Data Science: People

I joined King Abdullah University of Science and Technology to start my Ph.D. study in 2010. I was a member of the Structural and Functional Bioinformatics group, led by Prof. Xin Gao. My research focus was on determining the 3D structures of proteins using Nuclear Magnetic Resonance (NMR) data. My research during Ph.D. resulted in five journal papers published in Bioinformatics, Plos One, Journal of Biomolecular NMR, Scientific Reports, and IEEE/ACM Transactions on Computational Biology and Bioinformatics.

In 2017, I started my first postdoctoral training. I joined the group of Prof. Jianyang Zeng in Institute for Interdisciplinary Information Sciences, Tsinghua University. Prof. Zeng gave me all the trust and freedom to do proper research. He suggested me a very promising research topic about 3D genome modeling using Hi-C and FISH data. My work in this project resulted in a Nature Communications paper that was published earlier this year (2019). Also, I worked on another related project about 3D genome modeling using single-cell Hi-C data. This second project gave good results, and its manuscript is still under preparation.

I am a first year student in the Biomedical Engineering Ph.D program of the GSBSE at the University of Maine. My Master’s thesis focused on diagnosing the early sign of mild cognitive impairment, through piezoresistive sensors that capture sleep signals, to halt or treat the progression of Alzheimer’s Disease before it escalates and becomes severe. Before joining the GSBSE program, I was developing gradient tracking software models of G-Protein Coupled Receptor in yeast at Dr. Joshua Kelley's lab at the University of Maine. I am currently working on multi-view tracking system, which is designed to track mice using a convolution neural network. The system helps researchers in mentoring a drug response, autism and schizophrenia repetitive behavior, and Alzheimer’s disease in mice.

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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 publically 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.

During my Ph.D., I developed extensive experience in NGS data analysis. I focused on implementing computational and statistical methods to handle NGS data, in particular, ChIP-seq data. I developed methods for peak calling and differential peak calling for ChIP-seq data coming from cancer samples. Also, I developed an integrated database for ChIP-seq derived human enhancers. In addition, I have contributed to several other projects including FAMTOM5 short non-coding RNA bioinformatics analysis where I worked on analyzing CAGE, RNA-seq and ChIP-seq data.

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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. Barthel received his M.D. degree from the Vrije Universiteit Amsterdam prior to joining the MD Anderson Cancer Center in Houston to pursue a postdoctoral position in glioma genomics. He research is focused on transcriptional and epigenetic profiling of gliomas and in understanding the genomic basis of telomere maintenance in cancer.

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. 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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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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With a history in Applied Mathematics, I get excited about solving complex problems by utilizing computational resources. My relationship with JAX started when I worked as a Software Quality Assurance Intern in 2014. I then went on to intern with the Computational Sciences group in 2015 (writing actual code!), and was thrilled to accept a full time position in 2016. While python is my native language of choice, I pride myself on my ability to dive in and commit to learning new languages and frameworks. I've also spent some serious time with JavaScript, C++, R, SAS, and MYSQL, as well as markup languages like HTML and LaTeX (I transcribed most of my college notes in LaTeX). My lifelong fascination with computers means that before I even learned to code I had already started delving into the beautiful world of Unix, setting up my computer to run Arch Linux. I now have roughly four years of experience with Unix and Bash, experience I frequently leverage to complete housekeeping and testing tasks quickly and efficiently. If we run into each other outside of work you'll probably find me playing music, bouldering, running, hiking, or exploring.

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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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.

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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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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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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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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Computer Scientist, Biostatistician, Computational Biologist, Data Scientist: Since Dr. Choi started his postdoctoral career at the Jackson Laboratory in 2011, he has tackled computational challenges in the analyses of next generation sequencing (NGS) data from the Collaborative Cross (CC) or Diversity Outbred (DO) mice. Conventional analysis strategies do not exploit millions of annotated polymorphisms in these multiparental population, and therefore, were prone to biases, often resulting in misleading conclusions. Dr. Choi has developed a new pipeline consisting of novel statistical models and custom software packages to overcome the unique challenge especially in Whole Transcriptome Analysis for the CC and DO. His software tools are unique because they quantify expression at the maternal or paternal allele level and they improve accuracy by referring to information hidden in the population of samples. He is working on generalizing the current models to different type of NGS data in order to study associations in allele-specific methylation, allele-specific transcription factor binding, allele-specific expression, and allele-specific translation.

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Advances in sequence and image data collection have radically transformed how biological systems can be understood. My lab uses computational and mathematical approaches to identify essential phenomena within large biological datasets. We are currently focused in three areas: 1) Evolutionary and ecological changes in cancer in response to drug treatment, 2) Prediction of outcomes from cancer image data, and 3) Fundamental mechanisms of gene regulation. The lab is a leader in patient-derived xenografts, a cancer model system in which patient tumors can be tested and assayed in response to alternate treatments. Our projects involve collaborations with cancer biologists, medical oncologists, geneticists, and computational biologists from JAX and many other institutes around the world.

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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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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.

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.

I integrate operant and classical conditioning paradigms with modern systems genetics techniques to identify the genetic and genomic mechanisms underlying fundamental behavioral processes driving drug addiction and related behavioral disorders.

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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.

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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.

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 primary research interest is in developing new methods to analyze next-generation DNA sequencing data to aid disease understanding, diagnosis, prognosis, and treatment. My genomics research began in 2010 as a summer intern at NHGRI, where I created a quality control method to identify lane biases in Illumina HiSeq datasets. Since then, I have worked on several projects using targeted, exome, or whole genome sequencing from cohorts or families in search of disease causative variants. As a graduate student at Stanford, I developed methods to understand the clinical impact of genome sequencing, and created metrics and methods to evaluate and improve genetic variant discovery accuracy. At The Jackson Laboratory, I’ve been focusing on developing bioinformatics pipelines to identify epigenomic patterns from tumor, normal, and cell-free DNA, and, separately, for analyzing long-read RNA sequencing. 

Beyond my research goals, I am passionate about educating others about genomics and its impact on medicine and on our lives. Towards this end, I created and co-taught a Stanford University course on Personalized Genomic Medicine, where students learned about cutting-edge genomic technologies and their application to health and medicine. I also enjoy coordinating and leading educational workshops including The Jackson Laboratory’s Workshop on Long Read Sequencing.

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.

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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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.

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.

Dr. Johnson has a Ph.D. in Experimental and Molecular Medicine from Dartmouth where he received comprehensive training in quantitative and basic biomedical sciences. He has strong interest in the dynamic epigenomic landscape of glioma cells during disease progression and recurrence.

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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.

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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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 have broad interests in developing and implementing computational methodologies that can improve our understanding in molecular mechanisms involved in cancer initiation and progression. During my postdoctoral research at Yale School of Medicine, USA, I studied how cancer-associated mutations in splicing factors result in aberrant RNA splicing and gene expression in hematopoietic disorders such as, myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Besides, I studied the role of musashi 2 and its RNA-binding targets in myeloid leukemia, employing integrated transcriptome-wide approaches to determine RNA-protein interactome and translatome profile.

I received my graduate training (PhD) at the University of Tuebingen, Germany. Here, I was trained in both lab-techniques and informatics of next-generation sequencing data. My graduate work focused on investigating the prevalence of alternative splicing and nonsense-mediated decay, and the underlying regulatory mechanisms in plants. During my early training in bioinformatics, I developed online tools based on GeSTer (Genome Scanner for Terminators) algorithm, that generates a comprehensive landscape of intrinsic transcription terminators (RNA secondary structures) across the whole prokaryotic genomes.

Currently at the Jackson Laboratory for Genomic Medicine, my research focuses on identifying splicing signatures associated with the dysregulated expression of splicing factors in breast and ovarian cancer. Here, my computational interests are in algorithm and pipeline development for processing the transcriptomics data derived from the cancer models (eg. cell lines, patient-derived xenograft mouse) and patients. Recently, I constructed a pipeline to determine three-dimensional chromatin structures from ChIA-PET (Chromatin Interaction Analysis with Paired-End Tag) sequencing data generated in the laboratory, and strive to further develop scalable methods on local clusters and google cloud machines.

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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.

Dr. Kim has a Ph.D. in Electrical Engineering from Columbia University where he did research on computational biology with emphasis on data mining and systematic analysis of genomic data. The current focus of his research is on understanding disease evolution of brain tumors.

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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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We study the complex biological processes using physics-based dynamical (linear/nonlinear) models and refine these mechanistic models using data-driven (bioinformatics) analysis. We develop computational methods, tools, and interactive web apps to construct, model, simulate, and visualize the gene regulatory networks in various biological processes and then validate and finetune the models/simulations using literature-based evidence as well as RNA-Seq (bulk/single-cell), ATAC Seq, and CHiP Seq data. These models uncover the operating principles of the complex systems and they are a valuable tool for in-silico perturbation experiments to predict the effects of different perturbations.

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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 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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Dr. Laubenbacher joined the University of Connecticut Health Center in May 2013 as Professor in the Department of Cell Biology and Co-Director of the Center for Quantitative Medicine. He is also Professor, Computational Biology, at The Jackson Laboratory for Genomic Medicine. Prior to this appointment, he served as a Professor at the Virginia Bioinformatics Institute and a Professor in the Department of Mathematics at Virginia Tech since 2001. He was also an Adjunct Professor in the Department of Cancer Biology at Wake Forest University in Winston-Salem (NC) and Affiliate Faculty in the Virginia Tech Wake Forest University School of Biomedical Engineering and Sciences. In addition, Dr. Laubenbacher was also Professor of Mathematics at New Mexico State University. He has served as Visiting Faculty at Los Alamos National Laboratories, was a member of the Mathematical Science Research Institute at Berkeley in 1998, and was a Visiting Associate Professor at Cornell University in 1990 and 1993. Current interests in Dr. Laubenbacher’s research group include the development of mathematical algorithms and their application to problems in systems biology, in particular the modeling and simulation of molecular networks. An application area of particular interest is cancer systems biology, especially the role of iron metabolism in breast cancer.

Genetic diversity is key for survival especially in the phase of continuous environmental changes. My current research leverages whole-genome sequences from wild-caught mouse species representing the repertoire of diversity, to discover patterns of variations that give rise to genetic diversity. By focusing on introgression, positive selection and balancing selection, my goal is to detect the footprint of these genomic signatures and their evolutionary roles in mammalian genome.

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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.

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.

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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Dr. Liu received his Ph.D. in chemical biology from University of Pittsburgh, Pennsylvania, in 2017. Prior to that, he received his B.S. in bioinformatics from Huazhong University of Science and Technology, China, in 2011. His current research interest is to apply genetic and genomics techniques to study mechanisms of brain tumor evolution.

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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.

Lauren has extensive experience analyzing large-scale time series hippocampal neurophysiology data. She also has experience leveraging advanced data analytical, statistical and visualization techniques to investigate microbial community composition using 16S, shotgun metagenomic and metatranscriptomic technologies.  She serves as the analytical liaison between collaborators and clients for Microbial Genomic Services at JAX-GM.  Her research focuses on understanding the role of the microbiome in health and disease.  Lauren is interested in the relationship between the microbiome and the brain.  

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We use systems biology approaches to uncover the underlying principles governing the operation of genetic networks. Specifically, we integrate computational modeling and data analysis to elucidate the relationship among robustness of network dynamics, stochasticity in gene expression and heterogeneity in cancer evolution. Our studies will contribute to a systems-level understanding of cancer and will eventually lead to the design of personalized therapies for cancer patients.

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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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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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 am a systems Immunologist working in the field of dendritic cells biology. My research focuses on understanding how epigenetic and/or transcriptional modifications occurring in immune-cell populations orchestrate and modulate the quality of the immune response to different type of antigens including pathogens, auto-antigens and vaccines. To try to answer these challenging questions, we are employing a multidisciplinary approach, combining computational methods and cell biology.

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.

Develops computational approaches using phenotype data to identify complex genetic networks and gene interactions relevant to AD.

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.

My laboratory specializes in human immunology with a focus on experimental immunotherapy. We have pioneered the development of dendritic cell-based vaccines for patients with cancer or HIV, and I am very interested in understanding how vaccines work and precisely defining the immune mechanisms that underpin successful vaccination. We apply cutting-edge genomic approaches that offer unprecedented insights into the inner workings of immune cells (single-cell genomics, hybrid sequencing and long RNA reads).This knowledge can catalyze the discovery and development of novel immunotherapies,including vaccines that target cancer.

Highlighted Publication

Wu TC, Xu K, Martinek J, Young RR, Banchereau R, George J, Turner J, Kim KI, Zurawski S, Wang X, Blankenship D, Brookes HM, Marches F, Obermoser G, Lavecchio E, Levin MK, Bae S, Chung CH, Smith JL, Cepika AM, Oxley KL, Snipes GJ, Banchereau J, Pascual V, O’Shaughnessy J, Palucka K. IL-1 receptor antagonist controls transcriptional signature of inflammation in patients with metastatic breast cancer. Cancer Res. 2018 Sept 15; 78(18): 5243-5258. doi: 10.1158/0008-5472.CAN-18-0413. PubMed PMID: 30012670.

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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.

Throughout my graduate studies in computational biology my main focus was the evolution and mechanism of bacterial sRNAs. As sequencing technology matured my colleagues and I developed an experimental-computational method for detecting sRNA-mRNA interactions called RIL-seq, this procedure allowed us to capture the entire post-transcriptional regulatory network at any given time. I then entered the fascinating world of plant microbiome working for Evogene Ltd where I was involved in several projects. My main focus in JAX is next generation sequence analysis with a great emphasis on applying and developing tools for microbiome research.

Education
The Hebrew University of Jerusalem, Israel
Ph.D. Computational Biology
Adv: Prof. Hanah Margalit
2008-2014

The Hebrew University of Jerusalem, Israel
B.Sc. Computational Biology
2003-2006

Experience
Evogene Ltd
Computational Biologist/Computational Platform Development Leader
2015-2018

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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.

I am mainly interested in how regulatory and epigenetic factors can influence the inheritance and expression of heritable traits, especially complex traits. My Ph.D. work primarily used human genetic methods, and I joined The Jackson Laboratory to expand my expertise with mice. My current research involves the role of the histone methyltransferase PRDM9 in allocation and repair of meiotic double-strand breaks, which are necessary for the essential process of genetic recombination. In mammals, PRDM9 is responsible for both initiating and localizing the sites of recombination within the genome. I have several ongoing projects in the lab, all of which involve the DNA-binding properties of PRDM9 in vivo, and the roles of PRDM9-dependent histone modifications in recombination.

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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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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.

Broadly, Dr. Reinholdt’s research interests are in the development and application of genetic approaches for understanding the etiology of genome variation and for understanding the role of genome variation in health and disease.

Dr. Reinholdt’s interests in the role of genetic variation in disease led her to focus her research efforts on an exceptional resource of laboratory mouse strains with proven Mendelian disorders with unknown genetic etiology, strains stewarded by the Mouse Mutant Resource at The Jackson Laboratory for over 50 years. Taking advantage of this resource, her laboratory was one of the first to apply exome sequencing at scale for the discovery of naturally occurring genetic variants (mutations) that cause Mendelian disease in mice. Her laboratory is now focusing on the significant proportion of mutations that escape detection by exome sequencing to further understand the nature of these mutations and to improve computational approaches mutation discovery. Using reverse genetic approaches, Dr. Reinholdt’s laboratory is also working closely with the human genetics community to create new strains of mice carrying human Mendelian disease mutations.

Heritable genetic variation is the result of genome instability during germ cell development, instability that arises through mutation, chromosome rearrangement or chromosome mis-segregation during mitosis or meiosis. Germ cells employ a variety of mechanisms to counteract these destabilizing events and these mechanisms can ultimately result in developmental arrest and cell death. However, these mechanisms are still poorly understood and when they fail, aneuploidy and infertility result. Dr. Reinholdt’s post-doctoral work on the discovery of genes required for normal germ line development and fertility led to the discovery that the germ line is exquisitely sensitive to mutations in components of the mitotic spindle that have the potential to lead to aneuploidy. This sensitivity may also extend to embryonic and adult stem cells. Dr. Reinholdt’s laboratory have gone on to show that the sensitivity of the germ line genome instability differs across inbred strains of mice, offering a unique opportunity to use systems genetics approaches to discover the underlying pathways governing cell division and survival across a variety of cell types.

We are interested in the development and application of both forward and reverse genetic approaches for understanding the etiology of genome variation and it’s role in health and disease.

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.

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.

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.

I hold Masters degree in Computer Science and ISTQB certified professional with diverse experience of 7 years in Information Technology with emphasis on Software QA and Automation Testing including Mobile, Website, Security, SQL, SOAP and RESTful API. Experience with Automation tools like Selenium WebDriver, Selenium Robot Framework, SOAPUI pro and QTP.

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.

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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. I have, extensive knowledge of program leadership, research project management, information technologies and molecular biology and genetics combined with outstanding communication, analysis and collaboration skills results in 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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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, particularly glioblastoma and glioma. We mostly use high throughput sequencing and computational analysis in our research.

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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.

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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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.

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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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.

I obtained training and work experience in genomics and systems biology. My primary research interest lies in uncovering the molecular mechanisms of complex human disorders, such as Alzheimer’s disease and Cancers, using systems genomics approaches. In the past five years, I have a track record of developing and applying novel integrative genomic and systems biology approaches to identify key biomarkers, biological pathways, and gene networks involved in complex diseases and pathologies. My expertise in systems biology, integrative genomics, and bioinformatics has attracted numerous collaborations with international and national consortia (e.g., CARDIoGRAM, Framingham Heart Study, WHI, GENESIS) and individual investigators. In this context, I am delighted to join the Banchereau lab, which has developed a strong genomic approach of the immune system, both in healthy individuals over the whole lifespan or patients suffering from various diseases. I am looking forward to apply my skills in the integrated analysis of multi-omics approaches to the current project on to analyze at the single cell level the difference between elderly responders and non-responders to influenza vaccination.

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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