5^th International Conference on Biometrics & Biostatistics October 20-21,2016 Houston,Texas,USA

Biography:

Upendra Kumar Devisetty earned his PhD from University of Nottingham, UK and completed Post-doctoral studies at the University of California Davis and Oregon State University. He is currently working as Science Informatician at CyVerse, a life sciences cyberinfrastructure funded by the National Science Foundation (NSF). He has published more than 10 papers in peer-reviewed journals and has been invited to speak at several international conferences

Abstract:

CyVerse (formerly iPlant Collaborative) is a life sciences cyberinfrastructure funded by the National Science Foundation (NSF). The infrastructure’s purpose is to scale science, domain expertise, and knowledge by providing a variety of computational tools, services, and platforms for storing, sharing, and analyzing large and diverse biological datasets. The Discovery Environment (DE) in CyVerse provides a modern web interface for running powerful computing, data, and analysis applications. By providing a consistent user interface for accessing tools and computing resources needed for specialized scientific analyses, the DE facilitates data exploration and scientific discovery. DE merges the “science gateway” functionality and the bioinformatics “work bench” with high-performance data management to allow seamless access to reusable computational workflows that can run at very large scales. It is common in bioinformatics to build new analysis methods utilizing multiple programs, libraries, and modules. However, each analysis that uses these tools requires specific versions of the operating system and underlying software. Docker is a container virtualization technology that wraps software of interest (e.g., a bioinformatics tool) together with all its software dependencies so it can run in a reproducible manner regardless of the environment. CyVerse has adopted Docker for integrating software apps that run in the DE’s Compute Cluster. The user creates a Dockerfile, which is sent to CyVerse and used to build the Docker image containing the tool. After the image has been deployed on the DE’s compute cluster, the user can build an web app in the DE to enable other researches easily use the tool

Biography:

Azam Yazdani has completed her PhD in Statistical Causal Inference from Cambridge University, England, and Friedrich Schiller Jena University, Germany. She introduced the granularity directed acyclic graph (GADG) algorithm which is recognized at the 2015 atlantic causal inference conference and won “The Thomas R. Ten Have Award”. The GDAG algorithm integrates different biological levels of granularity in large scales to identify causal networks. She has carried out first-rate studies on robust statistical structures. Her research work aims to tackle the problems of finding principals which govern the mechanism of disease to provide a better understanding of the disease mechanisms applying her expertise on data integration in large scales and generating causal relationships.

Abstract:

Background: Analyses of individual metabolites does not provide information about likely targets of intervention because of co-linearity among the metabolites. One approach to directly incorporate relationships among the metabolome is to consider networks.

Method: Powerful and advanced analytic strategies are required to identify pathways from genome and environmental exposures to disease endpoints using comprehensive data from different biological granularities. To address the emerging challenge of relating information among different biological granularities, a granularity directed acyclic graph (GDAG) algorithm has been introduced, validated and applied to create strong instrumental variables from the genome granularity (Mendelian principle) to identify robust causal/Bayesian networks over traits. Analyzing biological causal networks provides important insights into the hierarchical regulation of physiology and metabolism.

Results: For the application, information from 1,034,945 genetic variants distributed across the genome was extracted and then employed by the GDAG algorithm to identify a metabolomics causal network among 122 serum metabolites. Five modules, largely corresponding to functional categories (e.g. amino acids), were identified over the network and module boundaries were determined using directionality and causal effect sizes. Based on causal network parameters, individual metabolites were identified as hypothesized targets for intervention and prediction.

Conclusion: Given the metabolomics causal network, metabolites with direct effect on risk factors (e.g. triglyceride) were identified which improved the understanding of the role of metabolites in quantitative risk factor phenotypes. Finally, pathways from the genome to risk factors via metabolites were determined to reveal underlying biological networks. Future steps will connect these results to cardiovascular disease end points.

Biography:

Dr. Wang got his Ph.D degree in genome science and technology at the University of Tennessee in 2011, with the thesis on the TGF-beta function in neuron development.  After graduate, he moved on and switched to bioinformatics and computational biology. Currently he is working as a postdoc at MD Anderson Cancer Center, University of Texas. His main research interest focuses on data mining, machine learning, especially with their integration and application in cancer genomic and precision medicine.  Dr. Wang has published 16 scientific papers in international well-recognized journals. He has been ACM  SIG and ISMB members and also served as journal reviewer for many top-tier journals in the field of bioinformatics and systems biology

Abstract:

Accurate prediction of the functional effects of genetic variation in cancer is critical for realizing the promise of precision medicine. Due to a lack of statistically rigorous approaches and training data, differentiating driver mutations from passenger mutations remains a major challenge in cancer research. We develope a novel Bayesian method, xDriver that combines mutations and their sequence-derived functional features (such as GERP scores) with gene expression in a population of tumor samples to identify mutations that significantly alter gene expression landscapes. We demonstrate using 752 breast cancer samples in the cancer genome atlas that our integrative approach is able to significantly improve the accuracy of driver mutation identification over existing approaches that do not perform such integration. In particular, our approach is able to enhance the functional prioritization of so-called “tail” (rare) mutations and more accurately delineate cancer subtype specific mutations (such as PIK3CA mutants associated with lymph node negative patients). Importantly, scores generated by our model achieve the best agreement with in vitro functional cell viability data obtained from transfected Ba/F3 and MCF10A cell-lines, compared to predictions from other commonly used algorithms. Our results exemplify the importance of integrating gene expression in predicting candidate driver mutations. This integrative study has the potential to impact functional genomic experiments and is expected to link cancer genomic event to precision medicine

Biography:

Wei Zhong, done Ph.D University of Cincinnati College of Medicine, Sr Biostatistician, Project Director at ICON Clinical Research, San Francisco Bay AreaPharmaceuticals

Abstract:

Missing data is a concern in most clinical trials, especially in those with a longitudinal structure. Sensitivity analyses are often used to assess the robustness of statistical inference made under conditions of missing data. There has been an increasing trend to use pattern mixture models (PMMs) to impute missing data in sensitivity analyses. The key to proper implementation of PMMs is to make valid assumptions about the link between observed values and missing values. By applying different assumptions to the data from a phase II trial, we have demonstrated that conclusions drawn from such sensitivity analysis fluctuate depending on what assumptions underlie the missing data imputation. Therefore, PMMs should be used with caution and the plausibility of model assumptions should be evaluated first by the clinical study team, taking into consideration past experience with the study drug and the patient population

Biography:

Murat Yazici has completed his BSc in Statistics and MSc in Quantitative Methods. During his Statistics and Quantitative Methods educations, he studied Bayesian Statistical Estimation as a thesis-project and Fuzzy Robust Regression Analysis as a master thesis. He has several scientific papers and a book chapter study about big data analytics. He has attended to several conferences to present his studies. He works as Senior Data Scientist & Researcher at JForce Information Technologies Inc. which is one of the solutions partners of IBM in Istanbul. His research areas include “analytics studies in many areas such as health, telecommunication, security, banking, insurance, transportation and logistics sectors”.

Abstract:

In statistics, a point process is a set of independent points in time, or geographical or general spaces. It is also a type of random process. The streak of lightning can be an example as a point process in both time and geographical space. The statistical disease mapping and location detection of radio stations in a telecommunication network is given as another example. Also, point processes are powerful instruments in statistics for modeling spatial data which is of interest in various disciplines such as plant ecology, epidemiology, geography, seismology, materials science, telecommunications, economics, astronomy and others. Spatial point processes are a type of stochastic process, each of whose realizations consists of a finite or countably infinite set of points in the plane. They are useful in the analysis of observed patterns of points; where the points represent the locations of some object of study e.g. disease cases, or petty crimes. Furthermore, spatial point processes occur in various areas in biology and ecology, e.g. in connection with the spreading of insect larvae, the distribution of trees in woodlands, the distribution of bird's nests, or experiments with revitalization of eroded areas in the Wadden sea. The geographical epidemiology is interested in the examination and definition of disease. It is also concerned with geographic variations and usually demographic, behavioral, environmental, socioeconomic, and genetic information etc. Some of types of studies of the geographical epidemiology are disease mapping, geographic correlation studies, and disease clustering. The spatial point process in geographical epidemiology is concerned with spatial analysis of geographical epidemiology by using point process.

Biography:

Tapti Sengupta is an Assistant Professor and Coordinator in the Department of Microbiology at West Bengal State University. She started her career as a Fisheries professional focusing on aquatic animal diseases and environmental modelling aspects but currently she is pursuing a research on Mycobacteriosis both in human and animals. She is involved in a number of projects with central and state government funding especially from Department of Biotechnology and ICAR. She is guiding a bunch of potential research scholars and inspires fellow and supervising numerous summer projects along with her departmental and administrative work

Abstract:

Nontuberculous mycobacteria (NTM) are ubiquitous in the environment and are responsible for several ulcerative diseases in human and/or animals, also known as mycobacteriosis. These granulomatous ulcerations caused by NTM infections have been increasing in number over the past decades, especially in immunocompromised and HIV/AIDS patients and in domestic and free living animals. The aim of the study was to ascertain the spectrum of nontuberculous mycobacteria within mycobacterial isolates not belonging to M. tuberculosis and M. avium complexes but phylogenetically related to them. Fishermen and fish handlers were examined for ulcerative lesions typical for mycobacterial infections for consecutive years, 2015 and 2016, from various districts of West Bengal viz. A- Jalpaiguri, B- Murshidabad, C- North 24 Parganas and D- Kolkata, India. Isolated cultures were analyzed within the framework of ecological studies, carried out in fish models. A total of four mycobacterial species were identified based on bio typing and molecular analysis. 306 human samples (swab and pus from the sight of the ulceration, sputum) and 423 environmental samples viz. water and soil relating to the fish handlers, were pooled and screened for two years. 214 human samples were positive for M. fortuitum and M. kansasii whereas 274 environmental samples were positive for M. kansasii and M. smegmatis. M. chelonae was ubiquitous in nature. M. fortuitum showed significant positive correlation with the lower temperature suggesting the role of environmental factors in the rate and degree of infections by the pathogens. M. kansasii and M. smegmatis were found to be opportunistic and presented themselves in primary as well as secondary infections, and in significant levels (p≤0.01) in human samples having direct contact with the contaminated water. Degree of infection showed positive correlation with districts situated in the north of West Bengal. Temperature ranging from 15^o-25^oC. Significant chi square (χ²) value (78.20) was observed at p<0.001 signifying that the degree of infection varied with temperature, with lower temperatures showing greater NTM occurrence visibly in the cooler districts. The present study signifies the prevalence of NTMs in West Bengal which was not reported earlier. The rise in the number and variety of NTMs especially of groups related to M. tuberculosis complex raises questions about drug regimens and undiagnosed symptoms. 70.02% of pathogenic mycobacteria reported in this study, from various districts of West Bengal, emphasize the need for research of these neglected and opportunistic pathogens particularly using multivariate analysis to identify causative factors

Biography:

Leigh Anne H Clevenger is a candidate of Doctor in Professional Studies in Computing at Pace University. As a software engineer at IBM in Poughkeepsie, NY, she has developed solutions for advanced technology microprocessor design for six technology generations. She has nine submitted patents in the area of wearables and healthcare. She was an invited speaker at the 2015 Pace University Cybersecurity Workshop

Abstract:

Continual authentication using passive monitoring of sensor data is not currently available on most mobile devices. This monitoring can maintain confidence that the device owner is the current user without inconveniencing them by requiring frequent re-authentication, for example with password, swipe, or fingerprint. Biometrics used for passive monitoring do not currently include heart sound, which is an interesting choice because it is constantly available, hard to obtain from another person, and has been shown to be reasonably unique between individuals. Clinical cardiology applications currently do not take advantage of the algorithms of heart sound authentication, for example, to indicate a change in the patient’s heart sound on an in-home wearable mobile device app. This research explores the biometric of heart sound for use in passive and continual screening for clinical applications, and for user authentication. Using the heart sound biometric for a cardiac patient allows passive monitoring of sensor data, screening changes in heart sound. Changes from baseline data trigger an alert to the user and caregiver. For user authentication, passive monitoring maintains confidence that the device owner is the current user without inconveniencing users by asking them to re-authenticate to access high security applications. Prior heart sound research is extended for potentially greater user authentication accuracy in the areas of time windows, number of heartbeats, feature vectors, classifiers, sample selection, and noise mitigation. Application and adaptation of user authentication methodologies from speech processing are applied. The methodology can be extended to work with different public and private heartsound datasets

Biography:

S M Bokhtiar was born in Chapai Nawabganj district, Bangladesh on 1 January, 1963. He graduated and achieved B.Sc. Ag (Hons.) from Bangladesh Agricultural
University (BAU), Mymensingh in 1985. MS in soil science from Bangabandhu Sheikh Mojibur Rahman Agricultural University (BSMRAU), Bangladesh and Ph.D
degree from the United Graduate School of Agricultural Science, Ehime University, Japan in 1999 and 2006, respectively. Dr. Bokhtiar worked as post doctoral
research fellow at Guangxi Academy of Agricultural Sciences, Guangxi, China for two years and studied on silicon nutrition of sugarcane crop. He started his carrier
as a Scientific Officer in Farming Systems Research and Development Project (FSR & D) at Bangladesh Sugarcane Research Institute (BSRI), Bangladesh in
1989. During his service period at BSRI, Dr. Bokhtiar was promoted as a Senior Scientific Officer and also performed as a heads of division of Soils & Nutrition
Division, BSRI till December 2010. Dr. Bokhtiar was appointed as a Principal Scientific Officer at Soils Unit of Natural Resources Management Division of
Bangladesh Agricultural Research Council (BARC), Bangladesh in 10 January 2011 and assigned for programme planning, execution, evaluation and monitoring of
soils programme of National Agricultural Research Systems (NARS) in Bangladesh. Currently Dr. Bokhtiar serving as a Director, SAARC Agriculture Center (SAC),
Dhaka, Bangladesh and involved in policy planning, formulation and implementation of the activities in the SAARC member states assigned by SAARC Secretariat
Katmandu Nepal. Dr. Bokhtiar has 60 research papers in his credit with total citations of 253 and author of two books. Dr. Bokhtiar attended several international
seminars and training programme in home and abroad. Dr. Bokhtiar visited several countries like Japan, Thailand, China, Egypt, Philippine, Malaysia, Mongolia,
South Korea, Pakistan, New Zealand and India. Dr. Bokhtiar is actively associated with the International Association of Professionals in Sugar and Integrated
Technologist (IAPSIT) based in Nanning, China since the very beginning of its formation in 2004. Dr. Bokhtiar also served as a Member-Secretary of Exchange and Cooperation Consortium for Agricultural Science and Technology, China- South Asia (ECCAST-CSA) Bangladesh part.

Abstract:

World fisheries production have remarkably increased since 1950 and with present annual 167.2 million tonnes fish production (FAO, 2016), fisheries and aquaculture became the potential contributors to food and nutrition security and livelihoods at global level. Almost 90% of aquaculture production takes place in Asia, most of it in the tropical and subtropical countries. The two South Asian Association for Regional Cooperation (SAARC) countries, India and Bangladesh with the annual production of over 10.0 and 3.55 million tonnes in 2016, respectively rank the 2^nd and 5^th largest fish producers in the world. The sector employs 56.6 million people globally of which India and Bangladesh alone share 32 million people. In South Asian region, at present hardly one third of the existing freshwater ponds and water bodies are engaged in aquaculture. Most of the rural people in the region depend on their backyard ponds and seasonal ponds for their house hold fish requirement throughout the year. These fisheries catches contribute substantially to the national fish production data. However, these production data are never included in the respective nation’s fish statistics data base. Therefore, there is underestimation of fish production data for any particular country in SAARC region. At present, the Food and Agriculture Organization of the United Nations (FAO) maintains global fisheries statistics by collecting data from the member countries. Past experience shows FAO sometimes encountered with incorrect data. Therefore, fisheries data may be scrutinized by the various regional bodies before sending them to world data pool. In this regards, SAARC can play the leading role for regional data pulling and scrutinisation. Also, review of existing methodologies for fish production estimation from diverse water bodies need serious attention. Based on the outcome, necessary policy may be framed at SAARC regional level for fisheries data collection and accurate reporting. Without reliable statistics, effective fisheries management and policy-making are impossible in the region, the major contributor to global fisheries production

Biography:

Tatsuya Takagi has completed his PhD from Osaka University. At that time, he had been an Assistant Professor of School of Pharmaceutical Sciences, Osaka University for 5 years. Then, since 1993, he had worked for the Genome Information Research Center, Osaka University as an Associate Professor until he became a Professor of Graduate School of Pharmaceutical Sciences, Osaka University in 1998. He has published more than 100 papers in reputed journals and serving as Chairman of Division of Structure-Activity Relationship of the Pharmaceutical Society of Japan.

Abstract:

It is significant to estimate the environmental fates of chemical substances which are emitted from factories or as residential wastes. Especially, since hydrolysis plays a main role with regard to chemical substance degradation in the environment, hydrolyzability of such chemicals have to be revealed. However, experimentally obtaining the information is time-consuming. Thus, we tried to predict the hydrolyzability of esters and related compounds using logistic regressions and regularization methods. The hydrolyzability data of 143 chemicals, which were extracted from literatures, were used for these analyses. These chemicals were classified into two categories, ‘stable’ and ‘hydrolizable’, according to their half-life periods. They were also classified into four groups, all chemicals (143), esters (73), amides, and others. In this study, the former two groups were analysed. 88 chemical descriptors were prepared for predicting the hydrolyzability. All the datasets were divided into training (3/4) and test (1/4) sets. Lasso was used as a regularization method. We built the model equation by two techniques using only training data sets. As the results of the analyses, training data were perfectly predicted in the case of esters, and sufficient results were obtained in the case of all chemicals. Even in the case of test data sets, satisfactory results were obtained.

Biography:

Professor Zeleke Worku is a South African academic working at the Business School of Tshwane University of Technology (TUT) in Pretoria, South Africa as an associate professor of statistics and coordinator of the MBA programme of study at TUT Business School. He holds a Ph.D. in statistics (University of the Orange Free State in Bloemfontein, South Africa) and a second Ph.D.in sociology (Aalborg University, Denmark). Professor Worku’s key research interests are in monitoring and evaluation, statistical data mining, biostatistics, epidemiology, public health, sociology, demography, econometrics and business sciences. Before he joined TUT Business School in 2010, Professor Worku has served the University of Natal in Durban, South Africa (1998 to 1999), Vista University in Pretoria, South Africa (2000), the University of Pretoria, Pretoria, South Africa (2001 to 2007), and the University of South Africa in Pretoria, South Africa (2008 to 2009). Professor Worku lives and works in Pretoria, South Africa with his wife and two children

Abstract:

A review of the relevant literature shows that teenage pregnancy and adverse outcomes of pregnancy constitute a major public health problem in South African women of the childbearing age of 15 to 49 years. A longitudinal study was conducted in Tshwane, South Africa in order to identify factors that affect utilization of modern contraceptives and adverse pregnancy outcomes in women of the childbearing age of 15 to 49 years. Data analysis was conducted by using statistical methods such as binary logistic regression analysis, survival analysis, multilevel analysis and Bayesian analysis. The study showed that the percentage of women who regularly used modern family planning methods such as condoms, pills, injections, intra-uterine devices and sterilization was 41.74%. The average ages of women at first sex and pregnancy were 18.72 and 19.36 years respectively. Adverse outcomes of pregnancy occurred in 12.19% of women. Based on Odds Ratios (OR) estimated from binary logistic regression analysis, utilization of contraceptives was significantly influenced by easy access to family planning services, level of support from sexual partner, and young age at first pregnancy. Based on hazard ratios (HR) estimated from the Cox Proportional Hazards Model, the occurrence of adverse outcomes of pregnancy was significantly influenced by easy access to family planning services, unwanted pregnancy, and young age at first pregnancy.  Women who experienced adverse outcomes of pregnancy were characterized by poor utilization of reproductive health and modern family planning services. Based on results estimated from multilevel analysis, there was a significant difference among the 20 health service delivery wards and 11 health service facilities in which reproductive health services were delivered to women with regards to the quality of service delivery

Biography:

Tao Liu has completed his PhD from the University of Pennsylvania. He is an Assistant Professor at Brown University, Associate Director of Data and Statistics Core of the Alcohol Research Center on HIV (ARCH) and a faculty member of the Center for Statistical Sciences (CSS) and Center for AIDS Research (CFAR). His research expertise includes “Design of clinical trials, clinical decision making, analysis of incomplete data, sensitivity analysis, and statistical causal inference”. His collaborative research interest focuses on the area of HIV/AIDS and related diseases

Abstract:

The World Health Organization (WHO) guidelines for monitoring the effectiveness of human immunodeficiency virus (HIV) treatment in resource-limited settings are mostly based on clinical and immunological markers (e.g., CD4 cell counts). Recent research however indicates that the guidelines are inadequate and can result in high error rates. Viral load (VL) is considered the “gold standard,” yet its widespread use is limited by cost and infrastructure. In this talk, a two-step diagnostic algorithm is presented that uses information from routinely collected clinical and immunological markers to guide a selective and targeted use of VL testing for diagnosing HIV treatment failure, under the assumption that VL testing is available only at a certain portion of patient visits. The proposed algorithm identifies the patient subpopulation, such that the use of limited VL testing on them minimizes a predefined risk (e.g., misdiagnosis error rate). Diagnostic properties of our proposed algorithm are demonstrated by simulations. The method is illustrated using data from an HIV clinic in Rhode Island, and results show considerable promise for improving the effectiveness of HIV treatment monitoring in resource limited settings

Biography:

Basiru Yusuf has completed his MSc in Statistics from University of Leeds UK and currently pursuing his PhD. He is a Principal Instructor I at Statistics Department in Jigawa State Polytechnic Dutse. He has conducted many researches, published conference papers and engaged in teaching, guiding to research project and industrial visits.

Abstract:

Regression analysis of large data sets (multivariate data) such as chemometric and microarray data which has more variables than the number of observations (n<

Biography:

Adelino Martins has completed his Master degree from Hasselt University and pursuing PhD at Hasselt University. He was a Lecturer at Eduardo Mondlane University in Maputo, Mozambique.

Abstract:

Individual heterogeneity in the acquisition of infectious diseases is recognized as a key concept, which allows improved estimation of important epidemiological parameters. Frailty models allow to represent such heterogeneity. Coull (2006), introduced a computational tractable multivariate random effects model for clustered binary data. The objective of this report was to apply and modify the proposed model, and compare to the shared and correlated gamma frailty models in the context of the analysis of multivariate current status data. The models were applied to the bivariate current status data on Varicella-Zoster Virus and Parvovirus B19 using different baseline hazard functions for the force of infection. The findings revealed that the proposed model which is called in this report as new correlated gamma frailty model is closely related to existing frailty models. The main difference is the way the multivariate gamma is introduced in the model, and the indirect way to specify the baseline hazard function. In terms of construction, a frailty model is typically formulated based on the specification of the proportional hazard function, whereas the new correlated gamma frailty model is built using a classical generalized linear mixed model for clustered binary data. Furthermore, in the new model the variances of the frailties are assumed to be identical, whereas in case of the frailty model, the variances can be different or identical and the correlation is constrained by the ratio of the variances.

Biography:

Mrs Olawale holds M.Sc degree in Statistics.She currently lectures at Osun State Polytechnic, Iree Nigeria. Mrs Olawale has presented several papers both locally and internationally. Presently, she is a PhD student in the department of Statistics,University of Ilorin Nigeria.Her research area is Bio-statistics with interest in Survival Analysis

Abstract:

Data collected in South West Nigeria, which covers about 25% of Nigeria land mass typically shows wide spread of this disease across age-groups. However, the cox survival analysis of the data has shown that age is the main risk factor within each type of cancer for death while breast cancer constitute more than one quarter of its prevalence. Other noticeable cancer types include liver, rectum, blood, ovary, skin, prostrate and pancreas. The uncommon cancers include Epigastric, nasopharyngeal, gall bladder, bone and brain. This indicates that while previously common types of cancer still exist, cancers such as nasopharyngeal and lymph are now showing presence in that part of the world

Biography:

Upendra Kumar Devisetty earned his PhD from University of Nottingham, UK and completed Post-doctoral studies at the University of California Davis and Oregon State University. He is currently working as Science Informatician at CyVerse, a life sciences cyberinfrastructure funded by the National Science Foundation (NSF). He has published more than 10 papers in peer-reviewed journals and has been invited to speak at several international conferences

Abstract:

CyVerse (formerly iPlant Collaborative) is a life sciences cyberinfrastructure funded by the National Science Foundation (NSF). The infrastructure’s purpose is to scale science, domain expertise, and knowledge by providing a variety of computational tools, services, and platforms for storing, sharing, and analyzing large and diverse biological datasets. The Discovery Environment (DE) in CyVerse specifically provides a modern web interface for running powerful computing, data, and analysis applications. By providing a consistent user interface (UI) for accessing applications and computing resources needed for specialized scientific analyses, the DE facilitates data exploration and scientific discovery. DE merges the “science gateway” functionality and the bioinformatics “workbench” with high-performance data management to allow seamless access to reusable computational workflows that can run at very large scales. It is common in bioinformatics to build new analysis methods utilizing multiple programs, libraries, and modules. However, each analysis that uses these tools requires specific versions of the operating system and underlying software. Docker is a container virtualization technology that wraps a bioinformatics tool (e.g BWA) together with all its software dependencies so it can run in a reproducible manner irrespective of enviroment. This workshop will teach users how to install Docker, write a Docker file for their bioinformatic tool of interest, build the Docker image containing the tool, test the built Docker image, submit a tool request, build the new app UI in the DE and finally test their web app and share it with their collaborators or make it public so that other users can use it