, is becoming increasingly important.

DNA sequences alignment in multi-GPUs: acceleration and energy payoff

Background

We present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal pairwise alignment of huge DNA sequences in multi-GPU platforms using the exact Smith-Waterman method.

Results

Our study includes acceleration factors, performance, scalability, power efficiency and energy costs. We also quantify the influence of the contents of the compared sequences, identify potential scenarios for energy savings on speculative executions, and calculate performance and energy usage differences among distinct GPU generations and models. For a sequence alignment on chromosome-wide scale (around 2 Petacells), we are able to reduce execution times from 9.5 h on a Kepler GPU to just 2.5 h on a Pascal counterpart, with energy costs cut by 60%.

Conclusions

We find GPUs to be an order of magnitude ahead in performance per watt compared to Xeon Phis. Finally, versus typical low-power devices like FPGAs, GPUs keep similar GFLOPS/w ratios in 2017 on a five times faster execution.

An infrastructure for precision medicine through analysis of big data

BACKGROUND

Nowadays, the increasing availability of omics data, due to both the advancements in the acquisition of molecular biology results and in systems biology simulation technologies, provides the bases for precision medicine. Success in precision medicine depends on the access to healthcare and biomedical data. To this end, the digitization of all clinical exams and medical records is becoming a standard in hospitals. The digitization is essential to collect, share, and aggregate large volumes of heterogeneous data to support the discovery of hidden patterns with the aim to define predictive models for biomedical purposes. Patients' data sharing is a critical process. In fact, it raises ethical, social, legal, and technological issues that must be properly addressed.

RESULTS

In this work, we present an infrastructure devised to deal with the integration of large volumes of heterogeneous biological data. The infrastructure was applied to the data collected between 2010-2016 in one of the major diagnostic analysis laboratories in Italy. Data from three different platforms were collected (i.e., laboratory exams, pathological anatomy exams, biopsy exams). The infrastructure has been designed to allow the extraction and aggregation of both unstructured and semi-structured data. Data are properly treated to ensure data security and privacy. Specialized algorithms have also been implemented to process the aggregated information with the aim to obtain a precise historical analysis of the clinical activities of one or more patients. Moreover, three Bayesian classifiers have been developed to analyze examinations reported as free text. Experimental results show that the classifiers exhibit a good accuracy when used to analyze sentences related to the sample location, diseases presence and status of the illnesses.

CONCLUSIONS

The infrastructure allows the integration of multiple and heterogeneous sources of anonymized data from the different clinical platforms. Both unstructured and semi-structured data are processed to obtain a precise historical analysis of the clinical activities of one or more patients. Data aggregation allows to perform a series of statistical assessments required to answer complex questions that can be used in a variety of fields, such as predictive and precision medicine. In particular, studying the clinical history of patients that have developed similar pathologies can help to predict or individuate markers able to allow an early diagnosis of possible illnesses.

Data challenges of biomedical researchers in the age of omics

BACKGROUND

High-throughput technologies are rapidly generating large amounts of diverse omics data. Although this offers a great opportunity, it also poses great challenges as data analysis becomes more complex. The purpose of this study was to identify the main challenges researchers face in analyzing data, and how academic libraries can support them in this endeavor.

METHODS

A multimodal needs assessment analysis combined an online survey sent to 860 Yale-affiliated researchers (176 responded) and 15 in-depth one-on-one semi-structured interviews. Interviews were recorded, transcribed, and analyzed using NVivo 10 software according to the thematic analysis approach.

RESULTS

The survey response rate was 20%. Most respondents (78%) identified lack of adequate data analysis training (e.g., R, Python) as a main challenge, in addition to not having the proper database or software (54%) to expedite analysis. Two main themes emerged from the interviews: personnel and training needs. Researchers feel they could improve data analyses practices by having better access to the appropriate bioinformatics expertise, and/or training in data analyses tools. They also reported lack of time to acquire expertise in using bioinformatics tools and poor understanding of the resources available to facilitate analysis.

CONCLUSIONS

The main challenges identified by our study are: lack of adequate training for data analysis (including need to learn scripting language), need for more personnel at the University to provide data analysis and training, and inadequate communication between bioinformaticians and researchers. The authors identified the positive impact of medical and/or science libraries by establishing bioinformatics support to researchers.

Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files

Background

Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. Distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance.

Findings

In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)-based high-performance computing (HPC) implementation, and the popular VCFTools.

Conclusions

Our experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems.

Analysis of an Internet Community about Pneumothorax and the Importance of Accurate Information about the Disease

Background

The huge improvements in the speed of data transmission and the increasing amount of data available as the Internet has expanded have made it easy to obtain information about any disease. Since pneumothorax frequently occurs in young adolescents, patients often search the Internet for information on pneumothorax.

Methods

This study analyzed an Internet community for exchanging information on pneumothorax, with an emphasis on the importance of accurate information and doctors’ role in providing such information.

Results

This study assessed 599,178 visitors to the Internet community from June 2008 to April 2017. There was an average of 190 visitors, 2.2 posts, and 4.5 replies per day. A total of 6,513 posts were made, and 63.3% of them included questions about the disease. The visitors mostly searched for terms such as ‘pneumothorax,’ ‘recurrent pneumothorax,’ ‘pneumothorax operation,’ and ‘obtaining a medical certification of having been diagnosed with pneumothorax.’ However, 22% of the pneumothorax-related posts by visitors contained inaccurate information.

Conclusion

Internet communities can be an important source of information. However, incorrect information about a disease can be harmful for patients. We, as doctors, should try to provide more in-depth information about diseases to patients and to disseminate accurate information about diseases in Internet communities.

From systems biology to P4 medicine: applications in respiratory medicine

Human health and disease are emergent properties of a complex, nonlinear, dynamic multilevel biological system: the human body. Systems biology is a comprehensive research strategy that has the potential to understand these emergent properties holistically. It stems from advancements in medical diagnostics, “omics” data and bioinformatic computing power. It paves the way forward towards “P4 medicine” (predictive, preventive, personalised and participatory), which seeks to better intervene preventively to preserve health or therapeutically to cure diseases. In this review, we: 1) discuss the principles of systems biology; 2) elaborate on how P4 medicine has the potential to shift healthcare from reactive medicine (treatment of illness) to predict and prevent illness, in a revolution that will be personalised in nature, probabilistic in essence and participatory driven; 3) review the current state of the art of network (systems) medicine in three prevalent respiratory diseases (chronic obstructive pulmonary disease, asthma and lung cancer); and 4) outline current challenges and future goals in the field.

Systems biology and network medicine have the potential to transform medical research and practicehttp://ow.ly/r3jR30hf35x

A Systematic Review of Techniques and Sources of Big Data in the Healthcare Sector

The main objective of this paper is to present a review of existing researches in the literature, referring to Big Data sources and techniques in health sector and to identify which of these techniques are the most used in the prediction of chronic diseases. Academic databases and systems such as IEEE Xplore, Scopus, PubMed and Science Direct were searched, considering the date of publication from 2006 until the present time. Several search criteria were established as 'techniques' OR 'sources' AND 'Big Data' AND 'medicine' OR 'health', 'techniques' AND 'Big Data' AND 'chronic diseases', etc. Selecting the paper considered of interest regarding the description of the techniques and sources of Big Data in healthcare. It found a total of 110 articles on techniques and sources of Big Data on health from which only 32 have been identified as relevant work. Many of the articles show the platforms of Big Data, sources, databases used and identify the techniques most used in the prediction of chronic diseases. From the review of the analyzed research articles, it can be noticed that the sources and techniques of Big Data used in the health sector represent a relevant factor in terms of effectiveness, since it allows the application of predictive analysis techniques in tasks such as: identification of patients at risk of reentry or prevention of hospital or chronic diseases infections, obtaining predictive models of quality.

Proteomic profile of seminal plasma in adolescents and adults with treated and untreated varicocele

Varicocele, the most important treatable cause of male infertility, is present in 15% of adult males, 35% of men with primary infertility, and 80% of men with secondary infertility. On the other hand, 80% of these men will not present infertility. Therefore, there is a need to differentiate a varicocele that is exerting a deleterious effect that is treatable from a “silent” varicocele. Despite the growing evidence of the cellular effects of varicocele, its underlying molecular mechanisms are still eluding. Proteomics has become a promising area to determine the reproductive biology of semen as well as to improve diagnosis of male infertility. This review aims to discuss the state-of-art in seminal plasma proteomics in patients with varicocele to discuss the challenges in undertaking these studies, as well as the future outlook derived from the growing body of evidence on the seminal proteome.

Status and Future Perspectives of Utilizing Big Data in Neurosurgical and Stroke Research

The management, analysis, and integration of Big Data have received increasing attention in healthcare research as well as in medical bioinformatics. The J-ASPECT study is the first nationwide survey in Japan on the real-world setting of stroke care using data obtained from the diagnosis procedure combination-based payment system. The J-ASPECT study demonstrated a significant association between comprehensive stroke care (CSC) capacity and the hospital volume of stroke interventions in Japan; further, it showed that CSC capabilities were associated with reduced in-hospital mortality rates. Our study aims to create new evidence and insight from ‘real world’ neurosurgical practice and stroke care in Japan using Big Data. The final aim of this study is to develop effective methods to bridge the evidence-practice gap in acute stroke healthcare. In this study, the authors describe the status and future perspectives of the development of a new method of stroke registry as a powerful tool for acute stroke care research.

Report of the National Heart, Lung, and Blood Institute Working Group: An Integrated Network for Congenital Heart Disease Research

The National Heart, Lung, and Blood Institute convened a working group in January 2015 to explore issues related to an integrated data network for congenital heart disease research. The overall goal was to develop a common vision for how the rapidly increasing volumes of data captured across numerous sources can be managed, integrated, and analyzed to improve care and outcomes. This report summarizes the current landscape of congenital heart disease data, data integration methodologies used across other fields, key considerations for data integration models in congenital heart disease, and the short- and long-term vision and recommendations made by the working group.

Anaplasma marginale: Diversity, Virulence, and Vaccine Landscape through a Genomics Approach

In order to understand the genetic diversity of A. marginale, several efforts have been made around the world. This rickettsia affects a significant number of ruminants, causing bovine anaplasmosis, so the interest in its virulence and how it is transmitted have drawn interest not only from a molecular point of view but also, recently, some genomics research have been performed to elucidate genes and proteins with potential as antigens. Unfortunately, so far, we still do not have a recombinant anaplasmosis vaccine. In this review, we present a landscape of the multiple approaches carried out from the genomic perspective to generate valuable information that could be used in a holistic way to finally develop an anaplasmosis vaccine. These approaches include the analysis of the genetic diversity of A. marginale and how this affects control measures for the disease. Anaplasmosis vaccine development is also reviewed from the conventional vaccinomics to genome-base vaccinology approach based on proteomics, metabolomics, and transcriptomics analyses reported. The use of these new omics approaches will undoubtedly reveal new targets of interest in the near future, comprising information of potential antigens and the immunogenic effect of A. marginale proteins.

Big Data in Health: a Literature Review from the Year 2005

The information stored in healthcare systems has increased over the last ten years, leading it to be considered Big Data. There is a wealth of health information ready to be analysed. However, the sheer volume raises a challenge for traditional methods. The aim of this article is to conduct a cutting-edge study on Big Data in healthcare from 2005 to the present. This literature review will help researchers to know how Big Data has developed in the health industry and open up new avenues for research. Information searches have been made on various scientific databases such as Pubmed, Science Direct, Scopus and Web of Science for Big Data in healthcare. The search criteria were "Big Data" and "health" with a date range from 2005 to the present. A total of 9724 articles were found on the databases. 9515 articles were discarded as duplicates or for not having a title of interest to the study. 209 articles were read, with the resulting decision that 46 were useful for this study. 52.6 % of the articles used were found in Science Direct, 23.7 % in Pubmed, 22.1 % through Scopus and the remaining 2.6 % through the Web of Science. Big Data has undergone extremely high growth since 2011 and its use is becoming compulsory in developed nations and in an increasing number of developing nations. Big Data is a step forward and a cost reducer for public and private healthcare.

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.

Molecular Pathology Informatics

Molecular informatics (MI) is an evolving discipline that will support the dynamic landscape of molecular pathology and personalized medicine. MI provides a fertile ground for development of clinical solutions to bridge the gap between clinical informatics and bioinformatics. Rapid adoption of next generation sequencing (NGS) in the clinical arena has triggered major endeavors in MI that are expected to bring a paradigm shift in the practice of pathology. This brief review presents a broad overview of various aspects of MI, particularly in the context of NGS based testing.

Lynx: Automatic Elderly Behavior Prediction in Home Telecare

This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder's daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user's health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.

eHealth: past and future perspectives

eHealth is an umbrella term incorporating any area that combines healthcare and technology to improve efficiencies and reduce costs. The ultimate goal of eHealth is to rationalize treatment selection to improve patient safety and outcomes. Telemedicine, first used in the 1920s, is the oldest form of eHealth. The introduction of broadband Internet, followed by wireless technologies, has allowed an explosion of mHealth applications within this field. Wearable technologies, such as smartwatches, are now being used for diagnostics and patient monitoring. Challenges remain to develop reusable Clinical Decision Support systems that will streamline the flow of data from clinical laboratories to point of care. This review explores the history of eHealth, and describes some of the remaining integration and implementation challenges.

Proteomic profile of seminal plasma in adolescents and adults with treated and untreated varicocele

Varicocele, the most important treatable cause of male infertility, is present in 15% of adult males, 35% of men with primary infertility, and 80% of men with secondary infertility. On the other hand, 80% of these men will not present infertility. Therefore, there is a need to differentiate a varicocele that is exerting a deleterious effect that is treatable from a "silent" varicocele. Despite the growing evidence of the cellular effects of varicocele, its underlying molecular mechanisms are still eluding. Proteomics has become a promising area to determine the reproductive biology of semen as well as to improve diagnosis of male infertility. This review aims to discuss the state-of-art in seminal plasma proteomics in patients with varicocele to discuss the challenges in undertaking these studies, as well as the future outlook derived from the growing body of evidence on the seminal proteome.

Evolving Roles for Physicians and Genetic Counselors in Managing Complex Genetic Disorders

Proponents of personalized medicine predict that genetic information will provide pivotal perspectives for the prevention and management of complex disorders. Personalized medicine differs from traditional Western medicine, in that it focuses on more complex disorders that require mechanistic disease modeling and outcome simulation by integrating genomic risk, environmental stressors, and biomarkers as indicators of disease state. This information could be useful to guide targeted therapy and prevent pathologic outcomes. However, gaps exist in the process of linking the pieces together; currently, genetic data are seldom used to assist physicians in clinical decision making. With rapid growth in genetic data and the requirements for new paradigms for complex disorders comes the need to train professionals to understand and manage the impact of genetic information on patients within these clinical settings. Here we describe the challenges, controversies, and opportunities for genetics and genetic counselors in managing complex disorders and discuss the rationale for modifications in genetic counselor training and function. We conclude that a major paradigm shift is underway and a compelling functional, ethical, and financial argument can be made for employing properly trained genetic counselors to be strategically positioned within the health-care industries that are responsible for managing complex disorders.

Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data

Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on expert-labeled VS data streams to automatically classify VS alerts as real or artifact, thereby "cleaning" such data for future modeling. 634 admissions to a step-down unit had recorded continuous noninvasive VS monitoring data [heart rate (HR), respiratory rate (RR), peripheral arterial oxygen saturation (SpO₂) at 1/20 Hz, and noninvasive oscillometric blood pressure (BP)]. Time data were across stability thresholds defined VS event epochs. Data were divided Block 1 as the ML training/cross-validation set and Block 2 the test set. Expert clinicians annotated Block 1 events as perceived real or artifact. After feature extraction, ML algorithms were trained to create and validate models automatically classifying events as real or artifact. The models were then tested on Block 2. Block 1 yielded 812 VS events, with 214 (26 %) judged by experts as artifact (RR 43 %, SpO₂ 40 %, BP 15 %, HR 2 %). ML algorithms applied to the Block 1 training/cross-validation set (tenfold cross-validation) gave area under the curve (AUC) scores of 0.97 RR, 0.91 BP and 0.76 SpO₂. Performance when applied to Block 2 test data was AUC 0.94 RR, 0.84 BP and 0.72 SpO₂. ML-defined algorithms applied to archived multi-signal continuous VS monitoring data allowed accurate automated classification of VS alerts as real or artifact, and could support data mining for future model building.

Bioinformatics in Africa: The Rise of Ghana?

Until recently, bioinformatics, an important discipline in the biological sciences, was largely limited to countries with advanced scientific resources. Nonetheless, several developing countries have lately been making progress in bioinformatics training and applications. In Africa, leading countries in the discipline include South Africa, Nigeria, and Kenya. However, one country that is less known when it comes to bioinformatics is Ghana. Here, I provide a first description of the development of bioinformatics activities in Ghana and how these activities contribute to the overall development of the discipline in Africa. Over the past decade, scientists in Ghana have been involved in publications incorporating bioinformatics analyses, aimed at addressing research questions in biomedical science and agriculture. Scarce research funding and inadequate training opportunities are some of the challenges that need to be addressed for Ghanaian scientists to continue developing their expertise in bioinformatics.

Can linking databases answer questions about paediatric heart failure?

Numerous data sets collect information on patients with paediatric cardiovascular disease, including paediatric heart failure and transplant patients. This review discusses methodologies available for linking and integrating information across data sets, which may help facilitate answering important questions in the field of paediatric heart failure and transplant that cannot be answered with individual data sets or single-centre data alone.

ONION: Functional Approach for Integration of Lipidomics and Transcriptomics Data

To date, the massive quantity of data generated by high-throughput techniques has not yet met bioinformatics treatment required to make full use of it. This is partially due to a mismatch in experimental and analytical study design but primarily due to a lack of adequate analytical approaches. When integrating multiple data types e.g. transcriptomics and metabolomics, multidimensional statistical methods are currently the techniques of choice. Typical statistical approaches, such as canonical correlation analysis (CCA), that are applied to find associations between metabolites and genes are failing due to small numbers of observations (e.g. conditions, diet etc.) in comparison to data size (number of genes, metabolites). Modifications designed to cope with this issue are not ideal due to the need to add simulated data resulting in a lack of p-value computation or by pruning of variables hence losing potentially valid information. Instead, our approach makes use of verified or putative molecular interactions or functional association to guide analysis. The workflow includes dividing of data sets to reach the expected data structure, statistical analysis within groups and interpretation of results. By applying pathway and network analysis, data obtained by various platforms are grouped with moderate stringency to avoid functional bias. As a consequence CCA and other multivariate models can be applied to calculate robust statistics and provide easy to interpret associations between metabolites and genes to leverage understanding of metabolic response. Effective integration of lipidomics and transcriptomics is demonstrated on publically available murine nutrigenomics data sets. We are able to demonstrate that our approach improves detection of genes related to lipid metabolism, in comparison to applying statistics alone. This is measured by increased percentage of explained variance (95% vs. 75–80%) and by identifying new metabolite-gene associations related to lipid metabolism.