This journal article in PLOS Computational Biology‘s long-running Ten Simple Rules series goes back to 2013, when a collaborative group of eight authors from around the globe pooled their thoughts together on the topic of open science and collaborative R&D. The conversations (linked to in this article) provide context and insight into the various projects — from the Gene Wiki initiative to the Open Source Drug Discovery (OSDD) project — that have required significant deviation of thought from the traditional company view of conducting business. From “lead as a coach, not a CEO” to “grow the commons,” the article’s authors provide their thoughts on what best makes for collaborative and open science projects.
In yet another installment of PLOS Computational Biology‘s Ten Simple Rules series, Boland et al. of Columbia University and the Broad Institute of MIT and Harvard share their thoughts and experiences with multi-site collaborations and data sharing. The group provides practical tips for making data sharing easier and more successful, strengthening collaborations and the scientific process.
This is another entry in PLOS Computational Biology‘s long-running Ten Simple Rules series, which attempts to break down computational biology / bioinformatics topics (that relate to the informatics side) down into a digestible and cited format. This 2017 entry by List et al. looks at the typical problems associated with computational biology software development and attempts to provide a clear approach for more usable, efficient software. The authors conclude that despite following these 10 rules, there’s more to be done: “…effort is required from both users and developers to further improve a tool. Even engaging with only a few users … is likely to have a large impact on usability.”
In this brief paper by Rumbold and Pierscionek, the implications and theoretical impact of the European Union’s General Data Protection Regulation are discussed. Addressing in particular claims that the new “consent requirements … would severely restrict medical data research,” the researchers break down the law that goes into effect in 2018, including anonymization issues, consent issues, and data sharing issues that will potentially affect biomedical data research. They conclude the impact will by minimal: “The GDPR will facilitate medical research, except where it is research not considered in the public interest. In that case, more demanding requirements for anonymization will entail either true anonymization or consent.”
Methods for specifying scientific data standards and modeling relationships with applications to neuroscience
Neuroscience, like so many fields of science, is swimming in data, much of it in differing formats. This creates barriers to data sharing and project enactment. Rübel et al. argue that standardization of neuroscience data formats can improve analysis and sharing efforts. “Arguably, the focus of a neuroscience data standard should be on addressing the application-centric needs of organizing scientific data and metadata, rather than on reinventing file storage methods,” they state. This late 2016 paper, published in Frontiers of Neuroinformatics, details their effort to make such a standardized framework, called BRAINformat, one that “fill[s] important gaps in the portfolio of available tools for creating advanced standards for modern scientific data.”
This 2017 paper by University of Colorado’s Siri Jodha Singh Khalsa, published in Data Science Journal, provides background on the successes, challenges, and outcomes of the Brokering Building Block (BCube) project, which aims “to provide [geo]scientists, policy makers and the public with computing resources, analytic tools and educational material, all within an open, interconnected and collaborative environment.” It describes the processes of infrastructure development, interoperability design, data testing, and lessons learned from the process, including an analysis of the human elements involved in making data sharing easier and more profound.
Turnkey data repositories such as DSpace have been evolving over the past decade, from housing publication preprints and postprints to today handling actual data management tasks of research. But what if this evolving technology could further be improved “to improve the discoverability of the deposited data”? Harvey et al. of the Imperial College London explored this topic in their 2017 paper published in Journal of Cheminformatics, developing new insights into repository design and DataCite metadata schemes. They published their results hoping that it “may in turn assist researchers wishing to deposit data in identifying the repository attributes that can best expose the discoverability and re-use of their data.”
Lukauskas et al. present their open-source software package DGW (Dynamic Gene Warping) in this December 2016 paper published in BMC Informatics. Used for the “simultaneous alignment and clustering of multiple epigenomic marks,” the software uses a process called dynamic time warping (DTW) to capture epigenomic mark structure. The authors conclude that their research shows “that DGW can be a practical and user-friendly tool for exploratory data analysis of high-throughput epigenomic data sets” and demonstrates “potential as a useful tool for biological hypothesis generation.”
In this short paper published in December 2016, Hiner et al. of the University of Wisconsin at Madison demonstrate their open-source library SCIFIO (SCientific Image Format Input and Output). Built on inspiration from the Bio-Formats library for microscopy image data, SCIFIO attempts to act as “a domain-independent image I/O framework enabling seamless and extensible translation between image metadata models.” Rather than fight with the difficulties of repeating experiments based on data in proprietary formats, SCIFIO’s open-source nature help with reproducibility of research results and proves “capable of adapting to the demands of scientific imaging analysis.”
As technology progresses, it allows bioinformaticians to improve the efficiency of their data processing tools and provide better solutions for patient care. As this December 2016 paper by Schulz et al. points out, one way in which dramatic change can potentially occur in science’s big data management is through the use of application virtualization. The researchers, based out of Yale, attempt to “demonstrate the potential benefits of containerized applications and application workflows for computational genomics research.” They conclude this technology has the potential to “improve pipeline and experimental reproducibility since preconfigured applications can be readily deployed to nearly any host system.”
This brief non-peer-reviewed article by Sanjay Joshi, Isilon CTO of Healthcare and Life Sciences at the Dell EMC Emerging Technologies Team, looks at the global state of imaging in oncology clinical trials. His message? “[C]linical trials need to scale this critical imaging infrastructure component (the VNA) globally as a value-add and integrate it with clinical trials standards like the Clinical Data Interchange Standards Consortium (CDISC) along with the large ecosystem of applications that manage trials.” In other words, standards, security, and scale are as important as ever in dealing with data, and clinical imaging is no exception.
Informatics metrics and measures for a smart public health systems approach: Information science perspective
In this 2017 paper published in Computational and Mathematical Methods in Medicine, Carney and Shea of the Gillings School of Global Public Health at University of North Carolina – Chapel Hill take a closer look at what drives intelligent public health system characteristics, and they provide insights into measures and capabilities vital to the public health informatician. They conclude that “[a] common set of analytic measures and capabilities that can drive efficiency and viable models can demonstrate how incremental changes in smartness generate corresponding changes in public health performance.” This work builds on existing literature and seeks “to establish standardized measures for smart, learning, and adaptive public health systems.”
Hartzband and Jacobs of the RCHN Community Health Foundation, a U.S. non-profit dedicated to support the operations of community health centers (CHCs), provide the results of their data analysis of CHCs and EHR data in this 2016 paper published in the Online Journal of Public Health Informatics. Hoping “[t]o better understand existing capacity and help organizations plan for the strategic and expanded uses of data” for the support of operations and clinical practice, the researchers looked at CHCs’ EHR-derived and stack analytic results. The concluded that “[d]ata awareness … needs to be prioritized and developed by health centers and other healthcare organizations if analytics are to be used in an effective manner to support strategic objectives.”
In this short paper, Williams et al. of the University of Michigan provide a brief technical view of microservices and how they have the potential to improve the organization and use of bioinformatics and other healthcare applications. They propose that “a well-established software design and deployment strategy” that uses micorservices framework can improve the collaborative and patient-focused efforts of researchers and laboratorians everywhere. They conclude that bioinformaticians, pathologists, and other laboratorians “can contain ever-expanding IT costs, reduce the likelihood of IT implementation mishaps and failures, and perhaps most importantly, greatly elevate the level of service” with properly implemented microservice-based versions of the software they use.
In this brief paper published in 2016, Wolske and Rhinesmith present what they call “a set of critical questions” for guiding those delving into a community informatics (CI) project. Properly using technology that allows collaboration, popular education, and asset development tools, community informatics projects are able to carry on with the primary goal of sustainably supporting community development projects. However, the authors argue, a set of ethical questions should be asked to drive the planning, development, and implementation of said CI projects. These questions, presented in this paper, have the potential to better “guide the evolution of ethical community informatics in practice, as well as the personal transformation of CI practitioners who seek to embrace all as equals and experts.”
In this 2016 article published in JMIR Medical Informatics, Kruse et al. of the Texas State University present the results of a systematic review of articles and studies involving big data in the health care sphere. From this review the team identified nine challenges and 11 opportunities that big data brings to health care. The group describes these challenges and opportunities, concluding that either way “the vast amounts of information generated annually within health care must be organized and compartmentalized to enable universal accessibility and transparency between health care organizations.”
The impact of electronic health record (EHR) interoperability on immunization information system (IIS) data quality
Moving data between systems via an electronic exchange (interoperability) while keeping it clean is always a challenge. Data exchanged between electronic health records (EHR) and immunization information system (IIS) is no exception, as Woinarowicz and Howell demonstrate in this 2016 paper published in Online Journal of Public Health Informatics. Working for the North Dakota Department of Health, Division of Disease Control, the duo explain how setting up their IIS for interoperability with provider EHRs “has had an impact on NDIIS data quality.” They conclude: “Timeliness of data entry has improved and overall doses administered have remained fairly consistent, as have the immunization rates … [but more] will need to be done by NDIIS staff and its vendor to help reduce the negative impact of duplicate record creation, as well as data completeness.”
Bioinformatics workflow for clinical whole genome sequencing at Partners HealthCare Personalized Medicine
Genomic data is increasingly used to provide better, more focused clinical care. Or course, its associated datasets can be large, and it can take significant processing power to utilize and manage effectively. In this 2016 paper published in Journal of Personalized Medicine, Tsai et al. of Partners Healthcare describe their “bioinformatics strategy to efficiently process and deliver genomic data to geneticists for clinical interpretation.” They conclude that with more research comes improved workflows and “filtrations that include larger portions of the non-coding regions as they start to have utility in the clinical setting, ultimately enabling the full utility of complete genome sequencing.”
Pathology report data extraction from relational database using R, with extraction from reports on melanoma of skin as an example
Synoptic reporting is an important part of not only managing patient testing information but also reporting that data for research and data mining purposes. As such, the extraction of particular elements from these types of reports — including those recording major cancer information — has historically been difficult. Recent developments in extracting key information from synoptic reports have made it easier, however, including the use of the R programming language. In this paper by J.J. Ye, the process of extracting melanoma data from pathology datasets is used to describe a broader, wide-ranging application of R and the associated RODBC package for extracting useful data from synoptic reports. Ye concludes: “This approach can be easily modified and adopted for other pathology information systems that use relational database for data management.”
In this 2016 paper published in Data Science Journal, Australian researcher James Hester provides “a simple formal approach when developing and working with data standards.” Using ontology logs or “ologs” — category-theoretic box and arrow diagrams that visually explains mapping elements of sets — and friendly file formats, adapters, and modules, Hester presents several applications towards a useful scientific data transfer network. He concludes: “These ontologies nevertheless capture all scientifically-relevant prior knowledge, and when expressed in machine-readable form are sufficiently expressive to mediate translation between legacy and modern data formats.” This results in “a modular, universal data file input and translation system [that] can be implemented without the need for an intermediate format to be defined.”