Pediatric biomedical informatics

Translational Bioinformatics 10 Series Editor: Xiangdong Wang, MD, Ph.D John J. Hutton Editor Pediatric Biomedical Informatics Computer Applications in Pediatric Research Second Edition Translational Bioinformatics Volume 10 Series editor Xiangdong Wang, MD, Ph.D Professor of Medicine, Executive Director of Zhongshan Hospital Institute of Clinical Science, Fudan University Shanghai Medical College, Shanghai, China Director of Shanghai Institute of Clinical Bioinformatics, (www.fuccb.org) Aims and Scope The Book Series in Translational Bioinformatics is a powerful and integrative resource for understanding and translating discoveries and advances of genomic, transcriptomic, proteomic and bioinformatic technologies into the study of human diseases The Series represents leading global opinions on the translation of bioinformatics sciences into both the clinical setting and descriptions to medical informatics It presents the critical evidence to further understand the molecular mechanisms underlying organ or cell dysfunctions in human diseases, the results of genomic, transcriptomic, proteomic and bioinformatic studies from human tissues dedicated to the discovery and validation of diagnostic and prognostic disease biomarkers, essential information on the identification and validation of novel drug targets and the application of tissue genomics, transcriptomics, proteomics and bioinformatics in drug efficacy and toxicity in clinical research The Book Series in Translational Bioinformatics focuses on outstanding articles/chapters presenting significant recent works in genomic, transcriptomic, proteomic and bioinformatic profiles related to human organ or cell dysfunctions and clinical findings The Series includes bioinformatics-driven molecular and cellular disease mechanisms, the understanding of human diseases and the improvement of patient prognoses Additionally, it provides practical and useful study insights into and protocols of design and methodology Series Description Translational bioinformatics is defined as the development of storage-related, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data, and genomic data in particular, into proactive, predictive, preventive, and participatory health Translational bioinformatics includes research on the development of novel techniques for the integration of biological and clinical data and the evolution of clinical informatics methodology to encompass biological observations The end product of translational bioinformatics is the newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders including biomedical scientists, clinicians, and patients Issues related to database management, administration, or policy will be coordinated through the clinical research informatics domain Analytic, storage-related, and interpretive methods should be used to improve predictions, early diagnostics, severity monitoring, therapeutic effects, and the prognosis of human diseases Recently Published and Forthcoming Volumes Genomics and Proteomics for Clinical Discovery and Development Editor: György Marko-Varga Volume Allergy Bioinformatics Editors: Ailin Tao, Eyal Raz Volume Computational and Statistical Epigenomics Editor: Andrew E Teschendorff Volume Transcriptomics and Gene Regulation Editor: Jiaqian Wu Volume More information about this series at http://www.springer.com/series/11057 John J Hutton Editor Pediatric Biomedical Informatics Computer Applications in Pediatric Research Second Edition Editor John J Hutton (1937–2016) Children’s Hospital Research Foundation Cincinnati, Ohio, USA ISSN 2213-2775 ISSN 2213-2783 (electronic) Translational Bioinformatics ISBN 978-981-10-1102-3 ISBN 978-981-10-1104-7 (eBook) DOI 10.1007/978-981-10-1104-7 Library of Congress Control Number: 2016941065 1st edition: © Springer Science+Business Media Dordrecht 2012 © Springer Science+Business Media Singapore 2016 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Science+Business Media Singapore Pte Ltd Foreword Apps for Pediatrics: Using Informatics to Facilitate, Optimize, and Personalize Care I was born before electronic computers existed Two key tools of my early educational years were a slide rule and a typewriter Just as I emerged from clinical training as a pediatric cardiologist and research training in biochemistry in 1977, calculators and word processors became tools to facilitate clinical care, laboratory research, and medical and scientific communication I was fascinated by Mendelian disorders and wondered why congenital heart defects sometimes ran in families, but DNA sequencing and other tools to approach genetics did not exist Only 40 years later, medicine, including pediatrics, and biomedical research, especially genetics and genomics, have been totally changed by biomedical informatics, such that even our American president and other international leaders can propose with confidence initiatives to provide personalized and precision medicine to each individual Even with the next generation of tools, from DNA and RNA sequencing to natural language processing of patient and physician notes through artificial intelligences, however, the essential questions of how to apply these current tools to optimize learning and patient care remain In this second edition of Pediatric Biomedical Informatics, with the guidance and tutelage of John Hutton as editor and with the expertise of the faculty and their colleagues who have contributed chapters, the “apps” and approaches to optimize learning, assess clinical outcomes on a population scale, aggregate genomic data for huge numbers of individuals, and organize all of the “big data” created by patients in electronic medical records are explored and presented The topics discussed include the major core informatics resources needed, from the EMR itself to transmission of information in it for storage and management to security required for patient protection, creation of usable patient data warehouses, and integration of patient information (the phenotype) with biobanked tissue and DNA for research, all critical infrastructure to optimize care and research In addition, some intriguing “apps” in both patient-oriented research and basic science are provided, to illustrate how population-based studies, assessment v vi Foreword of language, support for decisions, and generation of networks can be done These “apps” focus on perinatal, neonatal, and pediatric needs An emphasis on larger, multi-institutional networks and distributed research networks is apparent and essential if we are to more quickly assess the genomics, epigenomics, environmental impact, and treatment outcomes of the relatively rare disorders that we see in pediatrics Biomedical informatics is the key to our future, as we integrate clinical care, genomics, and basic science to improve outcomes and discover new therapeutics With careful design and acquisition of information, we can tame the avalanche of data We can link and integrate data across institutions to achieve greater power of analysis and increase the speed of discovery and evaluation of treatments This book provides insight into how to use data to benefit children around our world through “apps” for pediatrics Department of Pediatrics University of Cincinnati College of Medicine Cincinnati, OH, USA Cincinnati Children’s Research Foundation Cincinnati Children’s Hospital Medical Center Cincinnati, OH, USA Arnold W Strauss In Memoriam John J Hutton, MD, a pioneer and visionary leader across the gamut of Biomedical Research for nearly 50 years, died on June 19, 2016, after a brief but frightening and rapidly progressive form of Amyotrophic Lateral Sclerosis that began to appear over his last year Dr Hutton brought enthusiasm, energy, and effectiveness to virtually all his endeavors, and as some of his physical means were becoming difficult, his energy was particularly fierce about finishing this very book For this edition, he was passionate that it should address critical issues in biomedical informatics to improve data collection, integration, analysis, discovery, and translation Dr Hutton’s career led him to be both a scientific and administrative leader within and above many groups that had specialization within specific areas across the entire process of biomedical research, clinical care, education, and even how to balance the costs of doing all this He saw the potential of informatics as a natural means of advancing medicine and human health and embraced its mission to build tools, collect and distill data and observations, and to fruitfully carry out, collaborate with, or enable others to perform analyses and propagate significant data and knowledge That achieving these missions could provide resources to entire communities of educators, researchers, practitioners, and the public raised its significance in Dr Hutton’s view And he realized this when he graciously asked me if he could come back to a postdoctoral research project in my computational biology group back in 2003, just after he stepped down after serving as University of Cincinnati Medical School Dean for 15 years After getting a couple of research projects done and published that mapped and analyzed the significance of gene expression and gene regulatory regions associated with immune cells, tissues, and disease states, and taking a few classes in programming, he was ready to take on running my department! And then from 2005 to 2015 he served as Bioinformatics Division Director and Senior Vice President for Information Technology at Children’s Hospital Medical Center, the oversized Pediatrics Department for the College of Medicine A native of eastern Kentucky, Dr Hutton graduated in Physics from Harvard and attended the Rockefeller University and Harvard Medical School where he obtained vii viii In Memoriam an MD degree and completed postgraduate training in internal medicine with a research focus in biochemistry, genetics at the National Heart Lung and Blood Institute, and clinical training in hematology-oncology at the Massachusetts General Hospital From 1968 to 1971 he served as a Section Chief at the Roche Institute for Molecular Biology, then 1971–1980 as Professor and Medical Service Chief at the University of Kentucky, then 1980–1984 as Professor and Associate Chief at the University of Texas Medical Center San Antonio, and from 1983 to 1988 as a member and Chair of the famous NIH Biochemistry Study Section Dr Hutton returned closer to his original Kentucky home in 1984 as the Albert B Sabin Professor and Vice Chairman, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center In 1987, he was appointed Dean of the College of Medicine, and he served in that role up until 2003, also serving national roles in the American Society of Hematology, and as Executive Council Member for the Association of American Medical Colleges All of this experience contributed to his understanding the nature of the multidisciplinary problems that computational biology could, and should, solve Dr Hutton’s research career included editorial oversight of textbooks in internal medicine and pediatric bioinformatics and more than 200 peer-reviewed papers, including among the first trials of gene therapy for inborn immunodeficiency As Dean, he was principal investigator of a multimillion dollar Howard Hughes infrastructure improvement grant, which focused on development of resources in genomics, proteomics, and bioinformatics And after stepping down as Dean, he won a $1.7 million IAIMS grant from NIH/National Library of Medicine, which was awarded to support innovative research in and development of information management systems Other of Dr Hutton’s passions included the College of Medicine’s MD-PhD Physician Scientist Training Program, which was nationally recognized for its high quality and received peer-reviewed funding from the NIH, and also a strong emphasis on the development of programs in Ethics for Medicine and Medical Research An endowed annual Hutton Lectureship was established in Medical Ethics, and an endowed Hutton Chair in Biomedical Informatics was established for Cincinnati Children’s Hospital Research Foundation, and I am extremely honored to be its first recipient Dr Hutton’s family includes his wife, Mary Ellyn, a classical musician who also writes about classical music for the Cincinnati Post and other publications His daughter, Becky, graduated from the UC College of Nursing, married Thomas Fink, has four children, and lives in Tipp City, Ohio His son, John, graduated from Davidson College and the UC College of Medicine, married Sandra Gross, has two children, and lives in Mt Adams His daughter, Elizabeth, graduated from Harvard in 2001, works in Boston, and will enter the Ohio State University College of Law in August 2003 Dr Hutton leaves us all a rich legacy of achievement and inspiration to be and empower the next generation of computationally empowered students, researchers, educators, and practitioners Bruce Aronow, PhD, the John J Hutton, MD Professor of Biomedical Informatics, University of Cincinnati Department of Pediatrics, Department of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center Contents Part I Core Informatics Resources Electronic Health Records in Pediatrics S Andrew Spooner and Eric S Kirkendall Protecting Privacy in the Child Health EHR S Andrew Spooner 27 Standards for Interoperability S Andrew Spooner and Judith W Dexheimer 37 Data Storage and Access Management Michal Kouril and Michael Wagner 57 Institutional Cybersecurity in a Clinical Research Setting Michal Kouril and John Zimmerly 79 Data Governance and Strategies for Data Integration 101 Keith Marsolo and Eric S Kirkendall Laboratory Medicine and Biorepositories 121 Paul E Steele, John A Lynch, Jeremy J Corsmo, David P Witte, John B Harley, and Beth L Cobb Part II Clinical Applications Informatics for Perinatal and Neonatal Research 143 Eric S Hall Clinical Decision Support and Alerting Mechanisms 163 Judith W Dexheimer, Philip Hagedorn, Eric S Kirkendall, Michal Kouril, Thomas Minich, Rahul Damania, Joshua Courter, and S Andrew Spooner ix 20 Functional Genomics of Development 435 morphogen gradients, flanking cell crosstalk, and even stochastic gene expression can regulate decisions that generate diversity form apparent uniformity Nevertheless, to date we have not created a detailed blueprint of this process, at single cell resolution, for any developing organ system For example, consider the early kidney progenitor cells The metanephric mesenchyme is a histologically uniform cloud of cells that will eventually give rise to almost all of the varied cell types of the nephron When the first signs of differentiation into distinct developmental lineages appear within the mesenchyme? Within the early mesenchyme are some cells already lineage primed, predetermined to make specific cell types? Indeed, how varied are the gene expression profiles of the early metanephric mesenchyme? These kinds of questions can only be answered by the analysis of the transcriptional profiles of single cells While the genomics analysis of single cells is clearly highly desirable, there are serious challenges to consider First there is the technical difficulty of producing an accurate gene expression profile from the extremely small quantity of RNA present in a single cell Total RNA content per cell depends on cell type, but is generally in the range of 5–30 picograms This is an exceedingly small amount of starting material Some simple calculations that assume about 10 pg of total RNA per cell, with % of this mRNA, determine that there are approximately 160,000 molecules of mRNA per cell This might seem like a lot, but in fact about 10,000 genes are expressed per cell on average, so this works out to only 16 mRNA transcripts per gene And this assumes that all mRNAs are of similar abundance, but in fact a typical cell has around a 100 or so genes that are expressed at very high levels, with 1–10,000 copies per cell, accounting in total for about half of the mass of mRNA The end result is that for the majority of genes expressed the transcripts are present at an abundance level below ten copies per cell And our ability to capture these RNAs, in terms of reverse transcription efficiency and amplification, is quite limited, with perhaps only 10–30 % of present RNAs actually detected The net result is a considerable level of technical noise Our current technology is quite imperfect in the detection and quantification of such small numbers of RNA molecules In addition to technical noise there is biological noise to consider There are very few RNAs present per expressed gene and one would expect to see significant random fluctuation, even if genes were expressed in a constant steady state manner Gene expression, however, is not steady state, but instead occurs in a pulsatile bursting mode Early work in both bacteria and eukaryotes showed that gene expression is largely an on/off process, with gradual induction increasing the percentage of cells with expression, rather than giving incremental increase of expression in each cell (Ko et al 1990; Novick and Weiner 1957) More recent landmark studies include the use of two copies of the same promoter in a single bacterial, or yeast cell, driving expression of two different fluorescent proteins The results elegantly demonstrated striking fluctuation, or noise, in the expression levels of two genes with identical promoters in single cells (Elowitz et al 2002; Ozbudak et al 2002) Gene expression varies from cell to cell, or within a single cell as a function of time, as a result of sporadic short bursts of active transcription (Chubb et al 2006; Golding et al 2005; Raj et al 2008; Ross et al 1994; Takasuka et al 1998) 436 S.S Potter The causes of the pulsatile nature of gene expression are not fully defined, but one model states that transcription occurs within a limited number of transcription factories in cells (Jackson et al 1993; Osborne et al 2004; Wansink et al 1993) Genes would compete for occupancy of sites within a factory where they would be highly transcribed It has been proposed that there are relatively few of these factories, on the order of hundreds per cell, which account for the bulk of the transcription of the approximately 10,000 genes that are expressed There are important biological consequences to the noisy nature of gene expression Each gene is present in only two copies per cell And, as mentioned, each expressed gene on average has relatively few transcripts per cell These are very small numbers, statistically speaking And the observed burst mode of gene expression dictates that variations in transcript levels are far greater than would be predicted by a simple Poisson distribution Indeed there are so many genes with so much variation that it makes one wonder how things ever turn out right, especially during development, when correct combinations of transcription factors are thought to drive appropriate developmental destiny decisions One strategy for biological success is to employ genetic functional redundancy Indeed, such redundancy is generally acknowledged to be responsible for the surprisingly mild phenotypes often observed in mice with targeted homozygous mutations of single genes Gene noise is probably an underappreciated cause of incomplete penetrance and variable expressivity, which can persist even on isogenic genetic backgrounds Remarkably, it appears that in some cases the noisy nature of gene expression has actually been harnessed to drive developmental decisions One example is the selection of specific odorant receptors during development of the olfaction system There are over a 1000 receptors and they are activated using a random “Monte Carlo” strategy in a mutually exclusive fashion (Tsuboi et al 1999; Vassar et al 1993) There is also evidence that during hematopoiesis the differentiation of stem cells could be regulated by stochastic gene expression events (Chang et al 2008) Another elegant example is the development of photoreceptors in the Drosophila eye, where stochastic variations in spineless gene expression drive photoreceptor type differentiation decisions (Wernet et al 2006) Despite the technical and biological challenges it is now possible to effectively perform RNA-seq analysis of single cells Because of noise issues it is necessary to examine many single cell replicates Limited information is obtained from each single cell The goal is to first generate sufficiently distinct gene expression profiles to divide single cells into categories and subtypes The RNA-seq datasets from the multiple cells of each category are then pooled to derive an accurate gene expression definition of that cell type This fundamental single cell strategy is of key importance Each individual cell represents a biological replicate Although the gene expression data for each cell is quite imperfect, the underlying principle is to divide the cells into distinct groups based on their gene expression signatures The data from each group is then pooled, 20 Functional Genomics of Development 437 to average out the noise and to generate a robust gene expression profile for each cell type How many cells must be examined in a single cell study? A general rule of thumb is that there must be at least ten representatives of each cell type The total number of cells required therefore depends on the degree of cell heterogeneity If, for example, only two cell types are thought to be present, and they are in roughly equal proportion, then relatively few total cells are required to achieve the minimum of ten cells per type On the other hand, if there is great cell heterogeneity, with many types of cells present and some being quite rare, then the total number of cells required would be very high, potentially in the thousands In single cell studies the general rule is the more cells the better, as this provides more representatives of each cell type 20.9 Pioneering Single Cell Studies Early studies pioneered the strategy of dissociation of tissues into single cell suspensions followed by gene expression profiling of single cells to define novel cell types For example Chiang and Melton carried out a single cell transcript analysis of pancreas development in 2003 (Chiang and Melton 2003) They were able to examine the developing pancreas at E10.5, when the cells are morphologically uniform The analysis of 60 single cells allowed the developing pancreas to be divided into six subtypes based on expression of distinct markers Of particular interest, one subset of cells showed expression of a combination of markers, including P48, Nkx2.2, and Nkx6.1, suggesting that these cells might be progenitors for multiple cell types In another study, published around the same time, a similar dissociation/single cell gene expression profiling strategy was used to examine the mammalian olfactory system (Tietjen et al 2003) The results defined a number of genes with differential expression in olfactory sensory neurons and olfactory progenitor cells This work included extensive validation of the general procedures used for the single cell analysis These early studies established the general strategies that would be used for the single cell dissection of developmental mechanisms The limiting quantities of RNA present in single cells necessitate powerful amplification methods to generate sufficient material for microarray or RNA-seq analysis PCR methods are most often used, but are subject to amplification bias Methods based on in vitro transcription amplification (Van Gelder et al 1990) offer better amplification linearity The most recent methods, however, combine the power of PCR with the hybridization of unique molecular identifiers that completely eliminate amplification bias (see below) 438 20.10 S.S Potter High Throughput Single Cell Studies Fluidigm offers the C1 machine that combines robotics and microfluidics to facilitate high throughput single cell analysis The C1 receives a single cell suspension, and the cells are then randomly distributed to chambers during a capture step Individual capture sites can then be examined with a microscope to identify those with single cells Typically 60–80 % of capture sites show single cell occupancy The Fluidigm C1 then carries out a series of steps, including cell lysis, reverse transcription and PCR based amplification The products are harvested and used for RNA-seq gene expression profiling Different IFCs are offered for the processing of cells with different sizes The original Fluidigm C1 IFC was designed with 96 chambers, but a subsequent IFC offered 800 chambers, and it is likely that this number will increase in the future The advent of the Fluidigm C1 microfluidics/robotics technology greatly facilitated a number of single cell gene expression profiling studies For example consider an analysis of the developing kidney (Brunskill et al 2014) Single cells from the early E11.5 metanephric mesenchyme progenitors were subjected to gene expression profiling, thereby distinguishing the gene expression profiles of the cells committed to make nephrons from those that will make stoma A surprising result was that even at this very early stage of kidney development some of the progenitors showed expression of markers of differentiated cells For example a small fraction of the early progenitors showed robust expression of MafB, a marker of podocytes This sporadic expression could be confirmed by immunostain These early cells did not yet, however, appear to be committed to making podocytes Many of the metanephric mesenchyme cells showed expression of one or two podocyte markers, but none showed expression of a strong podocyte signature, involving many markers At later stages in development progenitors showed more restricted potential lineages, and expression of more genes associated with those lineages For example the renal vesicles (RV) are the progenitors of the nephron epithelia cells Many RV single cells showed expression of five or more podocyte specific differentiation markers, suggesting they were well on the way to becoming podocytes Nevertheless, many of these same cells also showed expression of multiple markers of other lineages, including for example proximal and distal tubules (Fig 20.6) In summary, it appears that early progenitors are capable of expressing a few random markers of their many potential lineages, while later progenitors will express more markers of each of their now more limited lineage choices 20 Functional Genomics of Development 439 Fig 20.6 Multilineage priming in the renal vesicle Heatmap of renal vesicle cells, with red indicating high expression, blue is low expression, and yellow is intermediate expression Each column is a separate single cell Podocyte markers are Mafb, Plat, Sulf1, Sncaip and Wt1 Proximal tubule markers are Akr1c18, Clcn5, Dhcr7, Dll1, Gcfc1, Grm4, Irx3, Lama1, Pcsk9, Pdss1, Slc11a2, Slc30a6, Slc37a4, Tagap, Timm9 and Zfand1 Cdh16 is a distal tubule marker and Cdh6 is a parietal epithelial cell marker Many cells show expression of multiple podoctye markers, even though only a small percentage of cells of the resulting nephron will be podocytes Single cells typically show expression of multiple lineage markers 20.11 Drop-Seq, a New Single Cell RNA-Seq Technology An exciting new technology for single cell RNA-seq was described by the McCarroll lab (Macosko et al 2015) This Drop-seq technology is extremely high throughput, allowing the analysis of many thousands of single cells, and dramatically reduces the cost, to less than ten cents per cell, not counting the DNA sequencing costs A simple microfluidics device is used to make aqueous drops in oil The drops are made in a manner that results in the inclusion of a single cell in about one drop in ten, as well as a single microparticle bead The key concept of the technique is that the beads are coated with oligonucleotides Importantly, all of the oligonucleotides on each bead include a bead specific 12 base barcode The cell within a drop is lysed and the polyadenylated mRNA anneals to a dT region of the oligonucleotides on the 440 S.S Potter bead When cDNA is made from the annealed RNA it results in the inclusion of the bead specific barcode sequence This allows all of the DNA sequence reads from the same drop to be assigned to a single cell, by virtue of the shared unique barcode The power of Drop-seq derives from the ability to combine all of the beads from the thousands of drops into a single tube for processing Instead of carrying out tens of thousands of reverse transcription reactions, all are executed in a single small tube The resulting cDNAs are cell-specific barcoded by virtue of the bead specific olidgonucleotides that they are hybridized to The resulting remarkable efficiency is responsible for the low cost per cell for Drop-seq The McCarroll lab used this technology to analyze the transcriptomes of 44,808 mouse retinal cells, identifying 39 distinct gene expression profile patterns, including novel cell subtypes (Macosko et al 2015) Another useful feature of Drop-seq is that each oligonucleotide on a bead includes an eight base Unique Molecular Identifier So, in addition to the 12 base bead specific sequence, which is the same for all oligonucleotides on one bead, there is also an eight base sequence that is distinct for every oligonucleotide on a bead This allows the RNA-seq reads to be aligned not only to a single cell, via the 12 base barcode, but to a specific oligonucleotide on that bead In this manner it is possible to eliminate nonlinearity in the amplification chemistry The RNA-seq data can therefore be deconvoluted to count the number of RNAs hybridized to the bead 20.12 Single Cell Analysis Software Packages Single cell RNA-seq data offers unique analysis challenges There can be a very large number of datasets In addition the data is noisy, so the gene expression profile generated for each cell is far from perfect The profiles include so-called “dropouts”, where no transcripts are detected even though the gene is actually expressed This can result from the combination of the small number of RNAs present for the average expressed gene and the relatively inefficient capture of RNAs by the amplification chemistries It means that cells cannot be grouped by simple single marker strategies One cannot determine that a cell is a specific type based on the presence or absence of expression of a single gene Instead a more complex gene expression profile is required, using the expression patterns of many genes And these profiles are likely unknown at the start, and need to be generated as a part of the analysis Several software packages specifically designed to assist in the analysis of single cell data are available A partial list includes the Singular Analysis Toolset offered by Fluidigm, Sincera (Guo et al 2015), Monocle (Trapnell et al 2014), AltAnalyze (Salomonis et al 2010), and Seurat (Satija et al 2015) Each of these programs is powerful and useful, but the complexities of single cell analysis dictate that none of them offers a simple solution The analysis of the data clearly remains the greatest 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WR, Davis JR Dynamic changes in prolactin promoter activation in individual living lactotrophic cells Endocrinology 1998;139:1361–8 Temin HM, Baltimore D RNA-directed DNA synthesis and RNA tumor viruses Adv Virus Res 1972;17:129–86 Tietjen I, Rihel JM, Cao Y, Koentges G, Zakhary L, Dulac C Single-cell transcriptional analysis of neuronal progenitors Neuron 2003;38:161–75 20 Functional Genomics of Development 443 Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells Nat Biotechnol 2014;32:381–6 Tsuboi A, Yoshihara S, Yamazaki N, Kasai H, Asai-Tsuboi H, Komatsu M, Serizawa S, Ishii T, Matsuda Y, Nagawa F, et al Olfactory neurons expressing closely linked and homologous odorant receptor genes tend to project their axons to neighboring glomeruli on the olfactory bulb J Neurosci 1999;19:8409–18 Van Gelder RN, von Zastrow ME, Yool A, Dement WC, Barchas JD, Eberwine JH Amplified RNA synthesized from limited quantities of heterogeneous cDNA Proc Natl Acad Sci U S A 1990;87:1663–7 Vassar R, Ngai J, Axel R Spatial segregation of odorant receptor expression in the mammalian olfactory epithelium Cell 1993;74:309–18 Wansink DG, Schul W, van der Kraan I, van Steensel B, van Driel R, de Jong L Fluorescent labeling of nascent RNA reveals transcription by RNA polymerase II in domains scattered throughout the nucleus J Cell Biol 1993;122:283–93 Wernet MF, Mazzoni EO, Celik A, Duncan DM, Duncan I, Desplan C Stochastic spineless expression creates the retinal mosaic for colour vision Nature 2006;440:174–80 Index A Access control, 19, 34 Access management, 96, 100 ADHD See Attention deficit hyperactivity disorder (ADHD) Adolescent care, 28–29, 180 Adverse drug events (ADEs), 166, 174, 210 Adverse events (AEs), 39, 165, 166, 168, 330 Allele, 63, 64, 283–286, 288, 324, 341, 344, 346–351, 354, 358, 366, 373, 376 Allele prediction, 341, 350, 352, 358 Annotation, 111, 190, 208–217, 223, 234, 260, 262, 264, 299, 302, 308, 321, 325, 343, 344, 398, 402, 403, 405, 410 Asthma, 22, 45, 49, 106, 111, 244, 297 Attention deficit hyperactivity disorder (ADHD), 22, 254, 265–267, 297 Audit trails, 58, 62, 72, 92, 96–98, 171 Auditing, 62, 72–73, 83, 92, 95, 98, 171, 301 Authentication, 31, 61, 62, 76, 82, 89, 90, 92, 99 Automated detection, 59, 235–237 Automated Reporting, 185, 189 B Barcoding, 439, 440 Bayesian networks, 166, 403 Biobanking, 123–126, 128–137, 296, 297, 308 Biological networks, 252, 254–260, 269, 314, 322, 331 Biospecimens, 108, 131, 132, 148 Brain, 65, 156, 254, 265–267, 270, 395 C Candidate genes, 282, 320, 322, 324–326, 351, 355, 395, 425 Causal variants, 277–290, 324–326, 373 CDS See Clinical decision support (CDS) Cell types, 289, 290, 298, 387–390, 393, 409–413, 422, 426–430, 434–437 CER See Comparative effectiveness research (CER) Chromatin immunoprecipitation (ChIP), 402 Classification, 46, 47, 49–51, 58–60, 97, 146, 207, 217, 222, 223, 239–240, 242, 243, 317, 320, 351–355, 357, 365, 397, 398 Clinical decision support (CDS), 49, 151, 303, 304, 352, 357 Clinical laboratory improvement amendments (CLIA), 135, 304 Clinical notes, 148, 205, 209, 210, 217, 223, 236, 241 Clinical Text Analysis and Knowledge Extraction System (cTAKES), 219, 220, 233–235, 237, 239, 241, 244 Cloud storage, 65, 67, 301 Clustering analysis, 253, 352, 357, 397–400, 432 Co-expression, 252, 397 Cohort identification, 104, 110, 190, 195, 233, 240–241, 308 Comparative effectiveness research (CER), 180, 185, 194, 197, 244 Compliance, 41, 62, 63, 66–74, 83, 91, 97, 156, 296, 303, 346 Confidentiality, 30, 33, 98, 153, 223, 300 © Springer Science+Business Media Singapore 2016 J.J Hutton (ed.), Pediatric Biomedical Informatics, Translational Bioinformatics 10, DOI 10.1007/978-981-10-1104-7 445 446 Configuration, 87, 89–92, 97, 169, 174, 189 Congenital malformations, 371 Connectivity maps, 254, 265–267, 320, 330 Consents, 30, 32–35, 98, 108, 123, 124, 130–135, 137, 183–185, 191–192, 205, 235, 296, 297, 300, 305–308 Corpora, 208–210, 214, 223, 237 Craniofacial development, 368 cTAKES See Clinical Text Analysis and Knowledge Extraction System (cTAKES) Cybersecurity, 108 D Data capture, 180, 185 centric network, 80–85 entry, 8, 9, 22, 28–29, 46, 62, 97, 172, 182, 184, 185, 190 integration, 30, 76, 136, 144, 153, 188, 330–331 interchange, 38, 41 mapping, 106, 157, 393 models, 16, 23, 38, 41, 52, 102–105, 111, 115, 118, 170–171, 182, 185, 191, 194, 196, 197, 242 portals, 59–61 quality, 9, 23, 29, 116, 118, 149, 170, 182, 185, 189, 190, 298, 300 sharing, 9, 18, 32, 73, 80, 238, 298–303, 307, 308 storage, 10, 32, 44, 58–61, 63–67, 70–72, 74–76, 94, 97, 302, 343, 345 types, 39, 58–62, 65, 68, 74, 303, 304, 321, 343, 358 warehouse, 50, 64, 80, 94, 95, 103–105, 110, 115, 116, 118, 170, 181, 184, 190, 194, 237, 297, 304 Decision support, 11, 14–17, 19, 23, 44, 48, 49, 112, 144, 150, 151, 180, 204, 235, 305, 341, 352, 354, 358 Decision trees, 352–355 De-identification, 62, 104, 108–110, 209, 213, 222, 235–237 Development, 7, 38, 65, 84, 131, 144, 165, 204, 233, 263, 285, 296, 315, 316, 340, 387, 422 Differentiation, 365, 377, 386–391, 393, 400, 405, 409–411, 426, 432, 435, 436, 438 Digital Imaging and Communications in Medicine (DICOM), 53 Index Disaster recovery, 68, 74, 76 Disease candidate genes, 321, 322 networks, 300, 316, 317, 319, 331 Distributed research networks, 76 DNA sequencing, 59, 61, 322, 340, 347, 386, 390, 391, 413, 428, 433, 439 DNA variants, 286, 326 Document classification, 239, 240 Dominant, 206, 285 Dosing, 14–16, 148, 151, 164–169, 171, 173, 174, 189 Drug development, 326 discovery, 252, 314, 320, 327, 330–331 repurposing targets, 326 Dynamic profiling, 399 E EHR-linked registry, 22–23 Electronic health records (EHRs), 28, 31, 39, 41–43, 62, 64, 75, 118, 130, 132, 136, 147, 148, 152, 164, 173, 181, 185, 235, 244, 245, 254, 267, 268, 296, 303, 309, 358 Electronic medical records, 51, 95, 100 Electronic Medical Records and Genomics (eMERGE), 137, 205, 237, 238, 241, 296, 297, 304 Encryption, 62, 66, 67, 69–71, 73, 89, 90, 96, 97 Epilepsy, 239 Ethics, 129–137 Exome, 130, 281, 282, 285–288, 298, 299, 322–326, 341, 370, 371, 373–375 Exome sequencing, 281, 282, 285, 286, 288, 298, 322–324, 326, 370, 371, 373, 374 Exon sequencing, 281, 347 Expressions, 61, 218, 234, 252, 289, 320, 358, 386, 422 Extract-transform-load (ETL), 103, 107, 117, 182, 195–197 F Federated data warehouses, 181 Federated network, 301 Firewall, 80–85, 88, 90, 93, 96, 99, 186 Functional classification, 398 Functional genomics, 254, 289, 389, 422 Functional magnetic resonance imaging (fMRI), 65, 266 Index G GATK See Genome Analysis Toolkit (GATK) Gene discovery, 364 expression, 252, 264, 289, 320, 322, 330, 331, 373, 386, 389–394, 396, 399, 400, 403, 406–408, 410–412, 422, 425–438, 440 knockout, 263 prioritization, 320, 322, 325, 395 Genetic diseases, 263, 298 Genetic mapping, 283, 288, 364 Genetic models, 288, 400 Genetic polymorphisms, 358 Genetic variation, 278, 290, 340, 347, 369–371, 373–376, 378 Genome, 61, 130, 238, 264, 281–282, 315, 340, 386, 427 Genome Analysis Toolkit (GATK), 283, 284, 288, 303, 342–344 Genome variation, 299 Genome-wide association database (gwadb), 344–349 Genome-wide association studies (GWAS), 63–65, 297, 341, 342, 344–346, 348, 349, 372, 377, 378 Genomics, 30, 111, 152, 205, 237, 254, 314, 340, 389, 422 Genotype, 61, 64, 237, 240, 284, 286, 288, 289, 299, 301, 308, 317, 330, 341, 343–345, 347, 348, 350, 352–357, 369, 371, 376, 377, 398 Gestational age, 13, 145–147, 149, 399, 407, 409 Gold standard, 208, 214 Growth, 47, 74–75, 114, 146, 164, 387 gwadb See Genome-wide association database (gwadb) GWAS See Genome-wide association studies (GWAS) H Haplotype, 284, 341, 346, 347, 350, 353, 376 HapMap, Harm detection, 342, 376 Health information technology, 22, 33, 38, 42, 168 Health Information Technology for Economic and Clinical Health (HITECH), 4, 30, 41, 104, 304 Health Insurance Portability and Accountably Act (HIPAA), 28–32, 34, 42, 66, 67, 94, 104, 108, 109, 130, 135, 235 447 Health level (HL7), 18, 39, 40, 43–45, 50, 107, 186–188 Heatmap, 395, 396, 408, 410, 412, 426, 439 High density lipoprotein (HDL), 253, 260–263 HIPAA See Health Insurance Portability and Accountably Act (HIPAA) HITECH See Health Information Technology for Economic and Clinical Health (HITECH) HLA See Human leukocyte antigen (HLA) Homeostasis, 254, 392, 397, 398, 405–410, 413 Homozygous, 64, 285, 287, 288, 326, 354, 370, 436 Human leukocyte antigen (HLA), 341, 346–350, 352, 358 I i2b2, 104, 105, 110–113, 194, 210, 222, 223, 236, 237, 241 ICD See International Classification of Diseases (ICD) ICD-9, 46, 47, 49, 105, 106, 205, 222, 238, 241, 244, 296 ICD-10, 49, 50, 105, 148, 205 Identity management, 65, 71, 97 Imaging, 53, 61, 65, 74, 240, 265, 266, 280, 386–388 Immunization, 16–18, 21, 23, 39, 43, 44, 164, 267 Implementations, 6, 33, 38, 60, 132, 151, 164, 191, 240, 296 Incidental findings, 123, 129, 132–137 Infant mortality, 143–145, 147, 154, 386 Information, 4, 28–29, 38, 59, 80, 123, 147, 164, 183, 204, 232, 263, 281, 315–316, 340, 389, 436 Information extraction, 208–210, 219, 223, 233, 236 Informed consent, 129–131, 134 Institutional Review Board (IRB), 29, 76, 108, 110, 131–136, 183, 184, 190, 191, 205, 235, 237, 297 Integration, 30, 58, 84, 136, 144, 165, 182, 304, 327, 340, 386, 427 Interface(s), 6, 8, 46–51, 62, 64, 65, 73, 83, 112, 129, 132, 169, 174, 185–187, 191, 194, 265, 343–346, 386 International Classification of Diseases (ICD), 46, 49, 51, 111, 124, 148, 149, 205 Interoperability, 18, 20, 38, 40, 42, 45, 50, 106, 298, 299, 358 Intrusion, 80, 86, 98, 115 IRB See Institutional Review Board (IRB) 448 K Kabuki syndrome, 323 Kidney, 173, 422, 423, 425–427, 429–432, 434, 435, 438 L Laser capture microdissection (LCM), 422, 426, 432 Limited dataset, 108, 109, 112 Linkage, 9, 19, 111, 132, 154, 155, 191, 260, 319, 320, 364, 410 Linkage disequilibrium (LD), 347–349 Logical Observation Identifiers Names and Codes (LOINC), 46, 49–51, 105, 111, 188 Lung, 147, 254, 298, 386 Lung maturation, 254, 386–388, 390, 392, 398–400, 405, 407–409, 413 M Machine learning, 151, 152, 207, 209, 217, 234, 237, 239, 243, 349, 350, 352, 355 Maturation, 13, 15, 173, 365 Meaningful use (MU), 4, 41–42, 102, 106, 191, 233, 244–245, 304 Measure(s), 8, 43, 104, 126, 145, 167, 185, 209, 236, 256, 283, 299, 328, 344, 390, 425 Measure development, 10, 220, 425 Medical device, 187, 192 Medical informatics, 38, 210 Medication, 8, 29, 41, 104, 147, 164, 185, 208, 236 Messaging, 18, 21, 38–44, 53 Microarray, 59, 61, 263, 264, 303, 389, 390, 397, 400–403, 406, 407, 411, 426, 429, 432, 437 miRNA, 265 MITRE Identification Scrubber Toolkit (MIST), 236 Molecular networks, 252, 253, 258, 265 Monogenic disorders, 322 Motif, 253, 256–260, 289, 406, 407 mRNAs, 289, 398, 399, 402, 435 Mutant, 317–320, 429–431 Mutations, 135, 238, 263, 304, 322, 325, 389, 428 N Natural language processing (NLP), 64, 110, 166, 207, 218–224, 232, 235–238, 240–244, 296 Index Neonatal care, 143–145, 150–152 Neonatal data, 143, 144, 147, 150, 156–158 Neonatal intensive care unit (NICU), 17, 144, 147, 148, 150, 151, 153, 156, 303 Neonatal research network, 156, 157 Neonatal terminology, 157 Nephrogenesis, 425, 432–433 Network analysis, 252, 254, 256–270, 324, 326–328 applications, 257, 263–265 storage, 59, 66 Newborn, 18–20, 45, 144, 146, 148–153, 155, 157, 303 NICU See Neonatal intensive care unit (NICU) NLP See Natural language processing (NLP) O Operating systems, 90–94 Operational data store (ODS), 102, 106, 107 Opioids, 151, 152, 169, 351 Organogenesis, 391, 392, 411, 413, 422 Orphan disease, 254, 316, 318 Outcome measures, 188, 190 P Pain, 152, 219, 341, 351–355, 358 Parent(s), 7, 17, 20, 21, 28–35, 47, 48, 125, 129, 131, 135–137, 167, 266, 285, 287, 296, 297, 299, 303, 306, 307, 326, 376 Parental medical record, 32, 133 Parental notification, 32 Patient classification, 239–240 safety, 41, 144, 148, 172, 204, 213, 235 Perinatal, 9, 18, 19, 145, 148–150, 153–157, 386–388, 398, 400, 407, 409, 413 Perinatal data, 148–149 Permissions, 31, 58, 61, 71–74, 94, 97, 98, 130, 131, 268, 269, 296, 343 Personalized medicine, 304 Pharmacology, 326 Phenotypes, 64, 189, 205, 233, 237, 238, 240, 241, 263, 285, 289, 296, 298, 299, 301, 302, 308, 317, 320–322, 329, 341, 348, 351, 353, 358, 369–374, 376–378, 387, 390, 396, 398, 400–402, 426, 431, 436 Phenotyping, 64, 156, 205, 220, 235, 238, 296, 298 PHI See Protected health information (PHI) Pipeline, 61, 65, 218–220, 300, 340, 344, 345 Podocyte, 424, 427, 429–431, 438, 439 Index Populations, 4, 7, 8, 10, 12–14, 18, 22–23, 53, 80, 102–104, 143, 146–151, 154–156, 167, 180, 181, 184, 185, 189, 196, 197, 240, 244, 267, 285, 286, 288, 309, 314, 341, 342, 349, 350, 371, 372, 375, 376, 389, 391 PPIs See Protein–protein interactions (PPIs) Prediction, 150, 175, 215, 221, 222, 262, 263, 269, 285, 303, 324, 325, 341, 349, 351–354, 357, 358, 378, 391, 400, 402, 403, 405, 406, 411 Prescribing, 14–16, 22, 29, 41, 42, 53, 166, 167 Preterm birth, 145–147, 154, 386 Privacy, 19, 30, 32–34, 42, 69, 94, 98, 99, 103, 108, 112, 135, 152, 153, 156, 181, 191, 194, 223, 268, 300, 303, 308 Profiling, 117, 320, 330, 386, 389–392, 399, 400, 402, 406, 407, 410, 411, 422, 425–428, 430, 437, 438, 440 Promoters, 289, 377, 391, 398, 400, 401, 406, 407, 426, 427, 429, 435 Protected health information (PHI), 62, 69, 123, 209, 235–237 Protection, 32–34, 70–72, 76, 80, 81, 83, 84, 88, 98, 100, 112, 296, 300 Protein–protein interactions (PPIs), 252–256, 258–263, 318, 321, 328, 402, 407, 410, 412 Q Quality, 5, 29, 43, 65, 126, 148, 164, 179, 204, 235, 267, 283, 296, 317, 341, 393 Quality improvement, 103, 104, 112, 113, 148, 156, 179, 180, 182–185, 189, 193, 204, 235 Query, 39, 64, 65, 103, 104, 106, 107, 110–113, 181, 190, 193–195, 197, 237, 242, 244, 245, 308, 325, 331 R Rare disease, 51, 180, 300, 301, 307, 316, 317, 331 Recessive, 285, 370, 373, 376 Reference sequence, 344 Registry, 18, 22, 23, 44, 156, 183–185, 187–189, 191 Regulatory elements, 427 Reporting, 21, 24, 39, 43, 85, 102, 127, 133, 135, 164, 166, 170, 186, 187, 304, 308 Reports, 6, 28, 39, 69, 127, 146, 164, 180, 206, 236, 256, 280, 297, 318, 344, 370, 391, 427 449 Repurposing, 235 Residual clinical samples, 129–133, 137 Respiratory distress syndrome (RDS), 386, 400 Retention, 67–71, 116, 132 Return of results, 123, 136 RNA-Seq, 263, 264, 289, 391, 393–395, 402, 404–406, 411, 422, 425, 428–429, 432–434, 438–440 Run charts, 189 S Safety, 41, 150, 151, 165, 166, 168, 170–172, 326, 327, 354 Sample retention, 132 Sample tracking, 133 Screening, 20, 21, 241, 290, 303, 327, 329, 331, 432 Security, 30, 42, 58, 80, 103, 128, 153, 300, 346 Semantic interoperability, 38, 42, 45, 106, 299 Sentiment analysis, 242–244 Shared Health Research Information NEtwork (SHRINE), 194, 195, 237, 238 Single cell analysis, 437, 438, 440 Single nucleotide polymorphism (SNP), 64, 341, 342, 345–353, 355–358, 374, 376–378, 390 SNOMED, 47, 105, 106, 111, 157 SNP genotyping, 64, 347, 350, 356, 357, 376 Spatial analysis, 154 Standards, 7, 29, 38, 59, 83, 105, 125, 165, 204, 232, 258, 278, 297–299, 328, 343, 392, 433 Stratification, 23 Suicide, 205, 223, 243 T Target, 16, 90, 96, 107, 129, 170, 174, 183, 192, 196, 252, 254, 258, 265, 281–283, 299, 314, 322, 327–330, 351, 366–368, 370, 372–374, 379, 389–391, 400–402, 405–407, 409, 410, 436 Terminology, 7, 21, 38, 40, 44–53, 105–106, 111, 157, 186, 191, 194, 196, 197, 211, 301 Text, 5, 28, 39, 63, 89, 112, 186, 206, 233, 267, 286, 304, 317, 343 Text analysis, 219, 267 Tools, 8, 59, 86, 104, 144, 169, 183, 204, 233, 256, 289, 296, 317, 341, 391, 422 450 Transcription, 212, 213, 242, 377, 400, 425–426, 433, 435–438, 440 Transcription factors (TFs), 252, 289, 389, 390, 392, 397, 400, 402, 405, 407, 409, 412, 426, 436 Transcriptional networks, 254, 390, 397, 400, 407, 412, 413 Transcriptional programs, 389, 407–409 Transcriptome, 387, 389–391, 412, 440 Translational research, 62, 68, 69, 71, 74–76, 80, 88, 94, 96, 110, 205, 210, 232, 235, 239, 240, 242, 245, 267, 309, 340, 341, 344, 352, 355, 358 Transmission, 18, 30, 38, 39, 41, 107 Trigger tool, 152 Trios, 285–287, 344 U Users, 6, 8, 10, 13–16, 18, 23, 30, 33, 34, 39, 45–51, 58, 60–62, 64–67, 70–73, 75, 80–85, 88, 89, 92, 96–99, 102–108, 110–113, 115–118, 130, 165, 168–172, Index 174, 179, 184, 185, 187–190, 192–195, 232, 234, 257, 263, 300–302, 309, 343, 345, 346, 412 V Variant, 63, 64, 277–290, 299–301, 308, 322–326, 340–347, 351–358, 364, 366–374, 376–379 Variant discovery, 277–290 Virtual private networking (VPN), 70, 80, 82, 84, 85, 88–90, 96, 97 W Warehouse, 50, 96, 102–104, 106, 108, 110–112, 136, 181, 205, 297 Whole genome sequencing, 130, 281–282, 285, 297, 299, 303, 305, 322, 343, 364, 366, 375, 376 Workbench, 111, 112 Workflow, 7–9, 18, 22, 24, 32, 38, 59, 61, 63, 65, 72, 97, 103, 110, 116, 118, 132, 144, 165, 166, 171, 186, 187, 192, 193, 195, 264, 287, 302, 308, 345, 374, 395, 411, 432 ... Professor of Biomedical Informatics, University of Cincinnati Department of Pediatrics, Department of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center Contents Part I Core Informatics. .. andrew.spooner@cchmc.org E.S Kirkendall, MD Departments of Pediatrics and Biomedical Informatics, Divisions of Hospital Medicine and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center,... series at http://www.springer.com/series/11057 John J Hutton Editor Pediatric Biomedical Informatics Computer Applications in Pediatric Research Second Edition Editor John J Hutton (1937–2016)