Data Science: People

I have a background in computer science, and shifted my interests towards Machine Learning recently. I work on the CUBE project to integrate data across different assays and develop links. But, more recently, I have been working with image processing on COVID-19 detection.

Dr. Amin received his Ph.D. in cancer computational biology from Baylor College of Medicine, Houston, TX in 01/2017. His thesis work was carried out at the UT MD Anderson Cancer Center, and was focused on understanding long non-coding RNA interactions in the context of chromatin organization using integrated analyses of publicly available expression, epigenomic and chromatin interaction data. Before completing Ph.D., Dr. Amin received research training (2008-2011) in computational biology at the Dana-Farber Cancer Institute, Boston, MA where he worked on the assessment of gene expression profiling as predictive biomarker in multiple myeloma. Previously, Dr. Amin received his first professional degree in medicine, MBBS from the Medical College of Maharaja Sayajirao University of Baroda, Vadodara, India in 2005.

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Bri started with JAX, as an IT Project Manager, in April 2016, where she was involved in many key projects, including the launch of CKB’s public site ahead of the ASCO conference in 2016, aiding MCGI during their establishment, and the implementation of the separate clinical infrastructure at JAX Genomic Medicine in Farmington. Bri most recently supported the IT infrastructure team with multiple cloud based collaboration initiatives aimed to improve the means by which our staff collaborate; not only with each other, but external partners and researchers around the globe. These initiatives involved enabling the secure use of Google Genomics Cloud Platform, implementing Webex for video and audio collaboration, migrating TIS to Webex from Adobe connect for their educational Webinars, and bringing JAX a secure, cloud based storage solution that can be accessed from any device anywhere in the world with Box.

Bri has been a project manager since 2011, working in various fields including insurance, pharmaceuticals, and manufacturing, and holds a BS in Physician Assistant Studies, Public Health, and Related Clinical Sciences.

She is looking forward to bringing her skills to the Computational Sciences department.

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!

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I hold a PhD in bioinformatics and Computational Biology with a minor in Statistics. For my dissertation work, I established the essential bioinformatics infrastructure in functional genomics studies using metabolomics as a systems biology tool. I have utilized Mass spectrometry for developing Human Embryonic (hES) and Induced Pluripotent Stem (IPS) cells based assays for drug toxicity screening, for developing a test for biomarkers of autism using patient’s blood and for detecting bio-markers for Escherichia coli O157 infection in cattle from blood samples. I am currently developing a computational metabolomics platform to analyze and visualize mass spectrometry based metabolomics data in JAX. In addition to metabolomics, I am gaining experience in next generation sequencing technologies and cloud computing by developing a tumor neoepitope prediction pipeline for personalized medicine.

Designs pipelines and develops tools to discover structural variations facilitated by Transposable Elements to better understand the potential mechanisms of their formation.

The human immune system is a double-edged sword: essential for maintaining health yet often itself the cause of disease. Understanding how this delicate balance is maintained requires a thorough understanding of its components and its responses to environmental factors. My laboratory is leveraging modern genomic tools to characterize the human immune system in both healthy and disease states. How the immune system deteriorates as part of aging and how the immune system of a mother reacts to the graft of a foreign embryo represent new areas of investigation. Our approach includes the development of humanized mouse models of human diseases. Our goal is to enable the future development of novel therapies for a range of serious illnesses.

Dr. Floris Barthel obtained his Doctor of Medicine (M.D.) degree from the Vrije Universiteit Amsterdam, The Netherlands where he developed an interest in malignant brain tumor (glioma) biology. He joined the laboratory of Dr. Roel Verhaak at the MD Anderson Cancer Center in Houston, Texas for a research fellowship to satisfy his scientific curiosity. There he trained in computational biology and performed the data analysis for several large translational studies investigating the molecular markers of gliomas. Dr. Barthel subsequently relocated to The Jackson Laboratory (JAX) for Genomic Medicine in Farmington, Connecticut where he was awarded the JAX scholar award. The JAX scholar award is a competitive institutional award supporting exceptional postdoctoral fellows become independent investigators.

Shortly after, he was awarded a K99/R00 pathway to independence award to study epigenetic strategies of telomerase reactivation in cancer. He has since been training in molecular biology while continuing translational research on malignant gliomas and telomere maintenance. He has published his research findings in various reputable journals, including Nature, Cell, and Nature Genetics. He was recently named a ForbeckScholar in lieu of his achievements and will attend yearly scholar meetings. He aims to build a basic and translational research program focused on identifying therapeutic vulnerabilities among telomere maintenance pathways in glioma.

I am a software engineer with a background in High Performance Computing. I have developed parallel software (both multi-threaded and distributed with MPI) in a variety of languages including C, C++, and Python. In addition to HPC projects, I have developed a variety of analysis and data management tools in Python (both web applications and command line).  I have also helped design and am currently the primary maintainer of the Civet pipeline framework, which drives analysis pipelines used for the JAX Cancer Treatment Profile and PDX.

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.

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Christine Beck on ORCID

I work as a Senior Scientific Curator on the Mouse Genome Informatics MGI database and on the Alliance of Genome Resources. I also help to develop the Mammalian Phenotype Ontology, the Unified Phenotype Ontology and the Human Disease Ontology.

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       Susan Bello on ORCID       

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.

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 Alex Berger on GITHUB 

My research focuses on functional and comparative genome informatics. I work on the development of systems to integrate and analyze genetic, genomic and phenotypic information. I am one of the principal investigators of the Gene Ontology (GO) Consortium, an international effort to provide controlled structured vocabularies for molecular biology that serve as terminologies, classifications and ontologies to further data integration, analysis and reasoning. My interest in bio-ontologies stems as well from the work I do as a principal investigator with the Mouse Genome Informatics (MGI) project at The Jackson Laboratory. The MGI system is a model-organism community database resource that provides integrated information about the genetics, genomics and phenotypes of the laboratory mouse. My current research projects combine bio-ontologies and database knowledge systems to analyze disease processes with the objective of discovering new molecular elements and pathways that contribute to particular pathologies such as respiratory diseases. 

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Judith Blake on Orcid

Research focuses on the investigation of genetic diversity and genomic evolution of multiple laboratory strains and wild mice.

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.

I received my M.S. in Computer Science and Engineering and B.S. in Ecology and Evolutionary Biology from the University of Connecticut researching biological databases and ontologies. For over ten years I have been leading the development of web applications for multiple Fortune 500 companies in a number of industries including environmental safety, facility maintenance, and media.

I am excited to be bringing my research and industry experience to The Jackson Laboratory community and contributing to the development of robust, innovative software designed to help us solve some of the most complex problems facing the biomedical community today.

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. 

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 Dnm1^ftfl and conditional Dnm1^null alleles to create inducible genetic models that do not exhibit these polymorphic comorbid effects. Preliminary observations of Dnm1^ftfl/flox mice 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.

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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.

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Jason Bubier on ORCID

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.

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Leigh Carmody, Ph.D., obtained her doctorate of philosophy in Molecular Physiology & Biophysics from Vanderbilt University in Spring 2007 where she studied targeting of signaling molecules to F-actin cytoskeleton/dendritic spines. Dr. Carmody continued her studies as a postdoctoral fellow at Massachusetts Institute of Technology where she investigated the role of Rac1 in dendritic spine motility. Late 2008, she joined the Broad Institute of MIT & Harvard as a Scientist where she aided in early-stage drug discovery efforts to identify chemical leads directed at cancer targets and neglected parasitic infections. Dr. Carmody joined Jackson Laboratory in 2015 as a Project Manager, and is currently a Scientific Curator annotating phenotyping and genomics data for the human phenotype ontology (HPO) database.

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Contemporary technologies such as high-throughput genome sequencing now enable the measurement of biological systems with unprecedented scale, power and precision, creating the opportunity to decipher the genetics that underlie human diseases. The overall goal of our laboratory is to develop novel computational strategies that use these data to understand complex genetic systems in which multiple genes and environmental factors combine to affect biological outcomes. These methods aim to map complex genetic architecture and infer models that predict the outcomes of genetic and environmental variation. We derive network models of interacting genes, integrate disparate phenotypic and molecular data types, critically evaluate models with experimental tests, and seek to understand how biological information is encoded in genetic networks and genomic data.

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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.

Gregory Cary on ORCID

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From the early days of my career, while designing and writing programs, from assembler, to C++, to SQL, to JAVA/J2EE, to NoSQL, to front-end JavaScript framework languages; my passion has been Software Development and Architecture, and the interesting study of surging technologies thereof.

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.

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.

Albert Cheng obtained his BSc in Biochemistry and MPhil in Biology from Hong Kong University of Science and Technology in 2005 and 2007, respectively. He studied neurotrophin signaling and C. elegans developmental genetics. He then pursued his PhD in Computational & Systems Biology at MIT in the labs of Profs Christopher Burge and Rudolf Jaenisch and worked on various topics on epigenetics, gene regulation and alternative splicing in stem cells, reprogramming, cancer metastasis, erythropoiesis and differentiation. Cheng and colleagues identified H3K27ac as a signature for active enhancers. He analyzed alternative splicing in epithetlial-mesenchymal transition, cancer metastasis as well as erythropoiesis and identified splicing factors regulating these processes. He constructed CRISPR-on, an artificial RNA-guided activator based on CRISPR/Cas. After graduating in 2014, he joined the Jackson Laboratory at Bar Harbor, ME, as one of the first JAX scholars where he continued to work on understanding and improving the CRISPR/Cas technology. In July 2015, he started his own lab as an assistant professor at the Jackson Laboratory for Genomic Medicine campus at Farmington, CT.

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 Albert Cheng Personal Lab Site 

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.

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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.

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         Jeffery Chuang on ORCID          

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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.

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I received my PhD in genetic epidemiology from the School of Medicine, University of Queensland, Australia, researching the genetic and environmental influences of obesity in Australian twin families. I completed post-doctoral positions at the University of Hong Kong working on founder mutations in Hirschsprung’s Disease in Han Chinese, and at the Singapore Eye Research Institute where I investigated the genetic variants linked to age-related macular degeneration and the role of endophenotypes in glaucoma in the three major ethnic groups in Singapore – Malay, Indian and Chinese. As a researcher at the Massachusetts General Hospital, I used exome chip and targeted sequencing data from related and unrelated individuals to identify novel genetic variants responsible for Type 2 Diabetes through their association with related quantitative phenotypes.

At the New York Genome Center, I was a senior bioinformatics scientist in genetic epidemiology and statistical genetics responsible for ensuring the integrity as well as the analysis and interpretation of pedigree-based data. I also provided lead support to several large ongoing projects which used sequencing and chip data to more precisely localize and characterize genes underlying previously identified variants in diverse ethnicities and races including African Americans, Puerto Ricans and Europeans. Before joining JAX, I was a population informaticist and team lead at Sema4, building out the molecular ancestry calculation pipeline for the expanded carrier test using new genotype technologies.

Belinda Cornes on Google Scholar

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.

Daniel Danis on ORCID

In June of 2019, I began my internship at Jackson Labs. After a month of training and education, I began work on a network that detects corners within a mouse arena. This network improves on the Kumar Lab's previous metric of corner detection, and provides information for analysis on behaviors linked to corner proximity, such as huddling or sleeping. This was wrapped up in late October. Currently, I am working on another network that identifies fecal boli within a mouse arena.

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I perform statistical analyses to characterize phenotypic patterns in aging, and to search for predictors of longevity and mortality. Additionally, I have an active role in data management and quality control, and I provide analytic support to external research groups.

1. Worked as QA Lead at Schlumberger to manage, co-ordinate and test real time Data Acquisition System for Oil Exploration involving multiple applications controlling Equipment Operations.
2. Performing Automation testing on software Products and updating Test Scripts as functionalities evolve.
3. Worked At HP as QA Lead to manage and plan testing for Network Controllers, Storage Controllers and HP E-Commerce website.
4. Worked with Project Managers, Developers , System Architect to define and customize QA process for Agile as per project needs.

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Previously, I have worked on design and development of scientific software solutions using complex algorithms, statistical analysis, machine learning, and NLP. As a recent addition to the Computational Sciences group here at JAX, I am thrilled to utilize my skill set and widen my understanding as I uncover more about the world of genetics, genomics, and bioinformatics.

My early research was in the area of chemical physics performing computational modeling of molecular collisions. My work in biological systems began with post-doctoral work in computational biophysics modeling DNA denaturation. My first work at the Jackson Laboratory, with Carol Bult in collaboration with the University of Maine, was in the development of GenoSIS, a visualization and analysis tool for genome data interpretation using geographic information science concepts and technology. As part of Mouse Genome Informatics I work in Judy Blake's Gene Ontology (GO) group at JAX in the development of computational methods and tools for statistical analysis and visualization. My current research involves the development of OncoCL, an ontology of cancer cell types, to provide a semantic framework for the integration of cancer-associated molecular data and the properties imparted by the corresponding disrupted cellular pathways with conventional pathology data associated with cancer progression.

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As part of the Gene Ontology (GO) project in the Mouse Genome Informatics Database System at The Jackson Laboratory, I participate in functional annotation of gene products ) using the GO, as well as contribute to the overall design and content of the Gene Ontology vocabulary. I am also a curator for the Protein Ontology Project, based at the Protein Information Resource at Georgetown University in D.C., focusing functional annotation of specific protein isoforms and other proteoforms (PTMs).

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.

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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.

Since joining JAX in June of 2017, I have been working with the Computational Sciences group on software resources and projects for the Chesler Lab, such as the Mouse Phenome Database website. I have enjoyed picking up and learning new programming languages and frameworks, as well as becoming familiar with various projects and the science behind them. I look forward to picking up more skills, knowledge, and projects in the future.

A synthetic organic chemist by training, I spent the first few years of my career at the bench.  I've been working as a software engineer for more than 15 years, with the last 10 years focused on delivering applications that help enable scientists.  Prior to joining JAX, I spent time at the Broad Institute and was exited to be part of a team that worked to deliver the BioAssayResearchDatabase.  Since joining the Computational Sciences group here at JAX, I've worked closely with Clinical and was excited to lead the software team that built JAX-Clinical Knowledgebase (CKB) (https://ckb.jax.org/).

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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.

Over the last 15 years Fasman has built many connections with JAX, serving on the Laboratory’s scientific advisory board and advising the Genetic Resource Sciences group. He currently serves on the JAX board of scientific counselors.

Fasman joins the Laboratory from the Adelson Medical Research Foundation in Needham, Mass., where he has been vice president and chief scientific officer since 2008. From 1998 to 2008, he worked with the international pharmaceutical giant AstraZeneca, rising to director of drug development strategy and performance.

From 1992 to 1998, Fasman worked on the human genome project at the Johns Hopkins University School of Medicine and later at the Whitehead Institute/MIT Center for Genome Research. While earning his Ph.D. in biomedical engineering from Hopkins, Fasman co-founded a laboratory software and systems design consulting firm, BME Systems, Inc.

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.

Bill Flynn on Google Scholar

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.

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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.

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I am a software engineer, working to make a difference where biology and computer science meet. I specialize in full-stack web applications, scripting, and bioinformatics related technologies such as bioPerl, bioPython, and NGS frameworks. I have additional interest in machine learning and data mining algorithms. I aim to bridge the gap between the vast amounts of data and the need for results or visualizations.

My research interests lie at the intersection of mathematics and molecular biology. I recently graduated from The University of Connecticut with a degree in Applied Mathematics. During my time there, I worked at UConn Health's Center for Quantitative Medicine, employing a dynamical systems approach to identifying reversion targets for a subtype of Triple Negative Breast Cancer. I also spent a summer working at Metrum Research Group, where I developed a physiologically based pharmacokinetic model to predict maternal and fetal exposure of drugs metabolized by Cytochrome P450 enzymes in pregnant women. As part of the Churchill lab, I am using bayesian statistics to develop a new approach for mediation analysis of complex traits in mice.

I’m interested in using computational and statistical methods to answer questions that we have about the world. I’m also interested in communicating these answers in a clear and comprehensible manner. These interests fall under the broad heading of Data Science, which is an amalgamation of statistics, machine learning and data visualization. My work focuses on ways to make the analysis methods and results, reliable, reproducible and transparent.

I’m also interested in the broader effect of computers on the human ecosystem. Computer technology has infiltrated every aspect of our lives. Algorithms decide how long we should stay in prison, what medical treatment we should receive, what advertisements we see and our eligibility for loans and jobs. Are these algorithms truly unbiased or do they mask the subjectivity of the input data? What effect does this data have on our lives? How can we evaluate the balance between benefits and harm caused by third parties who use our personal data?

Studying genome structure and function through the application of high-throughput DNA sequencing and mapping methodologies such as Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET), as part of the ENCODE three-dimensional (3-D) mapping of the human and mouse genomes project.

My interests include computational methods to estimate the genetic, epigenetic and transcriptional profiles of cells involved in disease like cancer and diabetes. Currently we are able to profile the transcriptional and epigenetic profiles of the cells involved in these diseases at single cell resolution.  These technologies generate huge amount of data and there is an urgent need to develop novel methods to analyze these data to generate biological insights.

I also coordinate the efforts to characterize the genome of patient-derived xenograft (PDX) models developed at JAX. PDX models are essentially human tumors engrafted in immunodeficient mice and are excellent models to study therapeutic response to cancer. We assess the genomic mutations and gene-expression profiles of these tumors using next-generation DNA sequencing technology.

My job is to use bioinformatics tools to understand gene and protein expression in the aging heart.

My masters degree was in chemical engineering. My computing skills were started with Computational Fluid Dynamics for a commercial company. These codes often have tens of millions of lines of code and are challenged by computational resources such that numerical methods of various kinds are used. I then moved into a job at Diamond Light Source as a scientific software engineer and a data acquisition expert. I worked with scientists to collect data using robotics and analyse data using various algorithms. After this I worked on machine learning for an oil company. The task was to validate the computational science and deploy it to the cloud software as a service or SaaS.

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I received my Ph.D. in Biochemistry and Molecular Biology from Huazhong Agricultural University where I studied the genetic and biochemical bases of rice metabolome variation by an approach combined genomics, genetics and metabolomics. Previously, I received my B.S. in Biological Science at Huazhong Agricultural University. After getting my Ph.D., I joined Dr. Wei Chia-Lin’s lab at DOE Joint Genome Institute, working on the epigenetic control of drought response in sorghum.

Currently I am a postdoctoral associate in our Genome Technologies group. My researches focus on developing ultra-long read sequencing methods with the third generation single-molecule sequencing technologies and applying them with other cutting edge genome technologies to identify the genomic structural variations in cancer genome.

I've worked with groups that have developed scientific database-driven web resources and software applications to support biomedical research, clinical trials, and biometry.

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 graduated from The University of Oklahoma with a PhD in neuroscience, and shortly thereafter joined the Kaczorowski lab as a postdoctoral associate. I am intent on leveraging genetically diverse mouse populations to uncover genetic and epigenetic mechanisms that govern organismal aging, and determine to what extent these mechanisms are acting in the brain to confer risk and resilience to Alzheimer's disease. Most of the known risk variants associated with Alzheimer's disease occur in non-coding regions in the genome and are proposed to influence gene targets that are (1) different than those classically been associated with, and (2) act in a cell type specific manner. I use single-cell technologies to characterize the effects of risk variants on their target genes and explore their influence on molecular networks associated with aging and Alzheimer's disease. My goal is test whether identified genetic variants alter the epigenetic landscape, and whether these variants can be targeted to delay or prevent age-related cognitive decline and dementia.

Niran Hadad on ORCID

As the first Research Assistant taken on by Dr. Dumont my responsibilities have included the establishment and implementation of resilient systems for managing the laboratory's inventory, mouse colony, and experimental data, in addition to the development and optimization of experimental protocols. In the coming year I plan to advance my development as a Research Assistant under the guidance of Dr. Dumont, acquire supplemental competencies in molecular and cell techniques, continue to learn Python and R programming, and assist in the authorship of published articles intended to elucidate the pseudoautosomal region of genetically diverse mouse models. My personal goal is to become a Research Scientist at JAX and an expert in CRISPR-mediated genome editing.

Education

University of Maine Orono
B.S., Bioengineering
2011-2014

Experience

The Dumont Laboratory
Research Assistant
2017 - present

The Jackson Laboratory
Animal Care Technician
2016 - 2017

University of Maine Chemical and Biological Engineering Laboratory
Undergraduate Research Assistant
Adviser: Dr. Michael Mason
2012 - 2014

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 joined the Simon John lab in the spring of 2013, excited by the opportunity of working with Dr. John and using a multidisciplinary approach in my research. Using DBA/2J mice as a model of glaucoma, my current research focuses on axon degeneration, the role of innate immunity in the central nervous system, and the role of diet in neurodegeneration. This includes studying the role of JNKs in glaucomatous injury and working to identify early signaling events that may initiate injury. For another project, I am collaborating with faculty from Dalhousie University, working on defining human genes responsible for exfoliation syndrome glaucoma. I also work closely with fellow postdoc Pete Williams and help direct two research assistants in the lab.  This opportunity to collaborate and manage while working through complex projects is broadening my conceptual thinking abilities both scientifically and managerially, and improving the array of skill sets necessary for running a lab.

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Prepare amplicon and shot gun libraries to sequence on different illumina platforms to investigate the functional role of microbiome on several diseases. Perform microbiological and molecular biology laboratory techniques such as qPCR, MALDI, making media and buffer solutions, bacterial culture and streaking, DNA extraction and purification, PCR, gel electrophoresis and imaging.

I focus on 1) developing computational methods to systematically and accurately characterize the genomics and the transcriptomics of cancer using high throughput sequencing technology, and developing integrative approaches that help understand the etiology of cancer. Translate of genomics into therapeutics and diagnostics reinforce its potential for personalizing medicine.

2) computational analysis from genomic sequences to other post-genomic data, including both DNA and RNA sequences, protein profiling, and epigenetic profiling, in an ongoing effort to find hidden treasures. With the development of next-generation sequencing, our understanding has been advanced through the use of a variety of platforms: methy-seq, ChIP-seq, exome-seq and RNA-seq. The large amount of publicly available next-generation sequencing data, such as datasets from TCGA and ENCODE, has created enormous opportunities for researchers to conduct genomic analysis beyond the traditional sequencing analysis. Transforming genomic information into biomedical and biological knowledge requires creative and innovative computational methods for all aspects of genomics.

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Leinani is a research intern who graduated from New College of Florida in 2019. The focus of her undergraduate studies was Biology and Computer Science, and she wrote a senior thesis on the intersection of AI and creativity from an embodied, dynamical systems perspective. Leinani is excited to work on computational/Machine Learning approaches to biology at the Kumar Lab.

She is currently working on projects related to aging and nociception.

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.

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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.

My Ph.D. work was aimed at understanding the function of the TH2BS11ph histone modification and linker histone variant H1t in the context of processes that occur during the meiotic prophase I of rodents. We showed that TH2BS11ph histone mark is associated with two classes of chromatin domains in spermatocytes: DNA repair domains of the XY body and Transcription start sites. This is the first study documenting the role of a post-translational modification of a germ cell-specific histone variant in meiotic prophase I related events (Mahadevan et al, Epigenetics and Chromatin, 2019). We demonstrated the close association of the linker histone variant H1t at repeat-associated chromatin domains in vivo, apart from its localization at the rDNA element. These repeat elements were found to be repressed by DNA methylation and repressive histone modifications like H3K9me3 and H4K20me3 in vivo. We hypothesize that H1t might induce local chromatin relaxation to recruit protein factors necessary to Transposable element repression (Mahadevan et al., Epigenetics and Chromatin, 2020).

Continuing with my interest of study, my projects at the Baker lab would be aimed at decoding the epigenetic phenomena related to various developmental pathways like ES cell pluripotency/differentiation and meiotic recombination in mouse model systems.

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.

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Kevin C. Johnson on ORCID

Dr. Johnson has a PhD in nutritional ecology and completed a career development fellowship in computational genomics before joining the Weinstock Lab. His interest is in leveraging next-generation sequencing technologies to study how environmental factors, such as nutrition, influence host-microbiome interactions at a functional genomic level.

To get a different answer you need to ask the question differently.

Alzheimer’s disease currently affects 5 million Americans and despite decades of research we do not yet have a treatment that can even substantially slow the devastating symptoms let alone cure the inevitable cognitive decline.  Work in my laboratory to address this situation was launched not by asking what causes the disease, but by posing the counterintuitive question: “What makes some people resistant to the disease”?  In looking for the answer to this disarmingly simple question my laboratory is now leading a unique attack on Alzheimer’s and age-related dementia by focusing on identifying and validating the genetic factors that protect individuals from cognitive decline.

Integrated with broad collaborative efforts, my team of interns, research scientists, postdoctoral fellows and graduate students are working on a variety of projects that seek to understand these “genetic mechanisms and biomarkers of resilience” with the ultimate goal of turning these protective factors into novel therapies.

I hold a PhD in Molecular Biology from the University Texas at Dallas and postdoctoral research expertise in functional genomics and transcriptomics from The Scripps Research Institute (TSRI), Jupiter, Florida. A molecular biologist by training, I worked to see how small RNAs fold and big RNAs control gene expression and cellular functions, especially in human diseases and disorders. In addition to see how smart RNAs are, I am interested to apply the project and people management methodologies applied in the business/industrial world to the scientific research environment. I worked as a Project Manager at Rice University, Houston, Texas, to develop a Web platform for data collection for scientific research.

At JAX, I am part of a robust team at the Computational Sciences (CS) where I collaborates with the faculty, software developers, and computation scientists- who are dedicated to find novel solutions in the fight against diseases such as cancer and Alzheimer's using cutting edge computational and genomics technologies. As a Project Manager, I supports the CS team to plan, facilitate, execute, and deliver projects within agreed-upon timelines, resources, and objectives.

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).

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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.

My primary responsibility is to direct R&D operations of Computational Sciences (CS) at The Jackson Laboratory. CS works with our collaborators and partners at all campuses of The Jackson Laboratory and the expertise of CS spans the whole landscape of bioinformatics.

My research interests are in developing and applying statistical bioinformatics methods, machine learning algorithms and network biology approaches to understand disease biology and model biological processes such as cell division cycle and DNA replication.  We mine integrative heterogeneous omics data analysis and modeling as basis for our research.

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Krishna Karuturi on Google Scholar

  Krishna Karuturi on Researchgate 

As an indispensible molecule of living organisms, RNA plays key roles in various biological processes at both intra- and inter-cellular levels. My overall research interest is to study RNA from birth to death during organism neurological development, and its malfunctions in diseases for effective therapies in clinics. My research has been always implementing inter-discipline approaches, including bioinformatics (genomics), biochemistry, innovative biotechnologies, genetics and molecular biology.

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Shengong Ke on Google Scholar

I completed my undergraduate studies in Biotechnology at IIT Roorkee, India. This was followed by a project in the Axon Regeneration Lab at IISc Bangalore, under Dr. Ramanan's supervision. I am currently a Graduate Assistant in the Stitzel and Ucar lab at JAX-GM.

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.

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Studies the complex biological processes using machine learning and physics-based dynamical (linear/nonlinear) models using data-driven analysis. Develops computational methods, tools, pipelines, and interactive web apps to construct, model, simulate, and visualize the gene regulatory networks in various biological processes using literature-based evidence as well as RNA-Seq (bulk/single-cell), ATAC Seq, and CHiP Seq data. Employs deep learning on video-based assays and genomics data from behavioral studies to connect behavior and genetics.

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          Vivek Kohar on ORCID        

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.

I work as a Senior Scientific Curator on the Mouse Models of Human Cancer Database (MMHCdb) (formerly known as the Mouse Tumor Biology (MTB) Database). The MMHCdb is part of the Mouse Genome Informatics (MGI) group here at The Jackson Laboratory.

Education

• Johns Hopkins University - BA, Biology (1990)
• University of Maine - MS, Microbiology (1994)

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.

Swarup Kumar on ORCIDID

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I have a strong background in computational biology/bioinformatics with specific expertise in single-cell sequencing technology. I have analyzed transcriptomes as well as genomes of thousands of single cells from a range of cell types, including cancer, induced pluripotent stem cells, neuronal cells, blood cells, ovarian cells for various studies identifying gene signatures and/or mutations specific to particular cell types. I have also been involved in the development of a computational approach to support the design and analysis of single cell RNA-seq experiments. Separately, I have also analyzed bulk transcriptomes/genomes either for comparison with single cell studies or for independent research projects.

The Kumar lab studies neural circuits in the brain whose misregulation leads to behavioral abnormalities including addiction, attention deficit and hyperactivity disorder,and depression. Using mouse molecular genetics as a foundation, and a combination of biochemistry, physiology, and imaging techniques, we dissect these complex behaviors in mammals.

We use two functional genomics approaches in mice—forward genetic ethylnitrosourea (ENU) mutagenesis screens and quantitative trait loci (QTL) analysis—to identify genes and pathways that regulate these behaviors. Powerful and unbiased, forward genetic approaches make no a priori assumptions and only require a clear, well-defined assay for gene discovery. We have used a high-throughput screening pipeline to discover mutants for cocaine response and open-field behavior. Using physical mapping followed by next-generation sequencing, we have identified novel genes and alleles that regulate cocaine response and anxiety-related behaviors.

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I work on teams to develop and improve internal software-based tools used by researchers within the lab. I am also involved in the JAX Diversity Strain Informatics project which will bring everything regarding CC/DO mice into one space where the scientific community can access data, tools, and educational resources. Most of my work is front-end, utilizing tools like D3, and UI/UX, but I'm always looking to improve and expand my knowledge to work towards becoming a full-stack developer.

My primary languages and tools are: Javascript, Python, HTML/CSS, D3, Sketch

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.

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Genetic diversity is critical for the survival of any species. It is also central to the understanding of why one population, under similar environmental challenges, survives over another. I am primarily focused on determining the underlying evolutionary mechanisms shaping the observed genetic diversity found in the wild house mice. The wild house mice are invasive species with a long history of adaptation to new and the most extreme habitat. Unraveling the mystery of such adaptation is important for biomedical discoveries.

  View Raman Lawal on Research Gate 

View Raman Lawal on Google Scholar

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.

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. 

Current projects:

1. Probabilistic metabolite and network models for metabolomics. This includes the Mummichog Project, and addresses challenges in the assembly of information in metabolomics.
2. 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.
3. 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.

Shuzhao Li on ORCID

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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.

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Sheng Li Personal Lab Site

I pursued my PhD study in Computer Science (specifically, statistical machine learning) from 1997-2000 at National University of Singapore. During this period of time, I not only worked in machine learning theory, but also designed and implemented a new family of online learning algorithms to recognize the handwriting digits in MNIST dataset, and obtained the state-of-art performance that could be achieved by online learning algorithms at that time.

From 2001-2003, I worked on microarray gene expression data. The methods I explored included supervised classifications via relevance vector machines and unsupervised class discovery. Since 2004, I have been working in statistical genetics -- dissecting genetic bases for complex human diseases by analysis of genome-wide linkage and association data, exome-wide sequencing data, RNA-sequencing data. Since the genetic variants far out-numbered the sample size, various statistical methods, including data imputation, regularized regression, generalized linear mixed models, pathway analysis, fine mapping and evidence combination from heterogeneous data sources, have been explored and developed.

I have published more than 40 peer-reviewed papers in various journals, including first or co-first author publications in Nature Genetics, American Journal of Human Genetics, Machine Learning, Journal of Computer and System Sciences, and IEEE Transactions on Information Theory.

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Yi Li on Google Scholar

During my Ph.D., I developed extensive experience in analyzing the next-generation sequencing data and developing computational methods. My research fields include but are not limited to RNA secondary structure, RNA binding protein regulation, mRNA translation, non-coding RNA, developmental biology, virus infections, and cancer. I have extensive experience in processing and analyzing the next-generation sequencing data which includes iCLIP/eCLIP-seq, RRBS, ic/SHAPE, PARS, ribosome footprinting, RNA-seq, and so on. I developed PRAS for CLIP-seq data to detecting the functional targets of RNA binding proteins.

Jianan Lin on ORCID

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 more than 15 journal and conference papers in the areas of machine learning with uncertainty, E-Health system and DNA computing.

At JAX, I engaged in multi-omic data analysis and algorithm development, including epigenetic pattern mining and algorithm development of cancer, and DNA methylation pattern mining research.

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I recently 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. Most recently, 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. These studies spanned a number of insect orders and also included ant-associated fungi. I am excited to apply my knowledge and skill set to the diversity of questions being asked at JAX.

Zhiyuan received his Ph.D. degree at College of Life Science, Wuhan University. In Ph.D. training, his work was focused on regulation of Notch signaling and long-noncoding RNA. Currently, Zhiyuan is a Postdoctoral associate in Dr. Shengdong Ke’s Lab at The Jackson Laboratory. He envisions his research to investigate the roles of m6A modification in neurogenesis. How dysregulation of epitranscriptome contributes to brain disorders? What’s the mechanism of m6A signaling modulating the tempo of cortical neurogenesis? How does the cellular machinery target m6A to the correct transcripts? Answering these questions will advance the understanding of brain development and cell fate decision. Meanwhile, Zhiyuan is also interested in deep learning. He is devoted to develop a novel method to predict m6A sites and discover its biological significance.

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I utilize knock-out mouse models and spontaneous mutants to study developmental disorders. I employ novel genetic techniques, assays and bioinformatics to understand the underlying genetics of observed phenotypes.

My academic training is in physics and mathematics. I completed my PhD in math at Dartmouth College in 2009. After graduate school, I expanded into systems biology with a postdoctoral fellowship in the genetics department at Dartmouth studying an autoimmune disease called systemic sclerosis. I subsequently took a second postdoc in systems neuroscience studying the coordination of neuronal firing underlying cognition and cognitive deficits in epilepsy models. While seemingly disparate, these two fields share a lot in common from the mathematical point of view, particularly the use of machine learning and network theory to cope with biological complexity. For the last several years, my interests have expanded into model systems genetics and the central problems of predicting causal genes for complex traits and defining robust phenotypes for genetic mapping using heterogeneous data. My current work is expanding these approaches for the Cube project and through several ongoing collaborations within and beyond JAX.

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.

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I have always been interested in biology but found my skill set tended more towards math and data science. After graduating with a B.A in Mathematics in 2016 I joined a computational biology lab as a research assistant, there I developed experience working with deep neural networks for image feature extraction, as well as the modeling of single unit neuronal recordings. In 2020 I was excited to accept a full time position in the Computational Sciences Core leveraging my previous experience.

Seamus Mawe on ORCID

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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.

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I am a Computational Scientist at the Jackson Laboratory @ Farmington, USA. I have several years of professional experience in Computational Biology, Bioinformatics, Statistics and Biostatistics. I have mainly authored publications on bulk- and single- cell transcriptomics (RNA-seq, CAGE and Microarrays), 3d chromatin organization, signal processing and image analysis. I have worked in cancer stratification and cell cycle, mast cell trascriptome, type 2 diabetes and, more recently, on heart disease, cardiac (trans)differentiation and characterization of myocyte and non-myocyte populations. Currently, I am mainly working on the single-cell transcriptomics and epigenomics of type 2 diabetes.

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 Efthymios Motakis on ORCID

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. 

My 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).

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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.

I have been developing computational pipelines to analyze human blood cells transcriptomics data at the single cell resolution in different contexts including SLE, aging, and influenza vaccination. My main project focusses on understanding how the single cell RNA-seq data can help resolving the SLE transcriptional signatures described decades ago. This analysis allowed a more accurate classification of lupus patients based on specific cell types. This first direct comparison of childhood and adult SLE confirmed the presence of similar transcriptional patterns across age groups and enabled patient classification according to disease activity.

Currently, my research includes understanding how the immune system varies: 1) during aging at the steady state (lifespan project): aimed at analyzing blood transcriptomes from infants, children, young and older adults; and 2) after in vivo stimulation (e.g. influenza vaccination): aimed at analyzing the responsiveness to influenza vaccine in the groups indicated above.

Djamel Nehar-Belaid on ORCID

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.

Software engineer for  Mouse Models of Human Cancer Database (formerly MTB), MouseMine, PDXFinder and PDXNet.

My research focuses on developing and applying an innovative translational bioinformatics approach to interpret genome variation and understand the microbial mechanisms of pathogenicity. Briefly, my current work contains 4 main axes:

1. Studying the interactions between human genetic variation and microbiome composition
2. Designing novel informatics and sequence assembly tools essential in the analysis of the newest generation of sequencing data produced by the most cutting-edge genomic technologies
3. Developing large integrative sequence databases particularly focused on the Human Microbiome Project that now includes the enormously complex human microbiome, termed the human “second genome”  that will prove hugely important for scientists worldwide to analyze and interpret their genomics data
4. Developing pipeline for pathogen identification from next-generation sequencing data

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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.

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.

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As an allele and phenotypes curator, I assign official nomenclature to alleles and strain as well create genotypes with phenotypic descriptions for disease models.

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.

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My research interests involve two scientific themes: 1) meiotic recombination and genes that affect recombination positioning and activity; and 2) chromatin organization and its role in differentiation and gene expression. Our work in meiotic recombination resulted in the discovery of the major recombination positioning gene in mice and humans, Prdm9. PRDM9 is a histone methyltransferase that binds DNA via its zinc finger domain and creates open chromatin structure, thereby setting the stage for double-strand break (DSB) initiation and subsequent recombination events. We developed Affinity-seq, an in vitro approach for detecting all DNA-binding sites of PRDM9 genome-wide, which we are now applying to other DNA-binding proteins with the goal of developing improved prediction programs for motifs and binding sites. We also found that PRDM9 functions are dependent on its interaction with several other proteins, of which we have identified EWSR1, EHMT2, and CDYL, and that its methyltransferase activity at recombination hotspots is not only dependent on its DNA-binding specificity, but is also affected by local chromatin structure and is subject to trans regulation by distant genes.

Recently, I turned my attention to the role of chromatin structure on hepatocyte development and function. We found unusually diverse distributions of chromatin marks in hepatocytes of different strains, and determined that responses of these chromatin-mark distributions to corticosteroid treatment involved the same pathways across strains but not necessarily the same genes. We specifically addressed the influence of trans-acting factors on chromatin-activating marks in hepatocytes of two mouse lines, C57BL/6J and DBA/2J, using their F1 hybrids and derivative recombinant inbred lines as tools for detecting trans-acting regulators. We are now extending these studies to determine the genes regulating the positioning of open and closed chromatin in hepatocytes of Diversity Outbred (DO) mice, an enormously diverse mouse population derived from eight founder strains, and how they affect gene expression.

My work on recombination hotspots, PRDM9, initiation of meiotic recombination, and the role of chromatin structure and protein interactors has resulted in over thirty publications to date in journals including Science, PLOS Biology, PLOS Genetics, Genome Biology, Current Biology, Trends in Genetics, Nature Reviews Genetics, Epigenetics and Chromatin, Genetics and Genome Research.

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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.

For a complete list of my published work, please visit my NCBI bibliography.

Over the past ten years, I specialized myself in big data analysis (i.e. Machine-Learning) applied to Genomics, Semantics and Next Generation Sequencing (NGS) data linked to single-cell and cancer research. I obtained my PhD in Computational biology and Evolutionary Genomics at Ecole Centrale de Lyon (Lyon University), France. I worked both in academics (University of Hawaii Cancer Center, UCSD) and in a Startup company (R&D engineer) developing and using my research skills on fundamental and more concrete problematics. My current work involves working with single-cell ATAC-Seq and RNA-Seq datasets, multi-omic datasets linked to survival, new methods, pipelines and data visualisation interfaces development.

         Olivier Poirion on ORCID         

Oliver Poirion on Google Scholar

The focus of my research has been on deciphering the complex genetic architecture of human disease traits and modeling complex trait interactions in animal models . As part of the MODEL-AD consortium, my role is to support the efforts in building precise mouse models for late-onset Alzheimer disease by prioritizing human genetic risk variants for the creation of novel mouse models.

Over 15 years of multi-faceted, cross-functional experience in managing and analyzing data generated by diverse high-throughput genomic technologies. Hands on experience and leadership in genomics, big data management, analysis pipelines, interpretation and application development in academic, government and commercial settings.

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KUNDE GOVINDARAJAN ON GOOGLE SCHOLAR

  KUNDE GOVINDARAJAN ON RESEARCHGATE  

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.

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Analyzing microbial data with applying innovative computational genomic and bioinformatic methods to find out relationships between microbiome and different factors like diseases.

Developing deep learning algorithms including graph embedding and link prediction techniques in knowledge graphs constructed from networks of genes, drugs and diseases to predict new treatments for diseases.

Implementing machine learning algorithms to analyze immune profiling and clinical data of patients with chronic fatigue syndrome.

Implementing methods for translational bioinformatics with a focus on whole exome and whole genome sequencing.

With a background in Biology (Argentina), I've been working in Genomics and Bioinformatics since 2009, when I started my PhD Fellowship at the University of Salamanca (Spain). Since then, I've been fascinated about using information technologies for decoding the complexity of life. I've been working in the bioinformatics side of a wide variety of projects, such as investigating pathogenicity genes in pathogenic fungi and bacterias, exploring the use of miRNAs as biomarkers in cancer or exploring the functional role of transposable elements. All these projects have had required not only an effective data management and processing of the sequencing data, but also a comprehensive understanding of the state-of-the-art computational methods.

At JAX I work closely to Genome Technologies Team in developing and applying the most innovative bioinformatic methods for analyzing data generated with the cutting-edge genomic and sequencing technologies.

Gabriel Rech on Research Gate

        Gabriel Rech on ORCID        

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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.

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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. 

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The overall goals of our laboratory are to understand why the immune system causes autoimmune diseases and to devise methods to predict and treat them. We develop and use mouse strains that provide models for human diseases such as lupus, rheumatoid arthritis and epidermolysis bullosa. We use a combination of genetics, molecular biological and cellular immunological tools to dissect the molecular and cellular processes that cause these diseases. Finally, we study the mechanisms that affect the persistence of antibodies and antibody-based therapeutics. The information gained from all of these approaches is then used to devise possible therapeutic approaches that can be translated to human treatments.

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.

Jill Rubinstein on ORDID

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As a Biostatistician in the Kumar Lab, I work with the team on research problems related to addiction and behavioral disorders.

My past research focused on combining experiments in natural and constructed populations with high-throughput genetic, genomic, and transcriptomic analyses to better understand the current and historical forces that have shaped the evolution of traits, and using those patterns to infer what changes may occur in the future. In my new work at JAX I plan to leverage the knowledge and experience I gained through my academic research to help with the diverse projects that are being conducted to help improve our understanding of the relationships between our genes, environment, and health outcomes.

Brian Sanderson on ORCID

As the Associate Director of Genetic Resource Science Model Development and Bioinformatics, I oversee the group responsible for the selection and importation of new mouse strains into the Repository.  We curate strain information and develop tools that enable the public to access the information in our extensive strain database. 

I also work on creating and characterizing mouse models that accurately model human disease and can be used in understanding neurodegenerative disease and in the development of new therapies. Ongoing projects are listed below.

The Jackson Laboratory Alzheimer's Disease Center

Alzheimer's disease is the most common cause of dementia. It is characterized by brain pathology including amyloid plaques composed of Abeta and intracellular tangles made up of hyperphosphorylated tau protein. Abeta is a cleavage product of the amyloid precursor protein (APP), and mutations in APP and its processing enzymes (including the presenilins, Bace) are thought to be one cause of familial AD.  The Jackson Laboratory offers a variety of genetic models for AD research, including strains expressing APP and presenilin (PSEN1) mutations, strains expressing mutant APP in either a constitutive or inducible manner, strains expressing mutant tau (Mapt), strains with Presenilin mutations, strains with Apoe mutations, and strains with Bace mutations. These models develop Alzheimer's-related characteristics as they age.

Parkinson's Disease Mouse Model Resource

Parkinson's disease is characterized by the loss of dopamine-producing cells in the substantia nigra. While the earliest and most obvious symptoms are movement disorders including tremor, rigidity, gait abnormalities and bradykinesia (slowness of movement), there are many other manifestations, including cognitive, emotional and sleep disorders. Accumulation of alpha-synuclein deposits in the brain lead to the formation of Lewy bodies, a diagnostic marker of PD.  The Michael J. Fox Foundation for Parkinson’s Research (MJFF) supports our program to develop, characterize, and distribute genetically engineered mice and information useful for their selection and use. 

Other model development projects

Chordoma

With funding from the Chordoma Foundation, we are developing and characterizing novels models of this rare form of skull and spine cancer.

Dravet Syndrome

With funding from the Dravet Syndrome Foundation, we have created a novel model of this form of early onset epilepsy. This model is currently being validated.

How can we change behavior? The answer to this question could substantially improve our health. I believe the key to knowing how we change behavior is understanding the exquisite similarities between us and non-human animals. Using methods from genetics, genomics, computational biology, and comparative ethology, I study human-relevant behaviors in non-human animals. These similarities manifest in the molecular networks of the brain. By revealing the conserved gene networks that drive behavior, we can develop new treatments for psychiatric disorders such as addiction and autism spectrum disorder.

At JAX, I work as a research scientist in the laboratory of Prof. Elissa Chesler. Our group’s study of addiction-relevant gene networks is facilitated by high genetic diversity mouse populations. The genetic diversity of these resources results in greater behavioral diversity than is typically observed in inbred strains. With these new data and new integrative genomics tools such as the GeneWeaver suite, we can connect mouse behavioral phenomes with the genetic and genomic underpinnings of behavior in the brain.

Michael Saul on ORCID

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Robert has a background in information systems, data science, software development, environmental consulting, and disaster response.

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.

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.

Variation in observable traits, such as disease susceptibility, is pervasive in the natural world. This variation is determined in part by genetic differences between individuals. Recent advances in sequencing and computation are providing us with an unprecedented view of patterns of genetic variation present within species. A major outstanding challenge is to identify and characterize the specific genetic variants that affect phenotypic variation and disease susceptibility, and the mechanisms through which they do so.

To this end, my research is centered on understanding patterns and types of genetic variation present within species. I am interested in how this variation affects the cellular and molecular phenotypes that mediate complex trait variation. I have experience in statistical modeling of genomic data in microbial systems, and am working to translate these interests to address biomedically relevant questions in the vastly more complex mouse.

Daniel Skelly on Google Scholar

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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.

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Recent advances in DNA sequencing technology led to the generation of vast amount of sequencing data and provided an unprecedented opportunity to understand the complexity of genome. However, this massive flood of sequencing data also created challenges in mining patterns of interest from this data. Moreover, diversity of datasets [RNA-SEQ, ChIP-Seq, Exome etc] generated from these technologies also demand more integrated evaluation across various sequencing platforms and data types. My dissertation was focused on deriving meaningful patterns from these datasets generated from both model and non-model species and also studying the variations in non-coding RNA (ncRNA) secondary structure and developing a novel method for ncRNA detection in the genome using patterns of chromatin-modifications. My post-dissertation work primarily concentrated on developing systems/algorithms to effectively analyze next generation sequencing data involving mammalian genome.

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. 

For a complete list of my published work, please visit my NCBI bibliography.

Ms. Sundberg has been involved with computer applications for 40 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 working with the Computational Sciences PDX (patient-derived xenograft) platform team of software engineers.

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I joined JAX as Associate Scientific Software Engineer in Computational Sciences department. I will be focusing on CKB project (Clinical Knowledgebase) for Clinical Curation and Analytics.

Previously in my career I worked in e-commerce and insurance industries focusing on consumer facing applications. I had an opportunity to be involved in architecture design, development and maintenance phases of the applications. Also worked closely with business partners to understand customer experience in the end product and identify ways to improve customer experience as well as improve technical side of the applications.

I am excited and looking forward to how can I apply my skills at JAX and contribute into its great mission.

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.

I am 25+ year technology and research management professional with experience in leading complex programs in the life sciences. My knowledge of strategic program leadership, research project management, information technologies, molecular biology and genetics combine with outstanding communication, analysis and collaboration skills to enable transformative team data science.

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.

Using basic and advanced statistical and data mining methods as well as high dimensional data visualization techniques, I perform downstream analysis including both multivariate parametric and non-parametric methods, test microbiome hypotheses and high resolution visualization for large scale data (network analysis). I also exploit and apply data regression and data dynamic modeling like as random forests, linear mixed models, Markov multi-state modeling as well as probabilistic modeling for microbial metagenomic data.

I am a genomics data scientist with extensive expertise in the analysis of next-generation sequencing data sets, including WGS, Exome-seq, RNA-seq, ATAC-seq and Pacbio long read SMRT-seq. I joined the Jackson Laboratory in 2016 to help developing a groundbreaking research program aimed at the identification of cancer isoforms using long-read sequencing. Our premise is that splicing aberration in cancer generates RNA-based tumor antigens that can be exploited for immunotherapy.

Using Machine Learning, Genomics and Proteomics, we are characterizing the landscape of RNA alterations in breast cancer, melanomas, lung and ovarian cancers. I am also a Software Carpentry instructor teaching R, Bioconductor and Genomics to scientists at JAX.

The full description of my research activities is available on https://diogoveiga.github.io/

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.

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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.

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 Duygu Ucar on Google Scholar 

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In 2013, I graduated from the College of the Holy Cross where I had work with Dr. Kenneth Mills on studying protein splicing. After college, I worked as a research assistant in Dr. Rameen Beroukhim's lab at the Dana-Farber Cancer Institute. In the Beroukhim lab, I worked with graduate student, William Gibson, and postdoctoral fellow, Brenton Paolella on validating the splicing factor SF3B1 as a CYCLOPS gene. In 2015, I began the University of Connecticut MD/PhD program, and joined the Anczukow lab in 2017.

Dr. Varn received his Ph.D in Molecular and Systems Biology from Dartmouth College in 2018. While at Dartmouth, his thesis work focused on developing and applying computational methods to investigate the factors influencing the immune response in different cancer types. As a postdoctoral associate at JAX, his research projects integrate his background in computational biology, cancer biology, and immunology to study how the immune response shapes brain tumor evolution.

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We are a computational cancer biology lab with a research focus on the analysis of cancer genomics data to improve our understanding of cancer biology. We have a specialized research interest in understanding disease progression of brain tumors, and to study the role of extrachromosomal DNA amplifications in cancer. Our group combines wet lab approaches for functional modeling with large datasets and computational methods.

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Anita, a joint postdoctoral fellow with the Oh and Weinstock groups, is most recently from Heidelberg, Germany, where she studied with Dr. Peer Bork and Dr. Magnus von Knebel Doeberitz on numerous microbiome projects related to colorectal cancer and methods development. She is interested in understanding the fundamental roles of the microbiome, particularly the skin and the gut microbiome, in health and disease.

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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 primary development tools include: Python, Java, Groovy, AngularJS, JavaScript, D3, Bootstrap, PostgreSQL, MySQL and Oracle. I have worked on large scale enterprise LIMS for collecting, tracking, processing and reporting patient samples; command-line analysis tools for scientific data; web applications for the presentation of analyzed results; and API’s to allow other programs access to data and tools using RESTful web services and JMS.

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.

I am a Project Management Professional (PMP)-certified project manager, certified Scrum master, and scientist with experience in molecular biology, genetics, in-vitro diagnostics, and computational biology. My diverse professional experience also include management consulting, product management, and intellectual property. My scientific training was in Molecular and Human Genetics (PhD) from Baylor College of Medicine, Houston, Texas.

At JAX, I support the Computational Sciences (CS) team in organizing, managing, and shepherding projects to successful completion.

In the last decade, leaps in DNA sequencing technologies have transformed our ability to collect and analyze genomic information. This revolution has opened up entirely new areas of study from human to microbial and infectious disease research. Currently, understanding the microbiome (the vast collection of microbes in our body with which we coexist), its interactions with its host (us) and its contributions to health and disease is a vital new research area that he is focusing on. The Weinstock Laboratory leverages advanced technologies to investigate infectious diseases and mammalian microbiomes.

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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.

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Emerging single-cell transcriptomic and highly-multiplexed imaging methodologies are advancing our basic understanding of tumor heterogeneity and its impact on patient outcome and therapeutic response. My previous work involved predicting disease progression and drug response using bulk (principally, expression) data. My interests lie in leveraging these new single-cell modalities to improve our ability to extract insight from the wealth of existing (and clinically annotated) bulk data. For example, we have recently completed a deconvolution DREAM challenge comparing methods that infer immune sub-populations from bulk expression data. Several participant methods used single-cell RNA-seq to identify markers that could subsequently be used to detect the corresponding population in bulk data.

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Brian White on ORCID

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.

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Lidya Wooldridge on ORCID

I strive to create models with explanatory power and predictive value to enhance our understanding of biological systems. My goal is to apply this knowledge to improve biomedical outcomes.

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.

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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. Currently I am developing pipelines for identification of lncRNAs and splicing events in RNA-seq datasets and integrating various omics data.

Marina Yurieva on ORCID

My research mainly focuses on genetic dissection of complex diseases using/developing the state-of-the-art multi- and inter-disciplinary approaches of genomic technologies, and statistical and bioinformatical methods. The approaches involve genome-wide association analyses, genome-wide transcriptome analyses, proteome-wide protein expression profiling, epigenetic profiling by various statistical methods, including e.g. data imputation, regularized regression, pathway analysis, fine mapping. I am also extending our research of complex diseases to systematically and accurately characterize complex disease. I am interested in developing novel statistical methods and bioinformatics tools for integrating large, complex multi-omics datasets (e.g. DNA-Seq, RNA-Seq) in research. e.g., I performed integration analysis for cancer at micro-RNA, mRNA, DNA methylation levels. In addition, I am interested in how to transfers knowledge from my basic research to clinical research.

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Solving computational biology and bioinformatics problems with machine learning and statistical methods.

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 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.

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I am currently working on several research projects in the Dr. Charles Lee lab. One of the projects is to identify and characterize genetic aberrations associated with congenital diaphragmatic hernia (CDH). CDH is a defect in the diaphragm that allows the stomach and intestines to move into the chest cavity and cause underdevelopment of the lungs. The incidence of CDH is approximately 1 in 2,500 newborns. More than 80 percent of the patients are not associated with any known genetic syndrome, and the underlying mechanisms for their conditions remain unknown. We have designed a customized high-resolution genome-wide microarray to detect genetic aberrations from congenital diaphragmatic hernia (CDH) patient samples. We are currently running this array platform on the CDH patients and control samples, and analyzing the data. Eventually, we envision a CDH-specific diagnostic array that can be used for studying different CDH cohorts. Another project is to design and optimize genomic structural variation analysis platforms. These platforms will be implemented to detect and analyze chromosomal imbalances for whole genome array comparative genomic hybridization (aCGH) and single nucleotide polymorphisms (SNP) microarray-based technologies.

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