Computational Sciences supports computational approaches to complex problems; develops software applications and platforms that facilitate access, visualization and sharing of data and algorithms and support JAX's research data and service products. In addition to our staff listing below, we have a full list of Computational Sciences Interns available.
I received a B.S. in Biology and Biotechnology from Endicott College and joined JAX shortly after in 2014 as part of the Transgenic Genotyping Services group. My primary role within this group was as part of the Process Improvement Team focusing on laboratory automation. When I joined the group I began developing automated pipelines for data-analysis and workflow management. This work grew into larger automation projects where I utilized liquid handling robotics to develop platforms for high-throughput genotyping.
Recently I've joined the Computational Sciences department as an Associate Scientific Software Engineer where I will be working on the Clinical Knowledge Base (CKB). In this new role I hope to leverage my passion for learning new technologies, as well as a focus on innovative design, to contribute to the growth and mission of JAX.
My project focuses on combining spatial transcriptomics and imaging data to predict phenotypes in cancer and other diseases. In my previous research I developed TISMorph software (in Matlab) to add new cell shape features which common software were not able to capture. Then I used these features to distinguished between cancer and normal cells with single cell resolution and predicted the function of novel kinases. I also mathematically modeled nuclear Beta-catenin's switch like response to WNT stimulation. During my last four years I have worked closely with biologists, developed a pipeline for them in R, and mentored them to analyze, automatically label plate-based assays and visualize their data.
"As you navigate through the rest of your life, be open to collaboration. Other people and other people's ideas are often better than your own. Find a group of people who challenge and inspire you, spend a lot of time with them, and it will change your life." ~ Amy Poehler
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.
Well-organized and proficient at multitasking and prioritizing. Hardworking, dedicated professional with extensive experience in Office Administration/HR Management. A strong work ethic with a commitment to integrity and excellence. A team player fostering trust and building strong relationships. Ability to work in any organization. I have provided administrative support for High Level Senior Directors. Expert in submitting Concur requests. Prescreens all candidates via phone and face-to-face interviews. Onboarding of new and exit interviews with departing employees. Equipment and software procurement. Video/room conference scheduling across multiple locations. Monthly reporting including transaction research. Expense submission and reimbursement. Partner with Human Resources.
I have a B.S. in Applied Mathematics from NYC College of Technology. I started my time at Jax in 2014 as a Software Quality Assurance Intern, joined Computational Sciences as an Associate Scientific Software Engineer in 2016 and became a Scientific Software Engineer in 2018. I'm a "full-stack" engineer who works on a variety of problems including genomics algorithm optimization in C/C++, RESTful API design and implementation using Flask, software reliability engineering, cloud computing, and project and systems design. I'm currently pursuing an M.S. in Computer Science as a member of the inaugural class at The Roux Institute at Northeastern.
I received my M.S. in Computer Science and Engineering and B.S. in Ecology and Evolutionary Biology from the University of Connecticut researching biological databases and ontologies. For over ten years I have been leading the development of web applications for multiple Fortune 500 companies in a number of industries including environmental safety, facility maintenance, and media.
I am excited to be bringing my research and industry experience to The Jackson Laboratory community and contributing to the development of robust, innovative software designed to help us solve some of the most complex problems facing the biomedical community today.
I hold a doctorate in Statistical Sciences from the University of Padua, Italy (1995), and was a graduate scholar at London School of Economics (1991-92), Northwestern University (1992) and UC Berkeley Statistics (1992-93). I then joined as a postdoc Stanford University (Computer Science PDP-AI Lab 1994-98), followed by a NATO-CNR Fellowship at the Niels Bohr Institute (Un. of Copenhagen) and the Danish Technical University in 1999 (neurocomputing and artificial learning), and an ERCIM fellowship at CWI - Center for Mathematics and Computer Science, in Amsterdam (2001-02) (stochastics).
After a research appointment at the Mathematical Sciences Research Institute UC Berkeley (2003) and a senior scientist appointment at Boston University (2004, Biomed. Eng. Dept.), I held a position at the biotech Serono in Evry (2005, Head of Methods) before joining the CRS4 Bioinformatics (Polaris Science & Tech Park, Pula-Italy) as the Head of the Quantitative Systems Biology Group (2006-11). Afterwards, while engaged (2012-15) in founding the Laboratory of Integrative Systems Medicine with the Institute of Clinical Physiology, National Research Council of Italy (CNR), and coordinating the CNR's Big Data in Health activities (2016-17), I joined the former Center for Computational Science (now IDSC) at the University of Miami as the Head of Computational Biology & Bioinformatics (2011-22).
I held faculty appointments in Shanghai (the Chinese Academy of Science, 2011), in Rio de Janeiro (the Fiocruz Foundation (2008-10), and I was appointed visiting scientist at the International Centre for Theoretical Physics in Trieste, Italy (2003), and at the Institut des Hautes Études Scientifiques (IHES) in Paris, France (2010).
I conduct research on methodological (Data Science & Big Data; Networks; Artificial/Machine & Statistical Learning) and applied themes (Radiomics & Medical Imaging, Systems Medicine, Cancer and other complex diseases).
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.
My research has focused on blood transcriptomics and its application in patient-based research, most notably in the field of infectious disease, autoimmune and vaccinology. My primary role at the Jackson Laboratory is to support the development of systems immunology and immunoinformatic research capacity – including through the implementation of training programs and the support of projects and investigators conducting immunology research at Jax.
I hold a Ph.D. degree in biophysics from Rice University with a focus in gene expression network analysis and neural network analysis. I developed models to study how biological structure spontaneously arises in an evolving system. After Ph.D. I worked as a data engineer at Two Sigma Investments,I built tools to automatically acquire, process and visualize financial data and maintained database used for trading and research.
I’m a computational scientist working at the intersection of genomics, machine learning and nonlinear dynamics in advancing data-driven scientific discovery.
I earned my PhD in physics focusing on the mathematical modeling of dynamical systems (deterministic and stochastic differential equations) on graphs (complex network topologies) to study the emergence of rich spatio-temporal patterns in various complex systems spanning from physics to ecology. After PhD, I did my first postdoc in theoretical ecology group at the Institute for chemistry and biology of marine ecosystems in Germany, where I worked on developing mathematical models of biodiversity in multi-species ecosystems competing for limited resources and developed expertise in applying machine learning methods to learn species-species trait relationships. Subsequently, I took a second postdoc to delve deeper into exploring some fundamental aspects of scientific machine learning from the lens of complex systems gaining insights that would enhance the interpretability and explainability of ML models.
At JAX, I'm excited to work on predicting cell fate dynamics by constructing a transcriptome-wide continuous vector field from RNA velocity.
After obtaining a B.S. in Electrical Engineering and Computer Sciences at the University of Maine in 2021, I began a software engineering internship with Jax. After my internship, I started my position as Associate Scientific Software Engineer on the Computational Science team. I work as a research and development engineer, seeking to find creative solutions to complex datasets and problems at Jax. My experience includes UI design, data modeling, web app design & development, and data visualization. My back-end experience is varying due to my Electrical Engineering background, but I'm working towards becoming a full-stack engineer.
I'm looking forward to expanding my knowledge at Jax and being apart of the Cube Initiative.
I use quantitative approaches to unravel the genetic factors that underlie complex disease susceptibility. I develop and apply computational and statistical methods to integrated large scale ‘omics data to provide further insights into the genetic pathways involved in the risk of disease.
I received my PhD in genetic epidemiology from the School of Medicine, University of Queensland, Australia, researching the genetic and environmental influences of obesity in Australian twin families. I completed post-doctoral positions at the University of Hong Kong working on founder mutations in Hirschsprung’s Disease in Han Chinese, and at the Singapore Eye Research Institute where I investigated the genetic variants linked to age-related macular degeneration and the role of endophenotypes in glaucoma in the three major ethnic groups in Singapore – Malay, Indian and Chinese. As a researcher at the Massachusetts General Hospital, I used exome chip and targeted sequencing data from related and unrelated individuals to identify novel genetic variants responsible for Type 2 Diabetes through their association with related quantitative phenotypes.
At the New York Genome Center, I was a senior bioinformatics scientist in genetic epidemiology and statistical genetics responsible for ensuring the integrity as well as the analysis and interpretation of pedigree-based data. I also provided lead support to several large ongoing projects which used sequencing and chip data to more precisely localize and characterize genes underlying previously identified variants in diverse ethnicities and races including African Americans, Puerto Ricans and Europeans. Before joining JAX, I was a population informaticist and team lead at Sema4, building out the molecular ancestry calculation pipeline for the expanded carrier test using new genotype technologies.
I joined the Computational Sciences - UI/UX Engineering crew in August 2022. Primarily, I will be working on the design for the BioConnect application, but will also be working with the team to create consistent, usable, and enjoyable user experiences across all CS applications.
Prior to JAX, I was a user experience designer for Centene Corporation's internal medical management systems. And before becoming an IT professional, I worked in libraries and museums in both archives and collections management and then program and exhibit evaluation.
I have been in QA for past 14 years and I have performed testing in various domains like E-Commerce, Servers, Storage and Network Controllers, WITSML, Oil & Gas and Education. I love to test and work with my team to deliver a Quality Software Product to clients.
During the last year of my post-doctoral research at Michigan State University I have developed a computational framework DECNEO for discovery of the combinations of receptors from a large collection of single cell RNA sequencing datasets. The single cell transcriptomics data processing and cell type annotations, which are crucial inputs for DECNEO, have been addressed in my previous work, Digital Cell Sorter, where the methods for single cell data processing, Hopfield landscapes visualization, anomaly detection and automatic identification of cell phenotypes were developed.
My objective is to continue developing bioinformatics algorithms and encapsulate these novel methods in reliable and ready-to-use software. My strong background in the physical, computational sciences, and large-scale computational techniques allow me to model biological systems in multidisciplinary projects. Given my background in software design for many applications, I will be able to contribute to a sustainable cyberinfrastructure for researchers in bioinformatics, and for the biological sciences in general. In my future research, I anticipate developing algorithms to explore analysis of Next Generation Sequencing (NGS) data.
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/).
I'm interested in how to leverage open-source software to help advance scientific research. An active contributor to open-source projects, I contributed to the development of data analysis tools used in synchrotron experiments. I am now a member of the Computational Science team at JAX working on tools used for genomic science.
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.
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 earned my Ph.D at the University of North Carolina at Chapel Hill in the toxicogenomics lab of Ivan Rusyn, where I studied the effects of genetic diversity on gene expression. I then worked at the Jackson Laboratory in Gary Churchill's group, where I developed and applied analytical methods for genetically diverse mice. I developed the first tools for haplotype reconstruction and genetic mapping in Diversity Outbred (J:DO) mice and have been involved in many projects that use J:DO mice. I currently work on tools and educational materials to help investigators use genetically diverse mice in their research.
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.
Since 1998 I have been developing scientific software, for instance computational fluids dynamics delivered over desktop and the web, data acquisition and analysis at the UK's largest science project (Diamond Light Source) and now with great pleasure at JAX. Scientific software development is where my heart is and what makes be most interested at work (I have solved problems outside this space but they are not as fun!)...
My research mainly focuses on developing and applying computational and statistical approaches to integrate and interpret large-scale biological “omics” data, with the goal of identifying disease mechanisms, biomarkers and treatment targets.
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.
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.
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.
Studies the complex biological processes using machine learning and physics-based dynamical (linear/nonlinear) models using data-driven analysis. Develops computational methods, tools, pipelines, and interactive web apps to construct, model, simulate, and visualize the gene regulatory networks in various biological processes using literature-based evidence as well as RNA-Seq (bulk/single-cell), ATAC Seq, and CHiP Seq data. Employs deep learning on video-based assays and genomics data from behavioral studies to connect behavior and genetics.
My primary focus at JAX is on developing data management systems and tools for the exploration and visualization of complex biological data and its interpretation. As part of a multidisciplinary team at JAX, I frequently collaborate with both experimental and computational scientists on data analysis and visualization projects.
In my role as a Scientific Software Engineer, I am leading the JAX Synteny Browser, a novel tool for interactive visualization of regions of conserved synteny between two genomes based on their biological properties such as function and phenotypes.
Another hallmark initiative that I am closely involved in is JAX’s Patient–Derived Xenografts (PDX) program, a platform for data management, visualizations, analysis, reporting of cancer models studies. I lead the implementation of interactive visualizations for cancer treatment response studies (SOC) and develop automated software to run robust pipelines for the analysis of genomic variations in cancer models.
I am also a key member of the Mouse Phenome Database (MPD) project, an integrated platform to explore physiology and behavior through genetics and genomics, for which I create highly interactive data visualization tools using the latest cutting edge and open source technologies.
In addition, I also teach several data science and programming courses/workshops at JAX on coding skills in R, Python and SQL.
My doctoral work focused on vertebrate cell biology, but relied on genomic sequence analysis to identify a more tractable homolog of an ostensibly vertebrate-specific gene in C. elegans. I subsequently worked in the pharmaceutical industry on identifying potential drug targets, comparative genomics, RNA sequence assembly, and bulk annotation of sequence data. Later, as a researcher at USEPA, I identified transcriptomic signatures of exposure to various toxicant classes, developed predictive models for chemical risk assesment and worked on computational tools for contaminant screening using time-of-flight mass spectrometry. Since joining JAX, I have been leading small teams of computational scientists and software engineers, as well as developing pipelines for long-read sequence analysis and splice-variant analysis. I have also taught short courses on various aspects of applied statistics, machine learning, software development and bioinformatics.
My primary research focuses on studying various cell types especially cancer cells using single-cell technology. Separately, I am also interested in understanding the impact of TET2 mutation in B cells functions and malignancy.
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
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.
I hold a M.S. in Computer Science with a background and hands on experience in High Performance Computing, Analysis Pipeline Development, Genome Assembly & Annotation, Microbiome Data Analysis, Long-Read Sequencing (PacBio & Oxford Nanopore). Previously I worked in The Genome Institute (Washingtion University School of Medicine in St. Louis), The Weinstock Lab and Microbial Genomic Services at JAX. I am now a member of Computational Sciences and working on Pipeline developments & Microbiome related projects.
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.
Camille Liedtka has a background in medical Anthropology and Behavioral Analysis and is pursuing a master’s degree in Project Management at Northeastern University. She is currently working at Jackson Labs as an Associate Research Project Manager with the Computational Sciences team where she leverages agile methodologies and workflows to allow for projects to match the pace of innovation. Previously she worked at Wayfair as a customer service trainer where she wrote, developed and facilitated curriculum while leading international project teams focused on knowledge services & distribution.
I joined the Jackson Laboratory having previously worked on a diverse set of organisms. My Ph.D. focused on population and conservation genetics of a submersed aquatic plant species, which was targeted for restoration efforts. Prior to joining Jax, I was a member of the Department of Entomology at the Smithsonian Institution. In that role, I was responsible for the generation and analysis of target enrichment data used in phylogenomics. At Jax I have been involved in research projects associated with the Jax PDX program, PDX Network, PIVOT consortium, and Cube initiative.
My 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.
I have always been interested in biology but found my skill set tended more towards math and data science. After graduating with a B.A in Mathematics in 2016 I joined a computational biology lab as a research assistant, there I developed experience working with deep neural networks for image feature extraction, as well as the modeling of single unit neuronal recordings. In 2020 I was excited to accept a full time position in the Computational Sciences Core leveraging my previous experience.
Broad experience working with animal model based LIMS systems, including UI design, system architecture and integration, data modeling, and development. When I started at Jax in 2003, I worked for Dr. Carol Bult in the NMF on the JaxTrack system supporting colony management and phenotyping. I subsequently joined Computational Sciences, where I worked on the JCMS and JaxLIMS systems. I spent a few years away from Jax working on a commercial SaaS LIMS system, primarily responsible for system architecture and DevOps. This gave me the opportunity to work with a large number of academic and commercial laboratories doing animal model research, and allowed me to participate in Microsoft engineering workgroups and case studies. I am excited to be back as a software engineer in Computational Sciences and look forward to working on the Cube initiative.
I am a Computational Scientist at the Jackson Laboratory @ Farmington, USA. I have several years of professional experience in Computational Biology, Bioinformatics, Statistics and Biostatistics. I have mainly authored publications on bulk- and single- cell transcriptomics (RNA-seq, CAGE and Microarrays), 3d chromatin organization, signal processing and image analysis. I have worked in cancer stratification and cell cycle, mast cell trascriptome, type 2 diabetes and, more recently, on heart disease, cardiac (trans)differentiation and characterization of myocyte and non-myocyte populations. Currently, I am mainly working on the single-cell transcriptomics and epigenomics of type 2 diabetes.
I received my PhD in Integrated Biomedical Science, concentration in cancer biology, from Ohio State University and my MS in Biomedical & Health Informatics from University of Washington. During my PhD, I focused on mouse genetics and genomics from the perspective of a bench scientist; my dissertation work involved investigating mutation types and numbers in preneoplastic cells and tissues from Fhit knockout mice. I completed a postdoctoral fellowship and MS degree simultaneously at the University of Washington where I investigated differential gene expression in the parasite Leishmania donovani.
I recently joined Jackson after many years of diverse commercial software development, including consumer, professional and medical software and firmware. Most recently I worked on a team that developed image processing algorithms for detection of physical features in coronary OCT (optical coherent tomography) scans. Prior to that I developed the image processing chain for a disposable endoscope used for direct visualization of pancreatic ducts. For this product I also designed the control feedback algorithm for automatic illumination control. I also worked on the image processing chain for an optical particle analysis system. While the system had its origin in marine biology research, it's also been used in pharmaceutical and industrial applications. Prior to working with the scientific and medical communities I worked for many years in commercial mapping. In addition to developing consumer and professional mapping applications, I also developed the software for a multi-sensor 360-degree panoramic camera. The purpose of this camera was to collect mapping data from a moving terrestrial platform to complement data collected from the aerial platforms. I look forward to applying these experiences to the diverse problems at Jackson.
I hold a PhD in Life Sciences from University of Tennessee/Oak Ridge National Laboratory Genome Science and Technology Program, with a focus in statistical and quantitative trait genetics of behavioral traits in genetic reference populations and integrative functional genomics of behavior across species and experimental platforms. During my Ph.D. program I investigated the increased precision and resolution in QTL mapping using mouse reference populations including the expanded BXD recombinant inbred (RI) strain panel and the Collaborative Cross (CC) reference population The BXD RI study highlighted the increase in statistical power obtained in using the expanded BXD RI strain panel. The CC study highlighted the increased allelic variation and QTL mapping precision achieved. Upon completion of my Ph.D., I joined the Computational Sciences - Statistics and Analysis group at The Jackson Laboratory in 2012 as a biostatistician, with responsibilities including QTL analysis, expression QTL analysis, gene expression analysis, statistical modeling of diverse biological datasets, and statistical consulting. In addition to my contributions towards the field of quantitative trait genetics and statistical genetics, I have undertaken several other data analysis tasks involving differential gene expression analysis using next generation sequencing technologies, leading to the generation of new or validation of existing hypothesis.
Over the past ten years, I specialized myself in big data analysis (i.e. Machine-Learning) applied to Genomics, Semantics and Next Generation Sequencing (NGS) data linked to single-cell and cancer research. I obtained my PhD in Computational biology and Evolutionary Genomics at Ecole Centrale de Lyon (Lyon University), France. I worked both in academics (University of Hawaii Cancer Center, UCSD) and in a Startup company (R&D engineer) developing and using my research skills on fundamental and more concrete problematics. My current work involves working with single-cell ATAC-Seq and RNA-Seq datasets, multi-omic datasets linked to survival, new methods, pipelines and data visualisation interfaces development.
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.
My past research focused on combining experiments in natural and constructed populations with high-throughput genetic, genomic, and transcriptomic analyses to better understand the current and historical forces that have shaped the evolution of traits, and using those patterns to infer what changes may occur in the future. In my new work at JAX I plan to leverage the knowledge and experience I gained through my academic research to help with the diverse projects that are being conducted to help improve our understanding of the relationships between our genes, environment, and health outcomes.
My perspective on biology celebrates what diversity can teach us. Within the mouse species, I study genetically diverse populations such as the BXD, Collaborative Cross, and Diversity Outbred. With Elissa Chesler and colleagues in the CSNA, my work has reiterated that who a mouse is – their genetic makeup and its sex – matters greatly to how a mouse behaves and how their brain responds. I am particularly interested in comparative and cross-species techniques, which give us perspective on the conserved systems that underpin similar behaviors spanning the animal kingdom.
Though I now work in Computational Science, my training was as a mouse behavioral neuroscientist who studies genetics and genomics. My work convinced me that biologists must do two things to understand complex systems like the brain: 1) collaborate with each other, and 2) use high-throughput techniques like next-generation sequencing and high-performance computing. I am delighted to collaborate with multiple investigators at JAX, where I apply the skills I attained in computational work to find the new biology of disease that will lead us to better treatments.
Variation in observable traits, such as disease susceptibility, is pervasive in the natural world. Recent advances in sequencing and computation are providing us with an unprecedented view of patterns of genetic variation within species. A major outstanding challenge is to identify and characterize the specific genetic variants affecting complex traits, and the mechanisms through which they do so.
I am a biologist and data scientist who uses single cell functional genomics to understand the genetic basis of complex traits. I utilize genetically diverse model organism populations together with the methodology of statistical and quantitative genetics to reveal novel mechanistic insights into the biology of complex traits. My background and interests include cancer, cardiovascular disease, diabetes, and obesity/metabolic disorders.
My project focuses on the analysis of spatial single-cell proteomics data (e.g. Imaging Mass Cytometry, CODEX) and spatial transcriptomics data (e.g. Visium Spatial Gene Expression by 10x). Previously, in collaboration with biologists from Leiden University Medical Center I have developed interactive pipelines for the analysis of single-cell high-resolution images (ImaCyTe, SpaCeCo). My research interests also include the analysis of single-cell tissue images with Graph Convolutional Networks.
Recent advances in DNA sequencing technology led to the generation of vast amounts of sequencing data and provided an unprecedented opportunity to understand the complexity of the genome. However, this massive flood of sequencing data also created challenges in mining patterns of interest from this data. Moreover, the diversity of datasets [RNA-SEQ, ChIP-Seq, Exome, Whole-Genome, ATAC-Seq, HiC, etc.] generated from various technologies also demand more integrated evaluation across multiple sequencing platforms and data types. My dissertation focused on deriving meaningful patterns from these datasets generated from both model and non-model species, studying the variations in non-coding RNA (ncRNA) secondary structure, and developing novel methods for ncRNA detection in the genome using patterns of chromatin modifications. In my role as an associate computational scientist (2014-2016), computational scientist (2016-2019), and then Senior computational scientist (2019-2021) at The Jackson Laboratory (JAX), I worked and led various research projects in collaboration with the faculty. I currently serve as an Associate Director of the Genome Informatics group in the Jackson Lab Department of Computational Sciences and lead a large team of Computational scientists and Bioinformatics Analysts. My group at JAX primarily focused on building systems/algorithms to effectively analyze next-generation sequencing data generated from various sequencing technologies from humans and mice. We have developed particular expertise in the analysis of patient-derived xenograft (PDX) cancer samples as a part of the NCI PDXNet and Pivot program. To sum up, my team focuses on research and building effective systems to help facilitate the research.
I joined The Jackson Laboratory's Computational Sciences Department to become the Deployment Lead in the Lab’s involvement in the NCI Cancer Biomedical Informatics Grid® (caBIG®) initiative and stayed on as a member of the Stats and Analysis group. My background in protein and genomic databases and conceptual representations of life sciences research has enabled me to contribute to the clinical and the PDX programs. I continue to gain experience in analysis of next generation sequencing, calling on my history of bench research in cancer, pharmacology, and transcriptional regulation. I have experienced the roller coaster ride of small start up companies and welcome the opportunity to be part of the JAX community. On any given day, I may spend time wrangling data for PIs or collaborators, launching a DNA-seq analysis, tracking down data for a potential PDX customer and checking out a new analysis tool or online database.
My initial contributions to science were the result of a Master of Science education program (University of Illinois - Champaign-Urbana) endeavor to identify quantitative trait loci affecting swine meat characteristics. Shortly after my leave of the University, I joined The Jackson Laboratory in 2007. The numerous collaborations that took place at the Laboratory have resulted in publications pertaining to a variety of conditions including: alopecia areata, asthma, cancer (of the brain, lung and skin), diabetes, chronic kidney disease, eye disease, and reproductive disorder. The general aim of these studies is to provide mouse model information as a method to improve human health. My role in these studies has been as a biostatistician consultant.
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.
I am 25+ year technology and research management professional with experience in leading complex programs in the life sciences. My knowledge of strategic program leadership, research project management, information technologies, molecular biology and genetics combine with outstanding communication, analysis and collaboration skills to enable transformative team data science.
I am primarily trained as a computational scientist. During my Ph.D., I worked on the developing probabilistic machine learning methodologies for various computational problems in engineering systems (uncertainty quantification, stochastic partial differential equations, dynamical systems, inverse problems etc.). In my first postdoctoral appointment, I worked on the problems pertaining to the interpretability of sequence-function relationships learned by DNN models from high-throughput sequencing functional assay datasets. As a computational scientist at JAX, I work on cross-species integrative analysis of single-cell functional data.
I am a full-stack principal level scientific software developer with a passion for software development that enables scientific research. I have over two decades of experience in a variety of industries, with a focus in biotechnology.
My role at Jackson Laboratory is one of technical leadership on multi-developer projects, using agile techniques, striving to provide meaningful/usable systems in a timely manner.
Emerging single-cell transcriptomic and highly-multiplexed imaging methodologies are advancing our basic understanding of tumor heterogeneity and its impact on patient outcome and therapeutic response. My previous work involved predicting disease progression and drug response using bulk (principally, expression) data. My interests lie in leveraging these new single-cell modalities to improve our ability to extract insight from the wealth of existing (and clinically annotated) bulk data. For example, we have recently completed a deconvolution DREAM challenge comparing methods that infer immune sub-populations from bulk expression data. Several participant methods used single-cell RNA-seq to identify markers that could subsequently be used to detect the corresponding population in bulk data.
My research focuses on revealing the genetic underpinnings of traits that vary continuously in populations, including those sensitive to environmental inputs. During my doctoral training, I used a series of crosses to characterize the relative contributions of genetics and aging to variation in fertility among hybrid male mice. My postdoctoral work focused on identifying novel genetic modifiers of toxicant susceptibility using a genome-wide association study of developmental delay in C. elegans. Throughout my career, I have sought to improve our understanding of human health and disease using genetically diverse model organism populations. At JAX, I support ongoing projects using quantitative genetics in the Diversity Outbred population by developing and maintaining computational workflows for QTL mapping, haplotype reconstruction, and genomics broadly.
North Carolina State University
2014 - 2019
University of Delaware
2010 - 2014
Adv: Dr. Erik Andersen
2019 - 2022
Graduate Research Assistant
North Carolina State University
Adv: Dr. David Aylor
2014 - 2019
Undergraduate Research Assistant
University of Delaware
Adv. Dr. Shawn Polson & Dr. K. Eric Wommack
2011 - 2014
My research is focused on the mediation roles of molecular between exposures and health outcomes. I developed PMDDA data analysis workflow to perform exhaustive metabolomics annotations. I also proposed the concept of 'reactomics' to retrieving general chemical relationships among molecular in biological samples. Meanwhile, I developed 'gatekeeper' model to screen the molecular sensitive to multiple exposures. With background from both wet lab and dry lab, I am trying to improve regular statistical or machine learning model with biochemistry knowledge. I am also interested in reproducible research and built a R based data analysis image (xcmsrocker) for reproducible metabolomics data analysis.
I have a diverse expertise in analysis of Multi-omics datasets, Immunology, Microbiome, Developmental Biology, RNA biology and Cloud Computing. During my Ph.D. I studied the downstream targets of Hox genes in developing hindbrain using mouse, zebrafish and chick models. My postdoc was focused on developing Bioinformatics pipelines for the analysis of microbiome data and various immunological studies. Currently I am developing pipelines for identification of lncRNAs and splicing events in RNA-seq datasets and integrating various omics data.
My research mainly lies in developing and applying statistical and system computational approaches to discover complex disease mechanisms, biomarkers and treatment targets by integrate large-scale biological “omics” data.
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.