Lyden possesses an extensive background in biostatistical analysis in pharmaceutical, biotechnical and medical device clinical research. Prior to founding BioStat International, Inc., Lyden served as the Manager of Clinical and Statistical Affairs at Bausch and Lomb Pharmaceutical Division.

“It can be challenging to get young people interested in biostatistics.” said Lyden. “Biostatistics can be a very rewarding career and it is the goal of this panel to impart students contemplating a future in biostatistics with career possibilities, industry challenges and rewards that exist in our field.”

The discussion panel occurred on the first day of the SIBS six-week learning program. Undergraduate students from across the nation interested in pursuing a graduate program in Biostatistics enroll in the program to learn more about graduate studies and gain insight from biostatistics experts. Participants have access to the university’s computing systems and libraries and will also receive hands-on training from top biostaticians, clinicians and epidemiologists.

Following the discussion panel, the students were invited to a reception with the SIBS staff to kick off the six-week program. Upon completion of the program, students may transfer 3 college credits to their home institution.

The tranSMART Project has released the first open source version of the award winning tranSMART software to the translational research community. tranSMART General Public License (GPL) 1.0 RC1 is now broadly available to institutions seeking an open framework for integrating and sharing data across medical records, clinical trials, reference content, and ‘omics data. The software is a product of multiple collaborations among non-profits, academic medical centers, pharmaceutical companies, and commercial groups. In addition to the release, the tranSMART Project team has established an online framework for open source developers and scientists actively contributing from multiple sites through a central GitHub repository, wiki, and organizational governance board. Visit www.transmartproject.org for more information.

The tranSMART Project is led by a board of informatics leaders from non-profit organizations focusing on pre-competitive strategies for drug discovery. This includes Sage Bionetworks (Mike Kellen), CHDI Foundation (Keith Elliston) and three major pharmaceutical organizations including Janssen Research & Development, LLC. Each group has contributed software extensions and expertise towards achieving the GPL release. In addition to the leadership team, more than eight sites have installed and are piloting pre-release versions of the software with their own data sets.

Stephen Friend, President of Sage Bionetworks, said: “We are very excited to see tranSMART enter the open source community and support pre-competitive sharing of software and scientific data in a model that will lead to the type of breakthroughs that Sage was founded to facilitate.”

Key academic medical centers leading the project include University of Michigan and Imperial College London. Imperial College London, led by Yike Guo, professor in computing science in the Department of Computing, has been applying tranSMART software to deliver knowledge management and data sharing systems for IMI research projects such as UBIOPRED. The University of Michigan NCIBI is working to provide a US academic home for the tranSMART software including multiple internal deployments and leading a work stream for API development and integration of NCIBI tools.

“The tranSMART biomedical knowledge management platform has a model that scales across groups to facilitate productive public/private partnerships,” said Brian Athey, PhD, Professor and Chair, Department of Computational Medicine and Bioinformatics, Michigan Institute for Clinical Health Research. “We are working with the tranSMART Project to take it to the next level to achieve the full potential of the vision.”

The tranSMART software was internally developed as a translational research data repository for Janssen R&D. The primary features for data analysis were produced as an extension of the open source i2b2 software (informatics from the bench to the bedside) with specific adaptations and extensions to provide researchers with workflows in drug discovery and pharmaceutical R&D. In December 2011, Janssen R&D decided to transition the software into open source under GNU General Public License v3.0. The first full release of tranSMART 1.0 is planned for July 2012 with the current GPL release providing a full beta preview of features and tools to be delivered in the final release. The tranSMART 1.0 release includes a major update to the framework, including an upgrade to leverage i2b2 1.6, a plug-in environment for R based analytics, new correlation analysis algorithms, and new multi-modal data export utilities.

Recombinant Data Corp., an original developer of the software, worked closely with early adopters to integrate the software from multiple partnering sites and has supported most deployments to date. Recombinant Data continues to offer commercial support to sites seeking integrated delivery of the software. “We believe that open source is an ideal way to produce and share scientific software tools and we are excited to see work that we have done become rapidly adopted by the scientific community,” said Dan Housman, CTO Recombinant Data.

“Our analysis raises questions about the best methods for generating evidence, as well as the capacity of the clinical trials enterprise to supply sufficient amounts of high quality evidence to ensure confidence in guideline recommendations,” said Robert Califf, MD, first author of the paper, vice chancellor for clinical research at Duke University Medical Center, and director of the Duke Translational Medicine Institute.

The analysis was conducted by the Clinical Trials Transformation Initiative (CTTI), a public-private partnership founded by the Food and Drug Administration (FDA) and Duke. It extends the usability of the data in ClinicalTrials.gov for research by placing the data through September 27, 2010 into a database structured to facilitate aggregate analysis. This publically accessible database facilitates the assessment of the clinical trials enterprise in a more comprehensive manner than ever before and enables the identification of trends by study type, by therapeutic areas, by type of sponsor, by number of participants and by many other parameters.

“Since 2007, the Food and Drug Administration Amendment Act has required registration of clinical trials, and the expanded scope and rigor of trial registration policies internationally is producing more complete data from around the world,” stated Deborah Zarin, MD, director, ClinicalTrials.gov, and assistant director for clinical research projects, NLM. “We have amassed over 120,000 registered clinical trials. This rich repository of data has a lot to say about the national and international research portfolio.”

This CTTI project was a collaborative effort by informaticians, statisticians, and project managers from NLM, FDA and Duke. CTTI comprises more than 60 member organizations with the goal of identifying practices that will improve the quality and efficiency of clinical trials.

“Since the ClinicalTrials.gov registry contains studies sponsored by multiple entities, including government, industry, foundations and universities, CTTI leaders recognized that it might be a valuable source for benchmarking the state of the clinical trials enterprise,” stated Judith Kramer, MD, executive director of CTTI.

“Analysis of the entire portfolio will enable the many entities in the clinical trials enterprise to examine their practices in comparison with others,” says Califf. “For example, 96 percent of clinical trials have ≤1000 participants, and 62 percent have ≤ 100. While there are many excellent small clinical trials, these studies will not be able to inform patients, doctors, and consumers about the choices they must make to prevent and treat disease.”

The analysis showed heterogeneity in median trial size, with cardiovascular trials tending to be twice as large as those in oncology and trials in mental health falling in the middle. It also showed major differences in the use of randomization, blinding, and data monitoring committees, critical issues often used to judge the quality of evidence for medical decisions in clinical practice guidelines and systematic overviews.

“These results reinforce the importance of exploration, analysis and inspection of our clinical trials enterprise,” said Rachel Behrman Sherman, MD, associate director for the Office of Medical Policy at the FDA’s Center for Drug Evaluation and Research. “Generation of this evidence will contribute to our understanding of the number of studies in different phases of research, the therapeutic areas, and ways we can improve data collection about clinical trials, eventually improving the quality of clinical trials.”

An analysis-ready copy of the ClinicalTrials.gov database is now available at www.ctti-clinicaltrials.org. Specialists from numerous therapeutic areas are now scrutinizing the contents to better understand how the number and characteristics of clinical trials match the perceived needs of the research communities.

BaseSpace Apps will include a publicly available API (application programming interface) that allows developers to create and deploy new applications for the analysis of genetic data generated on Illumina systems. Diagnomics, GenoLogics Life Sciences, Genomatix, Golden Helix, Ingenuity Systems, Knome, Omicia, Spiral Genetics, Omixon, Real Time Genomics, Station X, Integromics Inc., Biomax Informatics AG, and Strand Life Sciences were named as initial application development partners.

“The rapid adoption of BaseSpace coupled with BaseSpace Apps will help us achieve our goal to create an ecosystem where users of Illumina next generation sequencers can easily access a broad range of genome analysis tools from the world’s leading bioinformatics vendors,” said Alex Dickinson, Illumina’s Senior Vice President, Cloud Genomics. “By providing an open API and collaborative environment, we can encourage more rapid proliferation of the tools that will enable scientists to analyze, understand and make use of massive amounts of genetic data.”

Illumina also introduced its iSAAC genome alignment tool today. Historically, alignment has been the most time consuming and processor-intensive step in genome analysis. Available on BaseSpace as well as standard workstations, iSAAC maps sequencing reads to their proper location up to 10 times faster than existing aligners, significantly expediting and simplifying a critical component in data analysis.

Through BaseSpace Apps, a diverse array of new data analysis applications and programs such as iSAAC will be available as part of a growing toolset within the BaseSpace cloud for MiSeq(R) and HiSeq(R) systems. Collectively, the tools will provide a wide range of functionality, from workflow management and downstream data analysis, to data visualization and biological interpretation.

BaseSpace is a scalable cloud-computing environment for all of Illumina’s sequencing systems that can be accessed securely from anywhere in the world. MiSeq system data already can be seamlessly transferred to BaseSpace for storage, analysis and sharing between researchers and their peers around the world, all in a secure and user-friendly environment. HiSeq data storage and analysis capabilities will be commercially released later this year.

With the rapid development of high-throughput sequencing technology over the past ten years, the cost of DNA sequencing is decreasing much faster than data processing. Given that such research creates huge amounts of data, cloud computing is becoming a favorable solution for large-scale bioinformatic analysis, both in terms of resource utilization, flexibility, and efficiency, as well as time and cost savings for massive data generation and computation.

“To meet the increasing demand for bioinformatics analysis, BGI has developed a high performance platform for researchers, comprising the distributed cloud computing pipelines and tools.” said Ye Yin, Director of Research and Cooperation Division at BGI, “From the beginning of this year, BGI has conducted many testing and adjustment on the system in several different centers. I’m so excited that BGI-BOX has been successfully launched as scheduled. With this server, researchers could conduct the bioinformatics analysis with fast turnaround time and lower cost.”

The system of BGI-BOX contains a series of BGI’s standard bioinformatics analysis pipelines and software, including assembly tools, genetic variation analysis software, among others. There are two ways for remote users to easily access BGI’s cluster computing and storage resources: one is logging on to BGI-BOX directly and locally, the other is logging on to BGI’s computing centers by remote access. With this cloud computing terminal server, users not only can perform basic bioinformatics analysis based on the genomic data, but also can conduct customized analysis on a variety of biological data sets by choosing relevant software and adjusting specific parameters under each analysis module.

“The debut performance of BGI-BOX is satisfactory. Many of our collaborators have expressed their willingness to install BGI-BOX in their labs in order to share and exchange data in a cloud environment.” said Yin Ye. “In the first quarter of next year, we plan to install 50 BGI-BOXs worldwide to build a powerful and efficient cloud platform and support our partners with convenient access of bioinformatics analysis at hand.” He added.

Nekrutenko said that he and his team first developed the Galaxy computing system in 2005 because “biology is in a state of shock. Biochemistry and biology labs generate mountains of data, and then scientists wonder, ‘What do we do now? How do we analyze all these data?’” Galaxy, which was developed at Penn State and continues to use the University’s servers for its computing power, solves many of the problems that researchers encounter by pulling together a variety of tools that allow for easy retrieval and analysis of large amounts of data, simplifying the process of genomic analysis. As described in one of the team’s early papers in the journal Genome Research, Galaxy “combines the power of existing genome-annotation databases with a simple Web portal to enable users to search remote resources, combine data from independent queries, and visualize the results.” Galaxy also allows other researchers to be able to review the steps that have been taken, for example, in the analysis of a string of genetic code. “Galaxy offers scientific transparency — the option of creating a public report of analyses. So, after a paper has been published, scientists in other labs can do studies in order to reproduce the results described,” Nekrutenko said.

Now, Nekrutenko’s team has taken Galaxy to the next level by developing an “in the cloud” option using, for example, the popular Amazon Web Services cloud. “A cloud is basically a network of powerful computers that can be accessed remotely without the need to worry about heating, cooling, and system administration. Such a system allows users, no matter where they are in the world, to shift the workload of software storage, data storage, and hardware infrastructure to this remote location of networked computers,” Nekrutenko explained. “Rather than run Galaxy on one’s own computer or use Penn State’s servers to access Galaxy, now a researcher can harness the power of the cloud, which allows almost unlimited computing power.” As a case study, the authors report on recent research published in Genome Biology in which scientists, with the help of Ian Paul, a professor of pediatrics at Penn State’s Hershey Medical Center, analyzed DNA from nine individuals across three families using Galaxy Cloud. Thanks to the enormous computing power of the platform, the researchers were able to identify four heteroplasmic sites — variations in mitochondria, the part of the genome passed exclusively from mother to child.

“Galaxy Cloud offers many advantages other than the obvious ones, such as computing power for large amounts of data and the ability for a scientist without much computer training to use DNA-analysis tools that might not otherwise be accessible,” Nekrutenko said. “For example, researchers need not invest in expensive computer infrastructure to be able to perform data-intensive, sophisticated scientific analyses.”

Yet another advantage of Galaxy Cloud is its data-storage capacity. Using the Amazon Web Services cloud, researchers have the option of storing vast amounts of data in a secure location. “There are emerging technologies that will produce 100 times more data than existing ‘next-generation’ DNA sequencing, which already has reached the point where even more storage becomes an issue, not to mention analysis,” Nekrutenko said.

Perera and his team of biologists operate a state-of-the-art genome sequencing laboratory to compare patterns of microRNAs and their target gene expression in melanoma cells and normal melanocytes in hopes of finding differences that could be targeted for new diagnostics or therapeutics.

Their research found that melanocyte growth and the cancer’s ability to invade other tissue is partially controlled by abnormal expression of microRNAs. “We’ve identified two specific microRNAs, called miR-375 and miR-34b, which could be used not only as novel diagnostic markers for early melanoma detection, but may also serve as therapeutic targets,” explained Perera.

Because the team looked at so many different cells and patient samples, they generated a lot of data through next-generation sequencing technology that needed to be annotated and interpreted. Perera set out to find someone with the expertise and computing power to help him analyze the data. That’s when he met Shaojie Zhang, Ph.D., an assistant professor of engineering and computer science at UCF, whose expertise is in bioinformatics. Dr. Zhang and his graduate students analyzed the data using a computer program to identify differences in gene expression and methylation patterns between healthy and diseased cells.

“The collaboration between genomics and bioinformatics, two of the most advanced technologies in biomedical research, empowered this important research. These discoveries offer the possibility that the microRNAs we identified could be used as biomarkers to assist in earlier diagnoses of this fatal cancer,” Perera said.

For Zhang, the partnership has also meant an opportunity to give his graduate students real-world experience. “The collaboration provides an interdisciplinary education opportunity to the graduate students from the College of Engineering and Computer Science who are interested in applying their computational skills to biomedical research,” Zhang said. “The partnership not only helps advance science, but it also helps train future scientists. I’m very pleased to be a part of it.”

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