Faculty and staff at JAX are engaged in a wide range of research areas in data science including algorithm development, systems genetics, data analysis and visualization tools, and community genome informatics resource development. A professionally staffed Computational Sciences scientific services core supports the application of data science approaches for all JAX faculty through the implementation of data analysis workflows, experimental design consulting, and data analysis.
My goal is to model the genetic, molecular, and cellular origins of Alzheimer's pathogenesis using multiomics in novel animal models of late onset disease. As part of the MODEL-AD and associated consortiums I will work to achieve these goals and to compare findings in model systems with data from human populations to increase the translational potential for research findings.
The Agoro Lab uses Systems Biology approach to identify molecular targets that prevent kidney aging, the progression of chronic kidney disease (CKD), as well as the development of sickle cell nephropathy (SCN). We believe the kidney to be the pioneer organ in mammals, and we are interested in understanding the molecular and cellular dynamics that change in this structurally and functionally complex organ during aging, CKD, and sickle cell anemia. In aged or diseased states, changes in cellular iron metabolism occur in the kidney driving therefore oxidative stress and renal function impairment. One of this function is the decrease of the ability of the kidney to get rid of phosphate from the blood and thus preventing detrimental outcomes such as vascular calcification. Our lab is dedicated to study the mechanisms associated with renal iron handling during physiological and pathological states and how these mechanisms influence the kidney ability to eliminate phosphate from our body. We are using genetically engineered mouse models, in vitro assays, associated with computational biology tools to study the interplay between iron-driven renal oxidative stress pathways and FGF23-mediated phosphate excretion. Understanding the interactions between the above-mentioned biological mechanisms in the kidney is critical for: 1. an optimal physiological performance, 2. an effective interorgan signaling, 3. and a better control of kidney disease progression.
My project focuses on combining spatial transcriptomics and imaging data to predict phenotypes in cancer and other diseases. In my previous research I developed TISMorph software (in Matlab) to add new cell shape features which common software were not able to capture. Then I used these features to distinguished between cancer and normal cells with single cell resolution and predicted the function of novel kinases. I also mathematically modeled nuclear Beta-catenin's switch like response to WNT stimulation. During my last four years I have worked closely with biologists, developed a pipeline for them in R, and mentored them to analyze, automatically label plate-based assays and visualize their data.
Pete has been in the computing industry since 2000 where he worked and earned his BS in Computer Science from Southern Polytechnic State University (now Kennesaw University). In 2012, he left the computing industry to pursue an MS and a PhD from the Georgia Institute of Technology where he developed variant discovery techniques in bacteria and was involved in several small cancer studies with Dr. Fredrik Vannberg. In 2016, he joined the University of Washington under Dr. Evan Eichler where he developed methods to apply long-reads to structural variant discovery. In 2021, he joined the Jackson Laboratory under Dr. Christine Beck to study mechanisms of structural variation.
My long-term goal is to obtain the necessary skills to develop an independent research program focused on studying the effects of genetic and environmental variation on cell-fate decisions using a multi-disciplinary approach that combines wet-lab experiments with mathematical modeling. Towards this goal, I have sought interdisciplinary training in molecular biology, computational and systems biology, and quantitative genetics. As a postdoc in Munger lab, I aim to complement and enhance these skills by training in mouse genetics and developmental biology. As a graduate student working with Drs. Nick Buchler and Paul Magwene, I characterized the effects of natural genetic variation in budding yeast on growth dynamics in response to hyper-osmotic stress. I showed that this phenotype was highly variable in our genetically diverse collection of yeast strains, and then applied bulk segregant analysis to identify genetic variants that mediated this variable response. In my postdoctoral research, I have started exploring GxE interactions in a higher model organism (mouse) within embryonic stem cells. In addition to research, I am actively involved in teaching, mentoring and scientific outreach efforts at JAX. Outside of lab I enjoy the outdoors by hiking, snowshoeing and gardening!
I worked as a pharmacist following my undergraduate education. Shortly in my clinical work, I was frustrated with the progress of pharmaceuticals in the field of neuropsychiatric disorders. I was especially moved by my friend's struggles with limerence, a disorder that is still undiagnosable and with very little literature surrounding it. I was motivated to go into a research-based program in Boston; there I received my Master's degree in pharmacology and drug development. I got immersed in the field of neurogenetics by my previous mentor Dr. Leon Reijmers and I have decided that's the type of training I want to focus on. I got accepted to the Tufts-JAX collaborative Ph.D. program in neuroscience and I have never made a better choice than to join this program. Under the mentorship of Dr. Vivek Kumar, I feel I will get the training I need to ask and answer critical questions that can lead to better drug targets for neuropsychiatric disorders with unmet needs.
My project is involved in understanding the causal mechanisms involved in age dependent hyperactivity disorder and its relationship to cortical cell death.
The mechanisms governing non-recurrent human structural variation (SV) are diverse and often poorly understood. I am investigating how human DNA maintains fidelity in the context of a repetitive genome. For example, human Alu elements number over one million copies per human genome, and recent studies have found that these repeat sequences often mediate SVs in some loci. Through computational, molecular biological and genomic techniques, we will identify regions susceptible to this form of SV and investigate the enzymes that limit or promote Alu-mediated rearrangements. These lines of inquiry could find regions prone to instability in human cancers and lead to targets for therapy.
Daniel graduated from Florida Gulf Coast University (FGCU) with a degree in Biotechnology, while working with Dr. Takashi Ueda in the school’s Biotechnology lab. Upon graduating from FGCU, Daniel immediately joined the Tewhey Lab as a Research Assistant and concurrently enrolled in the Professional Science Masters (PSM) program in Bioinformatics at the University of Maine. As a Research Assistant in the Tewhey Lab, Daniel contributes to several different projects in both the “wet” and “dry” lab but his expertise is in Biotechnology methods. In his free time, Daniel studies for a Master’s degree but is also an avid Cyclist, Climber, Kayaker and all-around outdoorsy type.
I have a B.S. in Applied Mathematics from NYC College of Technology. I started my time at Jax in 2014 as a Software Quality Assurance Intern, joined Computational Sciences as an Associate Scientific Software Engineer in 2016 and became a Scientific Software Engineer in 2018. I'm a "full-stack" engineer who works on a variety of problems including genomics algorithm optimization in C/C++, RESTful API design and implementation using Flask, software reliability engineering, cloud computing, and project and systems design. I'm currently pursuing an M.S. in Computer Science as a member of the inaugural class at The Roux Institute at Northeastern.
Diabetes is a complex genetic disease with significant environmental components. While previous studies have identified numerous human genetic variants that are significantly associated with diabetes risk, the biological mechanisms through which they impact disease etiology and interact with diabetes-relevant cellular stresses (inflammatory stress, endoplasmic reticulum stress etc.) still remains incompletely understood. My research focuses on human pancreatic islets – mini-organs responsible for secreting hormones that regulate blood sugar levels - and utilizes high-throughput multi-omic approaches within an integrated computational framework to understand how these diabetes-associated variants (dys)regulate responses to cellular stresses and thereby contribute towards islet dysfunction.
Hannah Blau completed her Ph.D. in Computer Science at the University of Massachusetts Amherst. She earned the B.A. in French from Yale University and the M.S.E. in Computer and Information Science from the University of Pennsylvania. Prior to JAX, Hannah worked primarily in the areas of data science, machine learning, and natural language processing. She gained international experience at the Artificial Intelligence Center of the Bull Corporation (Louveciennes, France), and in the Machine Learning Group of the Daimler-Benz Research Centre (Ulm, Germany). She worked as a Research Scientist in the Automated Reasoning Group of the Honeywell Technology Center (Minneapolis, Minnesota). While in grad school she served as data scientist in the lab of Professor M. Darby Dyar, Chair of Astronomy at Mount Holyoke College and member of the science team for the Mars Science Laboratory (Curiosity rover). Hannah joined the Robinson Lab in May 2017.
A mouse's genetics are reflected in its phenotype, its measurable characteristics including appearance, behavior and physiology. We work on the Mouse Phenome Project, an international collaborative effort seeking to comprehensively characterize a large set of commonly used and genetically diverse strains of mice and other reference populations. All the data are collected and disseminated from the Mouse Phenome Database (MPD) and include data relevant to addiction, atherosclerosis, blood disorders, cancer susceptibility, neurological and behavioral disorders, sensory function defects, hypertension, osteoporosis, obesity and other research areas. MPD also contains extensive genotypic data, which allows for genotype-phenotype association predictions and facilitates efforts to identify and determine the function of genes participating in normal and disease pathways.
Previously worked as an Animal Care Technician in Bar Harbor, and as a Lab Manager in Ching Lau's Pediatric Cancer group. In Ching's Lab, I also lead a PDX Study of rare pediatric brain cancers and performed all mouse and cell culture work associated with these samples.
Our lab studies the “fitful” mouse as a model of generalized idiopathic epilepsy. Fitful mice carry a spontaneous mutation in the Dnm1 gene, which we described in 2010. Disruption of dynamin function in mice impairs SV endocytosis, with a more dramatic effect during high levels of neuronal activity. Heterozygous mice develop spontaneous and handling-induced seizures at 2 to 3 months of age, but otherwise appear normal. Homozygous mice have a more severe phenotype, including seizures that often lead to death before weaning age, ataxia and neurosensory defects, highlighting the importance of synaptic vesicle recycling in the brain.
Recently, human patients have been identified with mutations in DNM1. These patients all have very severe early epileptic encephalopathies and present early in life with seizures, developmental delay and intellectual decline among other comorbid issues. Currently, we are employing the Dnm1ftfl and conditional Dnm1null alleles to create inducible genetic models that do not exhibit these polymorphic comorbid effects. Preliminary observations of Dnm1ftfl/floxmice in combination with various neuronal subpopulation-specific Cre strains have demonstrated unique seizure phenotypes. These results suggest the possibility that the behavioral comorbidities may be separate from the seizures and that the gene defect may be pleiotropic in different neuron types.
I have used my formal training in molecular genetics in a variety of research areas: plant biology, immunology, dermatology and currently mouse behavior. I have been able to dissect the regulation of genes, identify genes underlying traits, map Quantitative Trait Loci (QTL), and identify modifier genes within different inbred populations. Currently I am using integrative functional genomics in the online software www.GeneWeaver.org, developed by Chesler et al., to bring together different types of data across numerous species and to utilize this convergent evidence to elucidate and validate the roles of genes in disease. I am also curating the addiction and alcoholism literature for the database, identifying relevant large-scale genomic studies and making these often incomputable data, computable.
The primary theme of my personal research program is “bridging the digital biology divide,” reflecting the critical role that informatics and computational biology play in modern biomedical research. I am a Principal Investigator in the Mouse Genome Informatics (MGI) consortium that develops knowledgebases to advance the laboratory mouse as a model system for research into the genetic and genomic basis of human biology and disease (http://www.informatics.jax.org). Recent research initiatives in my research group include computational prediction of gene function in the mouse and the use of the mouse to understand genetic pathways in normal lung development and disease.
My institutional responsibilities at The Jackson Laboratory include serving as the Deputy Director of the Cancer Center and as the Scientific Director of our Patient Derived Xenograft (PDX) and Cancer Avatar program. The PDX program is a resource of deeply characterized and well-annotated "human in mouse" cancer models with a focus on bladder, lung, colon, breast and pediatric cancer. This resource is a powerful platform for research into basic cancer biology (such as tumor heterogeneity and evolution) as well as for translational research into mechanisms of therapy resistance and therapeutic strategies to overcome resistance.
KRAB zinc finger proteins (KRAB-ZFPs) are mouse proteins that regulate gene expression. They interact with transposable elements (TEs), repetitive DNA sequences that can move within the genome.
In mouse embryonic stem cells (ESCs), KRAB-ZFPs bind to specific TE DNA sequences using their zinc finger domains. This binding recruits other proteins like KAP1 (KRAB-associated protein 1), which modify the surrounding chromatin structure. This creates a repressive environment, preventing TE activity and gene expression.
Different mouse ESCs have different KRAB-ZFPs and TE compositions, leading to variations in their interactions. The expression levels of KRAB-ZFPs also change during ESC differentiation, influencing TE regulation.
KRAB-ZFPs play a role in maintaining genome stability and normal cellular functions by repressing TEs and shaping the epigenetic landscape of the genome.
In summary, KRAB-ZFPs in mouse ESCs bind to TE DNA sequences, recruit other proteins to modify chromatin, and repress TE activity. The specific KRAB-ZFPs and TE compositions vary between ESCs, and their regulation is important for genome stability.
John graduated from University of Dayton with a B.S./M.S. in Biology. It was there he first began his interest in gene regulation, particularly cis-regulatory elements, while working with Dr. Thomas Williams studying the evolution of pigmentation patterns in Drosophila. Following graduation John joined Dr. Victoria Meller’s lab at Wayne State University working as Lab Manager and Research Assistant studying the impact of chromatin conformation on dosage compensation in fruit flies. As a PhD student John hopes to continue to probe the logic underlying CREs and how advancements in experimental methods can enhance this study. In his free time John enjoys making music, running, and playing tennis.
The Carter Lab uses large-scale data to strengthen the interface between experimental systems and common disease in humans. We develop computational methods to analyze genetic architecture, design translatable studies of model systems, perform data alignment to precisely quantify disease relevance, and share data through open science platforms. Our primary focus is leveraging genetic and genomic data to identify and test potential treatments for Alzheimer’s disease. We are creating and characterizing new mouse and marmoset models to understand the origins and progression of dementia in molecular and pathophysiological detail, and using these models to assess diagnostic biomarkers and perform preclinical testing. Through this work, we are derisking molecular targets to accelerate the discovery of precise, targeted therapeutic approaches. We are embedded in a network of collaborative projects and centers including MODEL-AD, TREAT-AD, and MARMO-AD. By integrating knowledge and standardizing analytical strategies across these research consortia, we are maximizing the translational value of computational and experimental research.
My research has focused on the logic of gene expression regulation, particularly in response to stress. In particular, I have utilized and integrated high-dimensional data to address complex questions about regeneration, neurodegeneration, development, and evolution. My role in the Open-AD consortium is to integrate genomic evidence in support of the prioritization and development of new therapeutic targets for the treatment of Alzheimer's disease.
My professional background includes experiences in various industries and in diverse capacities which have giving me the opportunity to understand end-to-end intricacies of software development life cycles and software platforms. During my career, I have had the opportunity to plan, architect, develop, and lead software projects and teams with the ultimate goal of providing solutions that meet and exceed stakeholder’s expectations. My 15 years of work experience includes serving in various arenas, such as Supply Chain, E-Commerce Software Platforms, and Insurance industries
I recently joined JAX, and I am absolutely thrilled with the opportunity to contribute to the goal of research for tomorrow’s cures and personalized treatments. The world of genome research
and bioinformatics is absolutely fascinating, so I am enthusiastic to apply my skills and to broaden my knowledge as a dive deeper into this great domain and inspiring cause.
Rodrigo graduated from Georgia State University with a doctoral degree in mathematics, focusing in deep learning research. As a postdoctoral associate in the Tewhey lab, Rodrigo works on generative deep-learning models for cis-regulatory elements and deriving new synthetic elements for desired regulatory functions. In his spare time, Rodrigo enjoys practicing music, biking, and learning about new AI advances.
I hold a Masters in Bioinformatics minor in Health Informatics. My dissertation revolved around literature based discovery utilized to mine existing drug interaction evidences in the clinical trial Phase I and II studies and predicting novel drug interaction signals with mentions of adverse related events. Few interesting projects during my masters includes mining the highthroughput data to understand changes in gene expression, methylation and variant profiles in cancers such as Glioblastoma Multiforme and lung cancer using the computational methods and systems biology approaches. My research interests are to apply computational and machine learning approaches to understand the molecular mechanisms and biological complexities in tumors leading to better prediction of treatments.
My research has focused on blood transcriptomics and its application in patient-based research. My primary role at the Jackson Laboratory is to support the development of systems immunology and immunoinformatic research capacity – including through the implementation of training programs and the support of projects and investigators conducting immunology research at Jax.
Develops software tools and resources for multi-species data integration in the study of health and disease and researches the genetic and biological basis for relationships among behavioral traits including addiction and other behaviors.
My laboratory integrates quantitative genetics, bioinformatics and behavioral science to understand and identify the biological basis for the relationships among behavioral traits. We develop and apply cross-species genomic data integration, advanced computing methods, and novel high-precision, high-diversity mouse populations to find genes associated with a constellation of behavioral disorders and other complex traits. This integrative strategy enables us to relate mouse behavior to specific aspects of human disorders, to test the validity of behavioral classification schemes, and to find genes and genetic variants that influence behavior.
The tools and approaches we have devised are applicable to other common and rare diseases, and are part of an integrated suite of capabilities for biomedical researchers.
During my time as an undergraduate, I joined a Computational Genetics lab that focused on dog genetics. We worked to catalog the young polymorphic SINE insertions within the dog genome across various breeds. We also looked at how these SINEs were importing polyadenylation signals into the 3’UTRs of dog genes. The work that we did was largely computational, and I found that I had a passion for asking Biological questions and using computational methods to answer them.
Through my interests in computational work, I joined Dr. Vivek Kumar’s laboratory as a Ph.D. student through the Tufts-JAX Genetics program. Now, I am working to develop a new generation of assays that access cognitive function in the context of aging, utilizing long-term monitoring of mice while they carry out ethological tasks. These tasks are simple foraging and learning tasks that mice can carry out and are accessed over several days. This allows for reproducible, scalable, and ethologically relevant analyses.
I'm a computational scientist working at the intersection of genomics, machine learning and nonlinear dynamics in advancing data-driven scientific discovery. Specifically, I'm developing a novel based approach whereby RNA velocity is used to further dissect a purely expression-based clustering using ideas of attractors and basins in dynamical systems. In another direction, I'm also working on developing an integrated mouse phenotyping platform that is an end-to-end solution to data collection, automated behavior quantification using machine learning and downstream genetic analysis for the laboratory mouse.
I earned my PhD in physics focusing on the mathematical modeling of dynamical systems (deterministic and stochastic differential equations) on graphs (complex network topologies) to study the emergence of rich spatio-temporal patterns in various complex systems spanning from physics to ecology. After PhD, I did my first postdoc in the theoretical ecology group at the Institute for chemistry and biology of marine ecosystems in Germany, where I worked on developing mathematical models of biodiversity in multi-species ecosystems competing for limited resources and developed expertise in applying machine learning methods to learn species-species trait relationships. Subsequently, I took a second postdoc at NC state university where I worked on the design and implementation of physics aware neural networks that can learn to correctly predict the dynamics of a system while respecting the underlying conservation laws. I also worked a bit on design of latent models that would enhance the interpretability and explainability of models in scientific machine learning.
At JAX, I'm excited to work on predicting cell fate dynamics by constructing a transcriptome-wide continuous vector field from RNA velocity.
Broad advances in sequencing, imaging, and machine learning are rapidly transforming the nature of biology research, providing rich avenues for discovery at the nexus of experimentation, mechanistic modeling and neural network analysis. My lab uses computational, mathematical, and high-throughput data generation approaches to study how cancer ecosystems function, evolve, and respond to therapeutic treatment. We study problems in cancer sequence and image analysis across a wide spectrum of cancer types, with particular expertise in breast cancer and patient-derived xenografts.
Our lab is actively applying a systems approach to study the genetics of health and disease, incorporating new statistical methods for the investigation of complex disease-related traits in the mouse. We employ a combination of strategies to investigate the genetic basis of these complex traits. We are developing new methods and software that will improve the power of quantitative trait loci mapping and microarray analysis, as well as graphical models that aim to intuitively and precisely characterize the genetic architecture of disease.
Within the Center for Genome Dynamics, we are part of a consortium of investigators with a shared interest in a holistic approach to understanding genetics from an evolutionary perspective. With an eye on the future of mouse genetics, we are also establishing two new mouse resources for complex trait analysis: the Collaborative Cross and the Diversity Outbred.
I am a current MD-PhD student at UConn School of Medicine with a background in physics. I joined the Robinson Lab after two years of medical school to conduct my thesis research.
My research focuses on using the Human Phenotype Ontology (HPO) to describe and understand the phenotypic features of neurodevelopmental disorders in a generalizable and computable format. By improving the way we describe patient phenotypes, we will improve our ability to identify the genetic drivers of these phenotypes. To do this, I work with domain experts to expand the HPO's terminology for neurodevelopmental disorders and to develop tools to translate clinical measurements to the HPO.
I studied Pharmacy and Normal and Pathological Physiology at Comenius University, Bratislava, Slovakia. During my PhD. study, I developed bioinformatics pipelines for analysis of Whole exome sequencing data with aim to identify DNA mutations leading to metabolic or sensory disorders.
At my current position, I work on development of novel algorithms and software tools for integrative analysis of clinical/phenotype data with high-throughput genomics data, such as long read whole genome sequencing.
This work includes ingest, curation, and management of large volumes of data which I use for training and validation of statistical models. I am also responsible for assembly of custom bioinformatics pipelines and their deployment on distributed computational systems. Finally, I work on evaluation of models on real patient data with aim to improve diagnostics and enable precision medicine.
After receiving my degree in Museum Studies in 2010, I moved to St. Louis, MO to work at the Saint Louis Science Center as an exhibits and program evaluator. In 2018, I participated in a museum conference that changed my career trajectory by introducing me to human computer interaction concepts. I later found a bootcamp program for user experience design which then connected me to my first job as a UX designer. I joined CS in August 2022 and primarily design for BioConnect, but I also enjoy working collaboratively to increase usability across all CS applications.
“Quality is not an act, it is a habit.”— Aristotle. I am passionate about testing and validating software systems that help solve problems and work with my team to ensure that a quality product is delivered .
I hold Bachelors in Electronics Engineering and I have over 18 years of experience in Software Testing and validation. I have been involved with testing real time systems and complex integrated systems. I have led testing teams in various industrial domains such as E-Commerce, Oil and Gas, Nursing education and storage and network controller testing. This has helped me develop an insight to looking at applications from design as well overall system perspective. I love to work with my team at The Jackson Laboratory to develop and deliver quality software which helps scientific community in their research efforts to make a difference in biomedical world!
I am driven by the stories microbes tell in order to live. My goal in research is to understand the functional diversity of the human microbiome by developing computational tools to facilitate the characterization of less studied species, strains and genes. My academic and research training allow me to probe this fundamental microbiological question using the wealth of publicly available 'omics data using computational methods. As an undergraduate at Bard College I majored in both biology and computer science and as a Ph.D. student in Dr. Deborah Hogan’s laboratory at Dartmouth College I studied microbial interactions between bacterial and fungal opportunistic pathogens. Now, as a postdoc in Dr. Julia Oh's lab, my focus is centered on deciphering molecular mechanisms of microbial and host interactions within health- and disease-promoting microbiomes, with a focus on the opportunistic pathogen Staphylococcus epidermidis. Specifically, I aim to develop and test the utility of a machine-learning model to identify virulence-associated transcriptional signals, validate the predictive utility of the model using gene knock-down experiments, and apply an investigation of gene essentiality to S. epidermidis behavior in polymicrobial co-culture.
Dartmouth College Ph.D., microbiology and immunology Adv: Deborah Hogan 2015-2021 Bard College B.A., biology and computer science 2011-2015
Mutation, recombination, and chromosome assortment account for all genetic diversity in nature, ranging from variants associated with disease to adaptive genetic changes. Despite their fundamental significance to genetic inheritance, the frequencies of mutation and recombination and the strength of chromosome transmission biases vary tremendously among individuals.
The broad objective of my research group is to understand the causes of variation in the very mechanisms that generate genetic diversity. Toward this goal, we pursue two complementary research strategies. First, we leverage the recognition that mutation rate, recombination frequency, and biased chromosome transmission are themselves complex genetic traits controlled by multiple genes and their interactions. We combine cytogenetic and genomic approaches for assaying DNA transmission with quantitative genetic analyses in order to identify the genetic and molecular causes of variation in these mechanisms. Second, through targeted investigations of loci with extreme recombination or mutation rates, we aim to illuminate the biological mechanisms that stimulate or suppress these processes. We are currently using this latter approach to investigate recombination rate regulation, patterns of genetic diversity, and the evolutionary history of the mammalian pseudoautosomal region.
I worked as a programmer on medical software for 6 years, where I gained a strong background in software development. When a bug could negatively impact the health of a patient, it really impresses upon you the importance of attention to detail, testing, and writing bug-free, supportable, and well-documented code.
My professional career started in the US Army Corps of Engineers. While stationed in Alaska, the Marshall Islands, the Republic of Korea and Iraq, I worked as a construction engineer, project manager, explosives and demolitions trainer, and logistics officer. After military service, I have been involved in both the worlds of startups and academia. I have led projects in a variety of fields including: hydrologic warning, field maintenance operations, crisis informatics, oceanographic event analysis, and software development. I have also been an assistant instructor in the Department of Spatial Information Science and Engineering at the University of Maine.
I hold a B.S. in mechanical engineering from the U.S. Military Academy at West Point, and a M.S. in environmental engineering from the the Missouri University of Science and Technology.
Ardian is a graduate student affiliated with UConn Health and The Jackson Laboratory. Under the supervision of Dr. Christine Beck, he is studying the impact of structural variants on diverse genomes, focusing on the effects of transposable element variants on stem cell transcription.
In my current role and throughout my research career, I have worked at the cutting edge of high throughput sequence data, bioinformatic analysis, and population genomics to answer research questions in evolutionary biology. In my present position I have a particular focus on the application of long-read sequencing technologies and on the transitioning of analysis methodologies from single, linear reference genomes to genome graphs.
I received my Ph.D.in physics from Rutgers University – New Brunswick in October 2017. During my Ph.D. work, I used machine learning to understand complex epistatic interactions among networks of correlated amino acid substitutions in protein sequence alignments, and I built distributed computational grids to run large parallel molecular dynamics simulations of protein-ligand binding. Here at JAX, I will apply machine learning techniques to new problems at the forefront of genomics and molecular biology.
I mostly work on selection and extraction on biological and morphological features that are indicative of outcomes such as response to treatment, risk of relapse, etc. I am also interested on integrating such features across data types for reliable prediction.
Before coming to JAX, I collected a bit experience in a small variety of fields. My education is in Physics, specializing in the imaging of novel magnetic materials. In particular, I explored the application of yttrium iron garnet as a read/write medium in high temperature hard disk drives. I spent time with a few data science startups; one that performed analysis to detect oil pump suboptimal performance and failure, and another which provided a platform for automated data shaping. My work at JAX consists of applying deep learning methods to image data. From mouse brain MRIs to 3D multiplexed immunohistochemistry images, I seek to extract valuable insights via research and streamline workflows such that scientists are better able to apply their skills where they can make the most difference via software development.
My research interest is focused on investigating key regulatory genes/elements and pathways involved in cancer development from the perspective of genetic alterations. In particular, I am interested in identification of characteristic genetic change which explains phenotype or sensitivity to drugs in a subset of tumors. Moreover, by the integration of next-generation sequencing data with the use of in vivo/vitro patient-derived xenografts and cancer cells, I aim to reproduce and demonstrate their involvement in cancer development. These developed models not only help the better understanding of the mechanism underlie a subset of tumors but also helps the development of novel approaches toward the personalized treatment for patients.
I am a software engineer working to make a difference where biology and computer science meet. I excel in full-stack web applications, bioinformatics, and delivering stable products. I aim to bridge the gap between translational genomics and user experience.
Genetically diverse animal models are critical to understanding complex traits. While the use of classical inbred strains has enabled significant genetic research, these strains do not adequately represent the genetic diversity amongst humans. Multiparent populations like the collaborative cross mice and outbred populations like the diversity outbred mice allow us to model human diversity and better understand the genetic architecture of complex traits. Genetic variation itself, however, can also be considered a complex trait that arises from mutation, recombination, and chromosome assortment. Using diverse mouse strains and computational tools, I am investigating how candidate genes can alter mutation rates and resulting population genetic variability.
Studying genome structure and function through the application of high-throughput DNA sequencing and mapping methodologies such as ChIA-PET (chromatin interaction analysis by pair end tag sequencing).
NHGRI ENCODE (Encyclopedia of coding DNA elements) Consortium– 3D genome mapping of the human and mouse genomes, NIH 4DN (4D Nucleome) Consortium - three-dimensional organization of the nucleus in space and time (the 4th dimension), HFSP (Human Frontier Science Program) - 3D genome studies of memory, learning and epilepsy.
Since 1998 I have been developing scientific software, for instance computational fluids dynamics on desktop and the web and data acquisition and analysis at the UK's largest science project (Diamond Light Source). At JAX I have been working with Chesler Lab to create a graph database which maps genetic information between humans and mice. Recently I have been working moving image analysis software to the cloud. Scientific software development is where my heart is and what makes the most interest for me at work.
Works in the crossroads of microbiology, immunology, and multi-omics analyses (esp. Metabolomics), focused on the metabolomic tools development and the applications of metabolomics in microbe-host interaction and immunological studies.
I have been working across multiple disciplines including immunology, microbiology, and bioinformatics. My research efforts during my Ph. D has built towards the application of multi-omics approaches to understanding microbe-host interaction. Thrilled by the recent development of metabolomics, I committed my efforts to develop metabolomic tools and analysis pipelines to decipher metabolic phenotypes in immune-related disorders and other diseases.
My current research includes developing metabolomic tools to the reconstruction of biochemical networks. The milestone towards this will be developing tools to upgrade genome scale metabolic models by using mass spectrometry data, via a combination of computational, genetic, cellular and isotope tracing techniques.
I have been involved in multiple projects since I first started doing research in 2015. They have ranged from electrochemistry to cancer, to neurodegeneration and ageing. I have a solid background in benchwork research and to further develop my multidisciplinary skillset in research, I have recently made a transition to bioinformatics. I am currently working to understand how epigenetics and transcriptional profiles, in immune system cells, drive clinical presentation in Systemic Lupus Erythematosus. Ideally, in the future, I hope to conduct clinical trials and put novel pharmaceuticals on the market.
My background is in evolutionary biology and modeling multidimensional phenotypes. My current work involves analyzing various sources of genomic and epigenomic data to better understand their interactions and role in driving Alzheimer's disease and other complex conditions. I use various computational approaches to model the heterogeneity underlying such conditions, and to align human data with mouse models.
I have always been fascinated by the genetic diversity of the genome, both in terms of its functional consequences, as well as its enormous power as a population genetics tool. My previous studies have focused on population genetics, history and phylogeographic patterns of humans and great apes, and the effects of Y-chromosomal genetic diversity on male in/sub-fertility. Over the years I have become increasingly interested in the diversity and evolution of complex regions and their potential association to phenotypes. Chromosome Y in particular, presents with unique challenges but at the same time offers unique opportunities, with obvious translational implications on male fertility, but also as a tool to investigate male-specific patterns of population structure and migration history.
I am currently utilising long-read sequencing technologies and chromatin interaction methods to interrogate previously poorly studied regions, with a longer term research goal of re-evaluating the evolution and structural diversity of known and novel complex genomic regions.
Peter Hansen studied Bioinformatics at Freie Universität Berlin, Germany. He worked as a research associate at the Institute of Medical Genetics and Human Genetics of the Charité University Hospital in Berlin and received his Ph.D. degree in Mathematics and Computer Science in 2019. He gained practical experience in collaboration with human geneticist at the Charité University Hospital and developmental biologists Max Planck Institute for Molecular Genetics. Furthermore, he developed various software tools for NGS data analysis including the ChIP-seq peak caller Q and a desktop application named GOPHER for the design of capture Hi-C probes. Peter joined the Robinson Lab in April 2019. He will contribute to a project that aims to improve diagnosis and therapy of ME/CFS by developing software that integrates immunoprofile and metagenomics data using machine learning techniques.
I have a background in research and model development in glaucoma, diabetes, and COVID-19. My current interests include using single cell and spatial transcriptomic technologies to investigate disease mechanisms and improving translation of animal research using genetically diverse models and cross-species analyses.
Alzheimer's disease (AD) is the leading cause of age-related dementia, but the underlying causal mechanisms of AD neurodegenerative phenotypes are relatively unknown. Microglia, the resident immune cells of the central nervous system, are known to participate in pruning of neuronal synapses under homeostatic conditions, and produce diverse activation states in the AD brain with relatively unknown consequence. By combining the Howell lab's expertise in mouse genetics and neurodegeneration, my background in immunology and genetics, and collaborative resources at JAX, we are investigating how states of activated microglia cause susceptibility to neurodegenerative phenotypes at the level of the neuronal synapse.
Although trained as a developmental biologist, I have been working since 2000 in the area of semantic data integration for biological knowledge. Sequencing of whole genomes and development of large-scale genomic technologies, coupled with traditional experimental biomedical research, has resulted in the generation of vast amounts of information about genes and how they function. My work in the Blake lab focuses on two aspects of making our knowledge about genes manageable and accessible to researchers. First, I am an ontology developer for The Gene Ontology Consortium, working to develop formal networks to describe how genes act and how they achieve their overall biological objective in a species-neutral context using modern ontology-development principles. Second, I manage biological data capture and representation, particular those data derived from studies of the laboratory mouse. My work is integrated into two major bioinformatics resources: Gene Ontology and Mouse Genome Informatics.
I have an MS in Bioinformatics from Northeastern University and a BS in Computer Science With Specialization in Bioinformatics from University of California San Diego. My career has spanned from pure bioinformatics-related research in proteomics to developing and scaling genetic sequencing software platforms, primarily focused on biomedical and computational sciences and related software engineering. In April 2023 I joined JAX in the computational sciences division to support research via the development of software tools and services.
We are interested in generating next-generation mouse models and novel therapeutics. Our primary focus is to develop and validate novel tools/reagents for rapid generation of mouse models of human disease using CRISPR/Cas9 and Integrase systems, identify genetic modifiers, and to treat the underlying cause of the disease.
Development of state-of-the-art technologies is essential to precisely and rapidly generate and characterize mouse models of human disease. Since 2015 we have been utilizing advanced genetic engineering technology, including CRISPR/Cas9 and Bxb1 recombinases to uncover several major findings: a) Gene disruption in mouse embryonic stem cells or zygotes is a conventional genetics approach to identify gene function in vivo. However, because different gene disruption strategies use different mechanisms to disrupt genes, the strategies can result in diverse phenotypes in the resulting mouse model. To determine whether different gene disruption strategies affect the phenotype of resulting mutant mice, we characterized Rhbdf1 mouse mutant strains generated by three commonly used strategies—definitive-null, targeted knockout (KO)-first, and CRISPR/Cas9. we found that Rhbdf1 responds differently to distinct KO strategies, for example, by skipping exons and reinitiating translation to potentially yield gain-of-function alleles rather than the expected null or severe hypomorphic alleles. These findings have significant implications for the application of genome editing in both basic research and clinical practice, b) Mice have been excellent surrogates for studying immune system and, moreover, murine models of human disease have provided fundamental insights into the roles of human macrophages and neutrophils in innate immunity. The emergence of novel humanized mice and high-diversity mouse populations offers the research community innovative and powerful platforms for better understanding the mechanisms by which human innate immune cells drive pathogenicity. We have been developing advanced genetic engineering tools, including Bxb1 recombinase system, sophisticated profiling technologies, and nanoparticle (NP)-based-targeting strategies to understand how genetic differences underpin the variation in macrophage/neutrophil biology observed among humans.
We are looking for a brilliant and self-motivated individuals to join our team to generate and validate humanized mouse models of cancer using novel gene-editing technologies. This position is ideally suited for an individual with a strong background in molecular biology (CRISPR/Cas9) and next-generation sequencing methods.
Exceptional candidates can show early independence through the JAX Scholars Program.
In the Howell lab, we apply genetics and genomics approaches to identify fundamental processes involved in the initiation and early propagation of age-related neurodegenerative diseases, focusing on Alzheimer's disease, non-Alzheimer's dementia and glaucoma. Understanding these processes provides the greatest opportunity of therapeutic intervention. We are particularly interested in the role of non-neuronal cells including astrocytes, monocyte-derived cells (such as microglia), endothelial cells and pericytes.
In previous work, I applied novel genomics and bioinformatics strategies to identify new molecular stages of glaucoma that preceded morphological changes. Genetic knockout and/or pharmaceutical approaches showed that targeting the complement cascade and endothelin system significantly lessened glaucomatous neurodegeneration in mice. Our work with glaucoma continues in collaboration with Dr. Simon John, and we are also now applying similar genetics and genomics strategies to understand initiating and early stages of Alzheimer's disease, vascular dementia and other dementias. A major aim is to combine knowledge from human genetic studies with the strengths of mouse genetics to develop new and improved mouse models for dementias and make them readily available to scientific community.
I am currently a postdoctoral associate in the laboratory of Dr. Roel Verhaak where my research has focused on brain tumor evolution and heterogeneity. To uncover the evolutionary trajectories that brain tumors take from initial diagnosis to disease recurrence, I co-led an international longitudinal brain tumor sequencing project. Computational analyses of these collected genomics data helped establish the order of somatic events throughout a tumor’s molecular life history and identified the most common evolutionary routes under selective therapeutic pressures. These findings were recently published in Nature, and I continue to be involved with projects that leverage this rich dataset. In a separate study, I have sought to deeply characterize the epigenetic heterogeneity that exists within brain tumors. To this end, I established a single-cell DNA methylation assay that enables genome-wide coverage of the epigenome and applied it to human brain tumor specimens.
The Mouse Genome Informatics consortium (MGI) integrates data from over 40 external resources with hand-curated data from published literature to provide an integrated data resource/website that facilitates the use of the mouse as a model for human disease and biology. My role in MGI is to co-direct, with Joel Richardson, the technical work behind theresource. This includes overseeing the hardware and software architecture and thesoftware/database development for both the back end, where data is loaded/integrated,and the front end website, where data is made available for public researchers. This worksupports most of the MGI programs, including the Mouse Genome Database (MGD) and the Gene Expression Database (GXD).
I model data generated by high-throughout omics technologies in order to explain variation in gene expression and alternative splicing under various conditions such as different cell types, biological sex or viral infection. The resulting models can be used to derive hypotheses about the processes that lead to disease and suggest novel therapeutic targets.
I am interested in modeling complex biological systems. My current focus is to combine multiple data modalities; provide as much insight into Alzheimer's Disease as possible. My previous work was implementing machine learning techniques on high-dimensional transcriptomics data to identify key driver genes in the disease progression of Alzheimer's Disease.
My research has focused on immune-genetics and immune-regulation at a resolution of both population and patient-driven questions. I used high dimensional biological data from WGS and functional immune profiling and applied multi-omics approaches by integrating data from RNA-Seq, PhIP-Seq and microbiome data. I am highly interested in building & deploying statistical models to understand biological patterns.
I introduce myself as a computational biologist studying evolution using genomics approaches. Before joining JAX, I have been studying evolution of mammalian including human and livestock animals using population genomics approach.
Glaucoma causes blindness in more than 70 million people worldwide. A major causal risk factor for glaucoma is the elevation of intraocular pressure (IOP). An increased resistance to the drainage of aqueous humor (the clear fluid filling the front of the eye) from the anterior chamber of the eye causes IOP elevation. However, the molecular mechanisms underlying both IOP elevation and aqueous humor drainage remain unknown. My goal is to fill this gap in knowledge. Using novel genetic tools,modern techniques and a variety of mouse lines, I am presently determining the molecular mechanism of aqueous humor outflow through the Schlemm’s canal (SC), a critical component of the pressure-dependent conventional outflow pathway. We have developed novel techniques and tools to measure outflow and study the SC at a cellular level. Using these tools we have already recently discovered that the SC is a unique vessel that has both lymphatic and blood vessel like characteristics. We are currently exploiting this new finding to obtain information regarding the molecular mechanisms of IOP elevation that can be leveraged to design new therapeutic interventions to prevent glaucoma.
My primary focus at JAX is on developing data management systems and tools for the exploration and visualization of complex biological data and its interpretation. As part of a multidisciplinary team at JAX, I frequently collaborate with both experimental and computational scientists on data analysis and visualization projects.
In my role as a Scientific Software Engineer, I am leading the JAX Synteny Browser, a novel tool for interactive visualization of regions of conserved synteny between two genomes based on their biological properties such as function and phenotypes.
Another hallmark initiative that I am closely involved in is JAX’s Patient–Derived Xenografts (PDX) program, a platform for data management, visualizations, analysis, reporting of cancer models studies. I lead the implementation of interactive visualizations for cancer treatment response studies (SOC) and develop automated software to run robust pipelines for the analysis of genomic variations in cancer models.
I am also a key member of the Mouse Phenome Database (MPD) project, an integrated platform to explore physiology and behavior through genetics and genomics, for which I create highly interactive data visualization tools using the latest cutting edge and open source technologies.
In addition, I also teach several data science and programming courses/workshops at JAX on coding skills in R, Python and SQL.
My doctoral work focused on vertebrate cell biology, but relied on genomic sequence analysis to identify a more tractable homolog of an ostensibly vertebrate-specific gene in C. elegans. I subsequently worked in the pharmaceutical industry on identifying potential drug targets, comparative genomics, RNA sequence assembly, and bulk annotation of sequence data. Later, as a researcher at USEPA, I identified transcriptomic signatures of exposure to various toxicant classes, developed predictive models for chemical risk assesment and worked on computational tools for contaminant screening using time-of-flight mass spectrometry. Since joining JAX, I have been leading small teams of computational scientists and software engineers, as well as developing pipelines for long-read sequence analysis and splice-variant analysis. I have also taught short courses on various aspects of applied statistics, machine learning, software development and bioinformatics.
Understand the genetic and neurobiological basis of complex behaviors that are important in psychiatric conditions such as addiction, ADHD, and depression using genomic, neural circuit, and computational tools.
The Kumar Lab consists of geneticists, neuroscientists, and computer scientists. We are passionate about discovering novel targets and models for mental illness through innovation at the confluence of computational, genetic, and genomic methods. Broadly, we are interested in development of better animal models and animal phenotyping methods for human psychiatric illnesses. We use computer vision approaches to quantitate behavior and functional approaches to understand its underlying neuronal and genetic architecture. We have developed high-throughput computer vision based methods for ethologically relevant animal phenotyping. In functional genomics work, we use QTL and mutagenesis approaches to discover novel pathways that can be targeted for addiction therapeutics. Our approaches are flexible and can be applied towards many psychiatric phenotypes. In sum, we are a leading research group using genetics as its foundation, and a combination of biochemistry, physiology, imaging, and computer vision techniques to dissect complex behavior in mammals.
Dr. Kumar carried out undergraduate research at The University of Chicago with Dr. Bob Haselkorn. He received his PhD at UCSD working with Dr. Michael G. Rosenfeld and structurally and biochemically characterized transcriptional co-repressors. During his postdoctoral work, Dr. Kumar trained with Dr. Joseph S. Takahashi at Northwestern and UT Southwestern and worked on functional genomics approaches to dissect the genetics of addiction.
I am a clinical fellow with the University of Connecticut in Hematology and Medical oncology. I also hold a physician fellow - visiting scientist position at the Jackson Laboratory for training in cancer genomics for a period of 1.5 years. My research interest lies in studying the interactions of immunity with tumors as well as identifying strategies to effect treatment of cancers. My focus is on computing gene expression profiling of tumor samples as well as experimental design and methodology. I have previously worked as a research trainee at Mayo Clinic Rochester with the department of Gastroenterology and Hepatology.
I am a PhD student in the Tufts University Neuroscience program. I am interested in understanding the contribution of sex-based differences in gene expression in the etiology and progression of Alzheimer's disease. In the Carter lab, I aim to better dissect the heterogeneity of Alzheimer's disease in humans using different model organisms for achieving improved translational outcomes.
I've brought my background in synthetic biology to the Oh Lab with the aspiration to leverage the human microbiome (the bacteria, fungi, viruses, and other microbes that live on us) to promote health, prevent infections, and treat diseases. Here I have been engineering Staphylococcus epidermidis, a ubiquitous skin commensal, to detect and kill pathogens, as well as secrete therapeutics. Additionally, I have been conducting a sizable clinical metagenome study investigating the relationship between aging, health, and the microbiome. This study should help us understand how we can leverage the microbiome to promote healthy aging, combat chronic illnesses, and prevent infections commonly acquired by older adults in healthcare settings. Finally, I have been exploring the use of human skin explants and stem cell derived skin “organoids” to model human skin microbiome interactions. This will allow us and others to test engineered skin microbiome therapeutics, and better identify mechanisms by which the skin microbiome modulates health and disease.
Using cutting-edge sequencing technologies to support new biological discoveries. Interested in Bioinformatics, Pipeline Development, Long and Short Read Sequences, Computational Biology, Population and Evolutionary Genetics.
Trained as a population and evolutionary geneticist, I have extensive experience as a bioinformatician developing research hypotheses and computational pipelines to address the origin and the influence of genetic mutation on an observed trait. The output of my research has broader implications for biomedical advancement and biodiversity.
Currently, I work with different sequencing technologies including long and short reads to develop computational pipelines or maintain existing tools for wider JAX communities. Involved in data mining and exploration to detect a pattern providing new biological insights. Provide scientific services including data quality control, platform evaluation, and troubleshooting.
The research laboratory of Dr. Charles Lee at The Jackson Laboratory for Genomic Medicine develops and applies state-of-the-art technologies to study structural genomic variation and its contribution to human diseases, and vertebrate genome evolution.
Dr. Lee is responsible for the scientific direction and coordination of The Jackson Laboratory (JAX) for Genomic Medicine. He joined JAX Genomic Medicine from Harvard Medical School and Brigham and Women's Hospital and is best known for his discovery of copy-number variation which is widespread and significant in the human genome. Throughout his career, Dr. Lee has received numerous accolades and awards for his research into the human genome, including an Award from the American Association for Cancer Research and the 2008 Ho-Am Prize in Medicine. He is an elected fellow of the American Association for the Advancement of Science (AAAS), a 2014 Thompson Reuters Citation Laureate and is currently president of the Human Genome Organization (HUGO). Dr. Lee is also a distinguished professor at EWHA Womans University in Seoul, South Korea and an adjunct professor at Xi’An Jiaotong University, Xi’An, China.
I hold a M.S. in Computer Science with a background and hands on experience in High Performance Computing, Analysis Pipeline Development, Genome Assembly & Annotation, Microbiome Data Analysis, Long-Read Sequencing (PacBio & Oxford Nanopore). Previously I worked in The Genome Institute (Washingtion University School of Medicine in St. Louis), The Weinstock Lab and Microbial Genomic Services at JAX. I am now a member of Computational Sciences and working on Pipeline developments & Microbiome related projects.
My research interest is to understand the inner workings of cancer cells – the genetic and epigenetic heterogeneity that drive cancer initiation and progression. We utilize computational and sequencing methodologies to identify and characterize the essential epigenetic lesions that guide cancer cells to evolve and escape from anti-cancer therapy. The ultimate goal is to develop novel methods to predict and address tumor evolution.
The application of high-resolution mass spectrometry now enables the measurement in human samples the metabolome, lipidome and small molecules of dietary, microbial and environmental origins. This revolutionary information fills a major gap between genome and environment, with broad applications to diseases and precision medicine. We combine experimental approaches with computational algorithms that identify pathway patterns and integrate chemical reactions and biology.
Probabilistic metabolite and network models for metabolomics. This includes the Mummichog Project, and addresses challenges in the assembly of information in metabolomics.
Reconstruction of biochemical networks and application to immunometabolism. The goal is to upgrade genome scale metabolic models by mass spectrometry data, via a combination of computational, genetic, cellular and isotope tracing techniques.
Multi-omics, multiscale modeling of human immunology. We are generating lakes of data from vaccine studies. Coupled with large-scale data mining and new generation of artificial intelligence, the resulting models shall aid vaccine development, immunotherapy and the fight against many diseases.
I was admitted to Xi'an Jiaotong University at the age of 15. And I received my B. S., and Ph.D. degrees from Xi'an Jiaotong University, China in 2004, and 2010, respectively. Before I worked in Blekinge Institute of Technology, Sweden and Zhengzhou University, China. I have published lots of journal and conference papers in the areas of machine learning, E-Health system and DNA computing.
At JAX, I engaged in multi-omic data analysis, including epigenetic pattern mining and algorithm development, and long-read DNA methylation detection research.
I joined the Jackson Laboratory having previously worked on a diverse set of organisms. My Ph.D. focused on population and conservation genetics of a submersed aquatic plant species, which was targeted for restoration efforts. Prior to joining Jax, I was a member of the Department of Entomology at the Smithsonian Institution. In that role, I was responsible for the generation and analysis of target enrichment data used in phylogenomics. At Jax I have been involved in research projects associated with the Jax PDX program, PDX Network, PIVOT consortium, and Cube initiative.
My research is focused on the study of epigenetic, transcriptional and splice-variant transcriptome changes in innate and adaptive immune cells in the context of aging and vaccination. The integrated genomic analysis is correlated with specific functional analyses involving the key innate and adaptive immune cell subsets and the magnitude of specific immune response of the vaccinated subjects. The overall goal is to determine the epigenetic factors and transcriptional alterations associated with immunosenescence, which is linked to a decline in the protective immunity including response to vaccination.
Susan McClatchy has worked in bioinformatics education at the Jackson Laboratory since 2004. She has many years of experience training scientists at external academic institutions, at conferences, and at home in genomics, bioinformatics, and computer programming. She manages the Bioinformatics Training Program at JAX and collaborates internationally with scientist-educators from Software and Data Carpentry, nonprofit organizations with a shared mission to build data science fluency in the global research communities that they serve.
Grants, Honors And Accomplishments
AAAS Science Prize for Inquiry-Based Instruction (2013)
Treasurer, The Carpentries Executive Council (appointed for 2018 - 2019)
Treasurer, Software Carpentry Foundation Board of Directors (elected for 2017)
JAX Director’s Innovation FundThe Jackson Laboratory Bioinformatics Training Program (Churchill / McClatchy) (2016-2018)
NIH BD2K Open Educational ResourcesCurriculum Development and Training for Systems Genetics (Churchill) (2016-2019)
As an evolutionary biologist, I’m interested in the molecular mechanisms that shape genetic diversity and drive adaptation. Through my work, I aim to uncover new insights into these processes and their implications for human health. Ultimately, I am driven by a vision of using this knowledge to develop innovative research projects that can lead to the improvement of human health. In the Robson lab, I’m part of the Molecular Phenotypes of Null Alleles in Cells (MorPhiC) initiative. I implement single-cell comparative transcriptomics to identify target genes that possess primate-specific features and are implicated in human disease. Before joining the Robson lab, I worked mostly on the evolution of the Major Histocompatibility Complex (MHC) in platyrrhine monkeys, with a focus on gene duplication and species hybridization. Other projects involved phylogeography and conservation genetics of primates in the Americas. More recently, I was part of a familial glioblastoma study that aimed at identifying heritable genetic variants associated with an increased risk of the disease.
At the Jackson Laboratory for Genomic Medicine, I am involved in several projects that explore the genome-wide patterns of genetic and transcriptional alterations characterizing human cancers. In particular,I am interested in the identification of key regulatory genes and/or pathways as well as complex rearrangement profiles, which may explain tumor initiation and progression as well as provide valuable targets for the development of novel therapeutic approaches and prognostic biomarkers. At present, I am investigating critical genomic aberrations implicated in ovarian and breast cancer tumorigenesis. By exploiting next-generation sequencing technologies combined with in vitro cell culture models and in vivo patient-derived xenografts, I aim at a better understanding of the individuality of cancer genomes and at the development of novel approaches toward the personalized management of cancer patients.
In general I am interested in cell differentiation. To resolve how cells differentiate, I have used a combination of large transcriptional data, computer modeling and cell culture for finding signaling pathways. At a broader level, I am curious how cells from different origins approach heterogeneity for performing specific tasks.
It has become clear that genetic background, including both common and rare variants,
significantly influences disease susceptibility, severity, prognosis and even treatment
effectiveness. Most genetic variants assert subtle effects in isolation, but certain combinations
can disrupt normal homeostasis and sensitize an individual to disorder. Thus, many complex
diseases have resisted classification by single-gene experimental and/or statistical modeling
approaches. A comprehensive characterization of the genetic etiology of complex disorders and
disease must account for the effects of all inputs (e.g. genetic variation) on all outputs (e.g.
transcription, measures of structure/function) in the context of the affected system.
overarching research goals are to 1) characterize the transcriptional network architecture
underlying normal organ development and homeostasis, 2) predict the genes, gene-gene
interactions, and coregulated gene cohorts with major roles in this process, and 3) identify and
validate genetic mutations with individual small effects that together disrupt the buffering
capacity of the transcriptional network and cause a disordered/disease state. To that end, I take
a systems genetics approach that integrates advanced computational methods and
experimental validation techniques to next-generation genetic mapping populations, including
the mouse Collaborative Cross and Diversity Outcross, to elucidate and compare the
transcriptional network structure and dynamics driving organogenesis (the embryonic gonad at
the critical time point of primary sex determination) and adult tissue homeostasis (liver).
During my postdoctoral training, I transitioned my research focus to the single-cell level, specifically delving into the investigation of epigenetic patterns in Acute Myeloid Leukemia (AML) within the context of aging. Additionally, I am working on establishing correlations between somatic mutations and these epigenetic changes. This in-depth analysis, which combines multiple types of biological data, has the potential to facilitate early detection, treatment, and prevention of AML in the aging population. We are fortunate to be in an era where biological data is exponentially increasing, awaiting deciphering. The analysis of such vast datasets necessitates the development of novel computational methodologies, a realm I am actively exploring in my postdoctoral research.
Obesity and Type 2 diabetes mellitus (T2D) are highly prevalent metabolic diseases that afflict a large proportion of the aging population in the United States. Nearly 40 percent of adults are obese, and about 10 percent of individuals over 65 have T2D. These diseases, together with cardiovascular disease, should be viewed as aspects of a metabolic syndrome that is a result of the interaction of many genes, rather than a collection of separate entities. To illustrate the complexity of the issue, there are approximately 500 to 1,000 genes in mice that may lead to obesity when mutated. Our program aims to identify new obesity and type 2 diabetes mutations and their genetic modifiers and to determine how the underlying mutations cause the disease phenotype.
One focus of our investigations are ciliopathies (diseases caused by impaired function of primary cilia), which combine aspects of metabolic syndrome with sensory loss. Our laboratory identified a human gene, ALMS1, that is mutated in patients with Alström syndrome, a rare inherited condition characterized by childhood obesity, retinal and cochlear (inner ear) degeneration, type 2 diabetes, proliferative and dilated cardiomyopathy, hepatosteatitis, and kidney disease.
My initial work on childhood lupus established that children with worse lupus symptoms tended to have higher frequencies of cells with an Interferon-Stimulated Genes (ISGs) and allowed a more accurate classification of lupus patients based on specific cell types. To explore healthy immune aging using systems immunology, I collaborated with Dr. Arne Akbar (UCL, UK) and we revealed the accumulation of the highly differentiated CD8+ T cells during human aging. These ‘aged’ CD8+ T cells: (i) express multiple markers of senescence, including DNA damage associated proteins and (ii) can develop NK cell-like features over time, including cytotoxicity. More recently I led all efforts to dissect the tumor immune microenvironment in primary and recurrent brain tumors.
Alex is a graduate student affiliated with UCONN Health and The Jackson Laboratory. Under the supervision of Dr. Christine Beck, he is investigating the mechanisms that regulate splicing of exons derived from transposable elements.
Our central goal is to develop microbiome therapeutics to treat human disease. We use diverse tools like genomics and synthetic biology to investigate our microbiome’s role in our health and engineer therapeutics.
Our central goal is to develop microbiome therapeutics to treat human disease. We use diverse tools like genomics and synthetic biology to investigate our microbiome’s role in our health and engineer therapeutics.
My primary area of research is the identification of cancer specific proteins and the development of novel anti-cancer immunotherapies for solid tumors, such as osteosarcoma. Utilizing high performance computing, we combine RNA sequencing and protein analysis in a proteogenomics workflow. The Jackson Laboratory for Genomic performs both long-read sequencing (Pacific Biosciences platform) and short-read sequencing (Illumina platform) on oncology patient samples. Our hybrid sequencing approach allows for generating a highly accurate cancer transcriptome upon which we can explore multiple biological mechanisms, such as RNA splicing and chromosomal rearrangements, leading to cancer specific mRNA isoforms. Mass spectrometry identification of isoform specific peptides aids in selecting candidates for validation in the laboratory. A central goal of my research is to expand current cancer treatment options and provide novel therapeutic agents with improved tumor specificity.
I received my PhD in Integrated Biomedical Science, concentration in cancer biology, from Ohio State University and my MS in Biomedical & Health Informatics from University of Washington. During my PhD, I focused on mouse genetics and genomics from the perspective of a bench scientist; my dissertation work involved investigating mutation types and numbers in preneoplastic cells and tissues from Fhit knockout mice. I completed a postdoctoral fellowship and MS degree simultaneously at the University of Washington where I investigated differential gene expression in the parasite Leishmania donovani.
I am a graduate student in Biomedical Sciences in Uconn Health with the supervisor of Dr. Sheng Li. I am interested in applying machine learning and advanced statistical modeling into biological questions.
Currently, I am involved in carrying out analysis of large-scale data sets to understand the genetics of neurodegenerative diseases. I will be analyzing data from clinical samples and mouse models of Alzheimer's disease to determine how genetic risk factors lead to dementia. Additionally, characterize the effects on the retina of genetic mutations that increase risk for eye disease. This work will substantially broaden our knowledge of the molecular mechanisms behind common neurodegenerative diseases.
Previously, I have been working on problems like understanding the evolution of genomes by identification of evolutionary strata in sex chromosomes of mammals, birds and plants using Markov model of segmentation and clustering, which can further help in resolving many epigentics related problems like X chromosome inactivation, Identification of horizontally transferred genes, which can have evolutionary, ecological and potential biotechnological significance in recipient species and more robust taxonomic profiling of metagenomic data. Beside this, I have been also involved in many projects, which were focused on differential gene expression, functional and pathway analysis of NGS/RNA-seq data.
I recently joined Jackson after many years of diverse commercial software development, including consumer, professional and medical software and firmware. Most recently I worked on a team that developed image processing algorithms for detection of physical features in coronary OCT (optical coherent tomography) scans. Prior to that I developed the image processing chain for a disposable endoscope used for direct visualization of pancreatic ducts. For this product I also designed the control feedback algorithm for automatic illumination control. I also worked on the image processing chain for an optical particle analysis system. While the system had its origin in marine biology research, it's also been used in pharmaceutical and industrial applications. Prior to working with the scientific and medical communities I worked for many years in commercial mapping. In addition to developing consumer and professional mapping applications, I also developed the software for a multi-sensor 360-degree panoramic camera. The purpose of this camera was to collect mapping data from a moving terrestrial platform to complement data collected from the aerial platforms. I look forward to applying these experiences to the diverse problems at Jackson.
I hold a PhD in Life Sciences from University of Tennessee/Oak Ridge National Laboratory Genome Science and Technology Program, with a focus in statistical and quantitative trait genetics of behavioral traits in genetic reference populations and integrative functional genomics of behavior across species and experimental platforms. During my Ph.D. program I investigated the increased precision and resolution in QTL mapping using mouse reference populations including the expanded BXD recombinant inbred (RI) strain panel and the Collaborative Cross (CC) reference population The BXD RI study highlighted the increase in statistical power obtained in using the expanded BXD RI strain panel. The CC study highlighted the increased allelic variation and QTL mapping precision achieved. Upon completion of my Ph.D., I joined the Computational Sciences - Statistics and Analysis group at The Jackson Laboratory in 2012 as a biostatistician, with responsibilities including QTL analysis, expression QTL analysis, gene expression analysis, statistical modeling of diverse biological datasets, and statistical consulting. In addition to my contributions towards the field of quantitative trait genetics and statistical genetics, I have undertaken several other data analysis tasks involving differential gene expression analysis using next generation sequencing technologies, leading to the generation of new or validation of existing hypothesis.
I investigate complex biological systems to identify underlying molecular signatures by integrating omics data from multiple levels of cellular organization. My current research aims to identify molecular underpinnings of Alzheimer's disease employing diverse systems biology approaches and analyze the interaction of such underlying processes. My investigations focus on novel insights into the development and progression of certain human diseases, especially age-related disorders, that may have future implications for personalized medical intervention. To address this research goal, I employ a multidisciplinary approach that combines network biology, cross-species genetics, functional enrichment profiling, and drug signature screening. My past research determined intricate mechanisms underlying human aging, longevity, cancer biology, and other complex disorders.
While studying for my BS in bioinformatics I helped research the SOX family of transcription factors, looking at functional domain analysis using molecular dynamics simulations and evolutionary analysis of variants. I graduated in 2018. I started working as a research assistant to Dr. Kevin Peterson in 2019, maintaining multiple lines of mice to study the Hedgehog signaling pathway during embryonic development. We are currently working to study the regulatory mechanisms of the Gli transcription factors.
Dr. Reinholdt’s research interests are in the development and application of genetic approaches for understanding the etiology and functional consequences of genome variation in the germ line and in pluripotent cells. Dr. Reinholdt is also committed to genetic resource development and has made significant contributions to the early development of high throughput sequencing approaches for genomic discovery in the mouse genome, and more recently the development of novel ES and iPSC cell lines from genetically diverse mice that are enabling platforms for cellular systems genetics.
The focus of my work is bioinformatics, specifically, the design, implementation,management and evolution of community databases. I have been intimately involved in the Mouse Genome Informatics (MGI) program since 1992. MGI provides online access to high-quality, comprehensive, and up-to-date information about the laboratory mouse, to support its use as a model for understanding human health and disease. Together with Jim Kadin, I lead the software and database development teams that support a number of resources,including the Mouse Genome Database (MGD – HG000330), the Gene eXpression Database for mouse development (GXD – HD062499), the Mouse Tumor Database (MTB –CA089713), and the International Mouse Strain Resource (IMSR - LM009693). I am also PI of the MouseMine project (HG004834), which provides a fast, powerful new data warehouse for accessing MGI data.
Our main focus is the Gene Expression Database (GXD), which captures and integrates mouse expression data generated by biomedical researchers worldwide, with particular emphasis on mouse development. Gene expression data can provide researchers with critical insights into the function of genes and the molecular mechanisms of development, differentiation and disease. By combining different types of expression data and adding new data on a daily basis, GXD provides increasingly complete information about expression profiles of transcripts and proteins in wild-type and mutant mice. We work closely with the other Mouse Genome Informatics (MGI) projects to provide the community with integrated access to genotypic, expression and phenotypic, and disease-related data. Thus, one can search for expression data and images in many different ways, using numerous biologically and biomedically relevant parameters.
Peter Robinson studied Mathematics and Computer Science at Columbia University and Medicine at the University of Pennsylvania. He completed training as a Pediatrician at the Charité University Hospital in Berlin, Germany. His group developed the Human Phenotype Ontology (HPO), which is now an international standard for computation over human disease that is used by the Sanger Institute, several NIH-funded groups including the Undiagnosed Diseases Program, Genome Canada, the rare diseases section of the UK's 100,000 Genomes Project, and many others. The group develops algorithms and software for the analysis of exome and genome sequences and has used whole-exome sequencing and other methods to identify a number of novel disease genes, including CA8, PIGV, PIGO, PGAP3, IL-21R, PIGT, and PGAP2.
Tumors are continually evolving collections of cells, characterized by a dynamic interplay among heterogeneous sub-clonal populations that expand and contract under innate and imposed selective pressures. My research couples deep learning imaging techniques with high-resolution molecular assays and matched clinical information to analyze tumors through a framework of evolution. We study the impact of treatment on the dynamics of the tumor ecosystem to elucidate resistance mechanisms and identify potential targets for intervention.
I am interested in using statistical science for answering research questions related to biological science. This involves developing statistical and computational methods for discovering inherent hidden structures in complex biological data and drawing meaningful inferences through such structures.
Focuses on creating and characterizing mouse models that accurately model human disease and therefore can be used to understand neurodegenerative disease and be used in the development of new therapies.
In the MODEL-AD program, my focus is on creating and characterizing improved models of late-onset Alzheimer’s disease to be used in the development of novel therapeutics. This includes generation of new mouse models expressing genetic risk factors previously identified in human disease and phenotyping of clinically relevant traits to be used in preclinical testing.
We also using a genetic knock-in approach to generate more relevant models of familial (early-onset) AD, and to study genetic risk associated with key aging and disease-associated variants at the APOE and klotho loci. As partners in a consortium focused on the role of neuroinflammation in neurodegenerative diseases, we create unique animal models useful for the study of disease etiology (e.g., fluorescent reporters for specific microglia or astrocyte cell states).
We continually strive to develop and implement improved experimental methods by partnering with companies to validate and adopt new technologies. In all projects, a main goal is to make novel resources, included animal models, information, and methods, widely available for both academic and biomedical research. Towards this goal, I am co-Director of an annual hands-on workshop Principles and Techniques for Improving Preclinical Translation in Alzheimer’s Disease.
My perspective on biology celebrates what diversity can teach us. Within the mouse species, I study genetically diverse populations such as the BXD, Collaborative Cross, and Diversity Outbred. With Elissa Chesler and colleagues in the CSNA, my work has reiterated that who a mouse is – their genetic makeup and its sex – matters greatly to how a mouse behaves and how their brain responds. I am particularly interested in comparative and cross-species techniques, which give us perspective on the conserved systems that underpin similar behaviors spanning the animal kingdom.
Though I now work in Computational Science, my training was as a mouse behavioral neuroscientist who studies genetics and genomics. My work convinced me that biologists must do two things to understand complex systems like the brain: 1) collaborate with each other, and 2) use high-throughput techniques like next-generation sequencing and high-performance computing. I am delighted to collaborate with multiple investigators at JAX, where I apply the skills I attained in computational work to find the new biology of disease that will lead us to better treatments.
I received my MS in mathematics and wrote my thesis on graph based image segmentation. I like to work on mathematical machine learning algorithms and optimization problems. I plan to develop and implement state of the art algorithms to solve high impact genomics problems.
In my Current role at JAX, I help maintain and improve our single cell bioinformatics workflows as well as develop infrastructure supporting data processing for new assays offered by the Single Cell Biology Lab. My current focus is mainly on multiplexed spatial omics image processing from theologies like Imaging Mass Cytometry and the Phenocycler system.
I joined JAX in 2001 and have been working on the genetics of renal function since 2003. We utilize many of the resources at JAX to explore the decline of renal function with age. I especially enjoy developing new models and tool to assess kidney function in mice.
Software Development, Machine Learning, Visualization and Image Processing. My goal is to leverage these technical interests to develop software tools that help advance behavioral and genomics research.
My role within the Kumar Lab is to develop software tools that enable and accelerate research into addiction and other behavioral disorders. This often involves developing deep neural networks or employing other computational methods to extract and analyze behavioral metrics of mice observed under many different experimental conditions. I enjoy being challenged to find or develop computational methods that allow our researchers to extract and analize the data that they need to answer important biological questions.
I am currently engaged in the investigation of the impact of mesenchymal lineage cells and immune-regulatory myeloid cells on adaptive immune responses in cancer treatment resistance and metastatic relapse.
My work encompasses analysis and interpretation of genomics and biological data using diverse bioinformatics tools and applications, including flow cytometry data, in vivo tumor data, microscopy data, and transcriptome next-generation sequencing data. In addition to my analytical work, I also served as lab manager, overseeing lab functions and managing housekeeping tasks and research projects and collaborations.
Prior to my current role, I worked as Research Assistant in Genetic Resource Sciences (GRS) and contributed to the development and generation of mouse models to study various diseases for different programs and consortiums, including the Scn1a (voltage gated sodium channel) knockout mouse model generation for genetic analysis of epilepsy and Dravet syndrome, humanized mutations of APOE4 and Trem2 associated with late-onset Alzheimer, and the T-brachyury mutated mouse model for Chordoma Foundation. Later I was brought into In Vivo Pharmacology Services as a specialist to troubleshoot and optimize problematic molecular genotyping assays of internal and imported mouse strains of the Repository.
I am a cell biologist by training, with experience in cancer biology, stem cell biology and regenerative medicine. My primary role at JAX is implementing highly multiplexed protein based imaging assays on a variety of technology platforms. My goal is to aid JAX and UCONN researchers in integrating multiplexed imaging into their studies, from project design to data delivery and analysis. My R&D work includes developing standardized reagent resource databases, testing and establishing newly developed scientific methods and process improvements in existing Single Cell Biology service offerings.
Variation in observable traits, such as disease susceptibility, is pervasive in the natural world. Recent advances in sequencing and computation are providing us with an unprecedented view of patterns of genetic variation within species. A major outstanding challenge is to identify and characterize the specific genetic variants affecting complex traits, and the mechanisms through which they do so.
I am a biologist and data scientist who uses single cell functional genomics to understand the genetic basis of complex traits. I utilize genetically diverse model organism populations together with the methodology of statistical and quantitative genetics to reveal novel mechanistic insights into the biology of complex traits. My background and interests include cancer, cardiovascular disease, diabetes, and obesity/metabolic disorders.
Comparative phenotype analysis and bioinformatics can be used to analyze congenital defects and pathological processes with the objective of discovering new molecular elements and pathways that contribute to disease states.
My research focuses on comparative phenotype analysis and bioinformatics. I develop systems to
integrate and analyze phenotypic information in the context of the genetics and genomics data of the
laboratory mouse within the Mouse Genome Informatics (MGI) project. I am primary developer of
the Mammalian Phenotype Ontology (MP), a controlled, structured vocabulary to annotate
phenotype data, enabling data integration, analysis and computational reasoning. My current
research projects include developing ontological relationships among other human and model
organism phenotype ontologies and database knowledge systems to develop comparative analysis
tools. These tools will be used to analyze congenital defects and pathological processes with the
objective of discovering new molecular elements and pathways that contribute to disease states.
My project focuses on the analysis of spatial single-cell proteomics data (e.g. Imaging Mass Cytometry, CODEX) and spatial transcriptomics data (e.g. Visium Spatial Gene Expression by 10x). Previously, in collaboration with biologists from Leiden University Medical Center I have developed interactive pipelines for the analysis of single-cell high-resolution images (ImaCyTe, SpaCeCo). My research interests also include the analysis of single-cell tissue images with Graph Convolutional Networks.
My primary interest is understanding how the chromatin environment primes gene expression. To study fluid changes in chromatin state and the resulting gene expression changes, we use mouse ESCs and follow them through early development from pluripotency to germline commitment. To investigate epigenetic differences among individuals, we use diverse mouse strains to represent diversity among human beings, studying two paradigms to through the lens of individual diversity: differences in naïve state that impact early development, and differences in mature brain striatum that impact susceptibility to addiction.
Recent advances in DNA sequencing technology led to the generation of vast amounts of sequencing data and provided an unprecedented opportunity to understand the complexity of the genome. However, this massive flood of sequencing data also created challenges in mining patterns of interest from this data. Moreover, the diversity of datasets [RNA-SEQ, ChIP-Seq, Exome, Whole-Genome, ATAC-Seq, HiC, etc.] generated from various technologies also demand more integrated evaluation across multiple sequencing platforms and data types. My dissertation focused on deriving meaningful patterns from these datasets generated from both model and non-model species, studying the variations in non-coding RNA (ncRNA) secondary structure, and developing novel methods for ncRNA detection in the genome using patterns of chromatin modifications. In my role as an associate computational scientist (2014-2016), computational scientist (2016-2019), and then Senior computational scientist (2019-2021) at The Jackson Laboratory (JAX), I worked and led various research projects in collaboration with the faculty. I currently serve as an Associate Director of the Genome Informatics group in the Jackson Laboratory, Department of Computational Sciences and lead a large team of Computational scientists and Bioinformatics Analysts. My group at JAX primarily focused on building systems/algorithms to effectively analyze next-generation sequencing data generated from various sequencing technologies from humans and mice. We have developed particular expertise in the analysis of patient-derived xenograft (PDX) cancer samples as a part of the NCI PDXNet and Pivot program. To sum up, my team focuses on research and building effective systems to help facilitate the research.
I joined The Jackson Laboratory's Computational Sciences Department to become the Deployment Lead in the Lab’s involvement in the NCI Cancer Biomedical Informatics Grid® (caBIG®) initiative and stayed on as a member of the Stats and Analysis group. My background in protein and genomic databases and conceptual representations of life sciences research has enabled me to contribute to the clinical and the PDX programs. I continue to gain experience in analysis of next generation sequencing, calling on my history of bench research in cancer, pharmacology, and transcriptional regulation. I have experienced the roller coaster ride of small start up companies and welcome the opportunity to be part of the JAX community. On any given day, I may spend time wrangling data for PIs or collaborators, launching a DNA-seq analysis, tracking down data for a potential PDX customer and checking out a new analysis tool or online database.
My initial contributions to science were the result of a Master of Science education program (University of Illinois - Champaign-Urbana) endeavor to identify quantitative trait loci affecting swine meat characteristics. Shortly after my leave of the University, I joined The Jackson Laboratory in 2007. The numerous collaborations that took place at the Laboratory have resulted in publications pertaining to a variety of conditions including: alopecia areata, asthma, cancer (of the brain, lung and skin), diabetes, chronic kidney disease, eye disease, and reproductive disorder. The general aim of these studies is to provide mouse model information as a method to improve human health. My role in these studies has been as a biostatistician consultant.
Type 2 diabetes is a disease of genes and environment. My laboratory studies the epigenome of human pancreatic islets and their developmental precursor cells. One aim is to use the epigenome as a read-out of effects of type 2 diabetes genetic variants on islet gene expression programs and function. Emerging evidence suggests that normal or disease-predisposing conditions can actually alter a cell's epigenome and lead to abnormal cellular functions. To this end, my lab is investigating how the islet epigenome is altered under different stimulatory and stress conditions. Finally, we are pursuing targeted modification of cells’ epigenomes to facilitate production of bona fide pancreatic islet cells from pluripotent stem cells or other terminally differentiated cells.
Jagadish Sundaramurthi completed Ph.D. in Immunology at the University of Madras. Prior to joining the JAX, he worked as a Research Scientist in ICMR - National Institute for Research in Tuberculosis and Postdoc in Stanford University and Berkeley Lab in the areas of Immunology, Infectious Diseases, Bioinformatics and Microbiome. His current research is aimed at making sense of genetic variants and elucidating the genetic architecture of Cerebral Palsy in the light of EHR/phenotypes and whole exome sequences of CP patients, Human Phenotype Ontology and other Bioinformatics resources.
Ms. Sundberg has been involved with computer applications for 45 years. She developed herd health management software in 1976, a database for managing pesticide chemicals for the state of Indiana in the mid 1970s, and worked on various other projects for the Administrative Data Processing Center at Purdue University. During the past thirty years she has worked on a project for managing mouse breeding colonies (JAX Colony Management System, JCMS) and a relational database for medical records management (The Mouse Disease Information System, MoDIS). MoDIS was developed in 1987 to manage histopathological data from The Jackson Laboratory massive mouse production colony as well as research data. Over the years this evolved to integrate the Mouse Anatomy Ontology (MA) and Mouse Pathology Ontology (MPATH), to eventually provide an integrated tool for storage of basic research discoveries, linking gross and photomicrographs to case materials, and getting this information into publicly accessible databases such as Pathbase (http://www.Pathbase.net), and Mouse Genome Informatics (http://www.informatics.jax.org). Currently she is providing software quality assurance for these Computational Science projects: PDX (patient-derived xenograft) platform, GeneWeaver, BioConnect, Mouse Phenotype Database (MPD), Study Intake Platform (SIP), Diversity Informatics Platform (GeDI), POET, and PhenoPackets. .
Sabriya completed her PhD in 2016 at the Mayo Graduate School in Rochester, MN where she studied the genome-wide epigenetic mechanisms underlying cellular transitions of the Interstitial cells of Cajal in the gastrointestinal tract. During this time Sabriya became interested in understanding how the epigenomic landscape impacts chromatin organization. To pursue this, Sabriya joined the Imbalzano Lab at the University of Massachusetts Medical School where she explored the role of arginine methyl transferase Prmt5 in mediating higher order chromatin structure in preadipocytes prior to and during adipogenesis using methods like ChIP-Seq and Hi-C. For this work, Sabriya was awarded the Ruth L. Kirschstein NRSA Postdoctoral Fellowship from 2019-2021. After this, Sabriya joined the Lee Lab as an Associate Research Scientist where she is currently exploring how structural variants in humans affect genome organization and modulate transcription.
The purpose of my research at the Ching Lau Lab is to examine genomic and epigenomic data from pediatric cancers in order to discover molecular phenotypes, prognostic biomarkers, and candidate therapeutic targets. My work includes method and pipeline development for integration of multi-omic data in the analysis of pediatric brain and bone tumors to develop a better molecular understanding of these often-lethal cancers. Currently, my research focuses on osteosarcoma, ependymoma, and intracranial germ cell tumors.
Dementia is an outcome of several neurodegenerative diseases, with no treatments currently available. Microglia, the brain’s immune cells, are implicated in resilience and susceptibility to cognitive decline. The goal of my research is to define the interplay between transcriptomic and functional changes that impact behavior in genetically diverse mouse models of cognitive decline and, subsequently, to discover small molecules that intervene in memory loss.
The past decade has seen a transformational change in our understanding of the human genome and the role it plays in influencing disease risk. Large-scale projects such as Encyclopedia of DNA Elements (ENCODE) have identified which non-coding regions correlate with gene regulatory function. Furthermore, the proliferation of genome wide association studies (GWAS) and scans for recent positive selection have identified thousands of loci that influence human health. Taken together, these efforts show the predominant contributors of heritability for complex phenotypes are common polymorphisms that reside within non-coding regions of the genome. However, despite our progress in mapping cis-regulatory elements (CREs) and genetic signatures correlated with disease, very few examples exist that mechanistically link genotypic variation to disease risk. This gap in our understanding is based on our inability to understand the sequence context of active CREs and their targets, without which it is difficult to identify single nucleotide variants that directly modulate gene expression. Thus, given the correct technological advances each disease association can become an untapped entry point that has the potential to transform our understanding of disease etiology.
The mission of our research group is to (1) characterize and learn the grammar of cis-regulatory elements, in both mouse and human models, using novel technological approaches such as high-throughput reporter assays and CRISPR based screens of non-coding regions in the genome. (2) Build upon the knowledge from genome wide association studies and leverage this resource of genetic risk to disease in human populations to construct better animal models that precisely reflect disease phenotypes.
My primary research field is three-dimensional genome organization which includes development of computational analysis methods from data generated using genome technologies such as ChIA-PET, ChIA-Drop, and HiC-based sequencing techniques.
I am primarily trained as a computational scientist. During my Ph.D., I worked on the developing probabilistic machine learning methodologies for various computational problems in engineering systems (uncertainty quantification, stochastic partial differential equations, dynamical systems, inverse problems etc.). As a postdoc, I worked on the problems pertaining to the interpretability of sequence-function relationships learned by DNN models from high-throughput sequencing functional assay datasets. As a computational scientist at JAX, I work on integration of cross-species and multi-omics data.
I study the genetics of complex diseases in humans, with focus on neuropsychiatric and metabolic disorders. I am particularly interested in investigating the polygenic nature of traits, pleiotropy and the missing heritability. A substantial part of my study focuses on population genetics, historical genetics, archaeogenomics, and the evolutionary history of modern humans, along with their implication for diseases and traits in modern humans.
At JAX I am focusing on identifying the contribution of structural variation to the heritability of complex disorders, the evolutionary and population genetics aspects of structural variation, and the bilateral translation of human genetic findings to mice.
I am interested in understanding how interactions between genes (epistasis) contribute to the genetic architecture of complex traits. My adviser, Greg Carter, previously developed an analytical method, called the Combined Analysis of Pleiotropy and Epistasis (CAPE), that combines information across multiple phenotypes to constrain possible epistatic models and thereby infer the direction of interaction between genetic variants. I have packaged the analytical pipeline into a freely available R package. CAPE has been used to infer directed epistatic networks in yeast, Drosophila, and mice, and I am currently working to adapt the method for use in human populations. This adaptation will include development of a new software package for detection and interpretation of epistatic interactions in medical genetics.
Next-generation sequencing technologies have revolutionized biological research and provided unique opportunities to study broad and novel questions about the regulation of gene expression. With these technologies, there has been an exponential increase in the types and amount of high-throughput datasets pertaining to the dynamics of gene expression. These data include gene expression data and genome-wide maps of nucleosome occupancy and open chromatin, epigenetic marks and transcription factor binding sites in cells and organisms under various experimental conditions. In my lab, we develop computational models to take advantage of genomics datasets to study the dynamics and mechanisms of transcriptional gene regulation and identify testable hypotheses for genomic medicine.
I am interested in understanding the genetic architecture of polygenic traits through the integration of omics technologies. To reduce the complexity of this problem, I am focused on putative subtypes that may be influenced by certain environmental stimuli while keeping in scope consistency with evolutionary mechanisms. Our lab, Dr. Greg Carter's group, is focused on Alzheimer's disease (AD), hence, I am utilizing human and model-organism (currently mouse) omics data to investigate potential subtypes of AD. My background is in genetics, where I have focused on various human traits, and also divergent lifespans in fruit flies.
The brain is a unique cellular ecosystem, housing the neurons, glia, microglia, and immune cells that together coordinate its vast array of functions. When a tumor forms in the brain, malignant cells co-opt these interactions to fuel their own growth. As a result, the normal cells that surround these tumors, collectively known as the tumor’s microenvironment, can directly influence a tumor’s developmental trajectory and ability to resist treatment.
The Varn Lab is devoted to understanding how interactions between tumor cells and the microenvironment shape the evolution of diffuse gliomas, the most common malignant brain tumors in adults. To accomplish this, the laboratory relies on data-driven techniques that integrate genomic, transcriptomic, and spatial information from human tissue samples. Projects in the Varn Lab rest on the central hypothesis that disrupting the environment in which a tumor develops will slow the tumor’s ability to grow and sensitize it to therapy. By deciphering these cellular interactions, the Varn Lab aims to generate novel discoveries that can be harnessed to improve brain tumor treatments and prolong patient survival.
I am a full-stack principal level scientific software developer with a passion for software development that enables scientific research. I have over two decades of experience in a variety of industries, with a focus in biotechnology.
My role at Jackson Laboratory is one of technical leadership on multi-developer projects, using agile techniques, striving to provide meaningful/usable systems in a timely manner.
As a Scientific Software Engineer, I help make biological data easier to manage and interpret. I have a B.S. in Bioengineering from the University of Maine which gave me a strong foundation in biology with a technical perspective. After spending a few years at a biotech software startup in Portland, I'm excited to bring everything I've learned to JAX where I hope to contribute to a variety of important projects.
My research focuses on the regulation of gene expression during lung development and in disease. Recent research projects include measuring gene expression in the lungs of three strains of mice during development from embryonic day E9.5 to maturity at 8 weeks of age and developing a new technique to identify direct messenger RNA (mRNA) targets of microRNAs (miRNAs). I am using a new technique, referred to as RIP-SIR, to study miRNA regulated gene expression during the progression of pulmonary adenocarcinoma in two mouse models. In a related project, serum samples collected from these lung tumor-bearing mice were analyzed to identify expression profiles of circulating miRNAs that may indicate the presence of early stage pulmonary adenocarcinoma. A new project is expanding upon this work and using patient-derived xenograft (PDX) mice to answer basic questions about which miRNAs are secreted from tumors and enter the circulatory system. Finally, as a member of a collaborative group headed by Dr. Patricia Donahoe, I am investigating gene expression in the developing mouse diaphragm and determining how novel mutations identified by this group contribute to congenital diaphragmatic hernia, a condition that is often associated with a fatal respiratory phenotype.
My research focuses on revealing the genetic underpinnings of traits that vary continuously in populations, including those sensitive to environmental inputs. During my doctoral training, I used a series of crosses to characterize the relative contributions of genetics and aging to variation in fertility among hybrid male mice. My postdoctoral work focused on identifying novel genetic modifiers of toxicant susceptibility using a genome-wide association study of developmental delay in C. elegans. Throughout my career, I have sought to improve our understanding of human health and disease using genetically diverse model organism populations. At JAX, I support ongoing projects using quantitative genetics in the Diversity Outbred population by developing and maintaining computational workflows for QTL mapping, haplotype reconstruction, and genomics broadly.
North Carolina State University
2014 - 2019
University of Delaware
2010 - 2014
Adv: Dr. Erik Andersen
2019 - 2022
Graduate Research Assistant
North Carolina State University
Adv: Dr. David Aylor
2014 - 2019
Undergraduate Research Assistant
University of Delaware
Adv. Dr. Shawn Polson & Dr. K. Eric Wommack
2011 - 2014
As a passionate technologist who has borrowed ideas heavily from biology in my exciting and challenging 13-years IT careers spanning multitude business sectors, I have never regretted venturing into the life science 18 years ago. My research focus has always been using computational approaches to acquire biological insights from the data. This is commonly done with the many great open-source tools from the research communities. I have had to develop instrument control software and new algorithm to fill in the voids. I have worked extensively on genomic, transcriptomic, epigenomic, proteomics, and glycoproteomics data. My current work focus include long-read and single-cell application in cancer genomes and regulome.
The broad goal of my research is to understand causes of infertility. Currently, I am studying meiotic recombination rate. Too few recombination events leads to infertility, therefore proper recombination is essential for gamete production and thus species survival. It is well established that recombination rate varies widely across species, individuals, and even within an individual's gamete pool. However, the exact loci and mechanisms controlling these variations are largely unknown. I am working to identify the loci responsible for recombination rate variation, as well as working to understand the exact relationship between recombination rate and fertility.
My research is focused on the mediation roles of molecular between exposures and health outcomes. I developed PMDDA data analysis workflow to perform exhaustive metabolomics annotations. I also proposed the concept of 'reactomics' to retrieving general chemical relationships among molecular in biological samples. Meanwhile, I developed 'gatekeeper' model to screen the molecular sensitive to multiple exposures. With background from both wet lab and dry lab, I am trying to improve regular statistical or machine learning model with biochemistry knowledge. I am also interested in reproducible research and built a R based data analysis image (xcmsrocker) for reproducible metabolomics data analysis.
My research focuses on comparative phenotype analysis and bioinformatics. I develop systems to integrate and analyze phenotypic information in the context of the genetics and genomics data of the laboratory mouse within the Mouse Genome Informatics (MGI) project. I am primary developer of the Mammalian Phenotype Ontology (MP), a controlled, structured vocabulary to annotate phenotype data, enabling data integration, analysis and computational reasoning. My current research projects include developing ontological relationships among other human and model organism phenotype ontologies and database knowledge systems to develop comparative analysis tools. These tools will be used to analyze congenital defects and pathological processes with the objective of discovering new molecular elements and pathways that contribute to disease states.
I have a diverse expertise in analysis of Multi-omics datasets, Immunology, Microbiome, Developmental Biology, RNA Biology and Cloud Computing. During my Ph.D. I studied the downstream targets of Hox genes in developing hindbrain using mouse, zebrafish and chick models. My postdoc was focused on developing Bioinformatics pipelines for the analysis of microbiome data and various immunological studies. At Jax I’ve been working with Adam Williams on identification of lncRNAs in RNA-seq and other omics datasets; with Olga Anczukow on developing pipelines for splicing analysis; and with Karolina Palucka on studies in Immunology and Covid-19 research.
The goal of my ongoing and future research is to apply machine learning and statistical methods to computational biology problems. My research emphasizes building a bridge between machine learning and statistical modeling especially deep learning and human health problem solutions.
My first research program focuses on the regulation of the de novo ceramide biosynthesis pathway that determines cellular profiles of sphingolipid metabolites, i.e. sphingoid long-chain bases (LCBs) and ceramide (acylated LCB) species, which have been implicated in many neurological diseases. Using mouse models generated in The Jackson Laboratory or contributed to the mouse depository at The Jackson Laboratory, I have been working toward elucidating the potentially specific neural functions and pathological roles of different LCBs and ceramides, respectively, in two related projects.My second research program focuses on the transcription network controlling photoreceptor differentiation and how deregulation of this network causes photoreceptor degeneration. We adopted a genetic approach to identify novel regulators of this network by searching for genetic modifiers of rd7, a mutation of the transcription factor NR2E3 causing a retinopathy called Enhanced S-Cone Syndrome. We have found several modifiers that suppress rd7. Currently, we are trying to identify the underlying genes and assess their interactions with other genes’ encoding factors involved in photoreceptor differentiation.
Wei has a strong background in host-microbe interaction and evolution of microbial pathogens. He is interested in developing computational tools to more efficiently characterize the microbiome using metagenomic data.
Wei is broadly interested in evolution and ecology of microbes in complex communities using genomic methods. Before joining the Oh Lab, he worked with Dr. Dustin Brisson in the University of Pennsylvania to study host-microbe interaction and evolution of microbial pathogens. Presently he is developing computational tools that characterize microbes and microbial interactions more effectively and with less necessary domain expertise.