Tag Archives: research

Free training in Bioinformatic analysis available with Pine Biotech’s T-BioInfo

Data analytics skills are in high demand in clinical research and treatment development, though most bioinformatics education courses focus on technical issues rather than the bigger picture of big data and its potential to change the way we view disease and treatment discovery. Learning how to run bioinformatic analysis is costly with the high prices of courses through a University or online education program – not to mention the countless hours which must be devoted to learning coding languages, scripts, and downloading software packages. The result is an approach to bioinformatics which ignores biology.

 

Our online educational modules are different – applying our algorithms and visualization tools to real publically available molecular data. Through our user-friendly visual interface cloud platform, T-BioInfo, we bypass the technical investment of education in bioinformatics. The T-bioInfo platform simplifies the computational approach to allow scientists from all backgrounds to move forward in the world of big data. “Analyzing large datasets can be a challenge for molecular biologists.” Said Dr. Christian Pfaller of the Cattaneo Lab in the Mayo Clinic. “T-Bio provides a comprehensive platform with a user-friendly graphical interface that allows a wide range of NGS algorithms.”

 

The online courses developed by Pine Biotech are centered on eliminating the disconnect between data gathering and data analysis by training professionals and students alike in bioinformatic analysis using the T-Bio Info platform. Projects crafted using publically available data contain detailed biological data, broken down into digestible, easy-to-manipulate visualizations, offering scientists and students alike a new means of working with ‘omics data – without expensive the technology and coursework. In a workshop conducted with University students, the students were able to complete our course modules, working with real scientific datasets – and passed content knowledge quizzes in a two hour session. Programs range from a basic introduction to bioinformatics, to in-depth project guides which lead the user through analysis.

 

With the abundance of data available, it is up to data scientists to derive the value of the information generated daily. The world of big data is constantly evolving, and scientists of all backgrounds need to find ways to integrate the value of the data within their own work in order to keep up with the deluge of new and backlogged information. We seek to streamline skill development in bioinformatics. Our approach focuses on the understanding of biological processes and molecular factors – without introducing complex computer science, empowering non-bioinformatician biologists to take full advantage of the endless data at their fingertips.

 

With the ability to understand large medical data, the possibilities for research and discovery are endless!

 

Our online education system is extensive, with full online courses in development for each of the analysis modalities.

 

Register for free at http://edu.t-bio.info/lp-courses/ to explore course modules:

 

  • Introduction to Biomedical Data Analysis
  • Transcriptomics: NGS Expression Profiling
  • Genomics: Mutation Variant Analysis
  • Microbiome: Microbial Diversity
  • Epigenetics: ChIP-Seq and WBGS profiling
  • Machine Learning: Understand your data

 

Each section contains step-by-step explanations from both data and biological perspectives to develop analysis logic that the user can take with them!

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A Personalized, Precise Approach to Fighting Cancer

Cancer is the second major cause of mortality in the United States and targeted cancer therapies are bringing about an exponential increase in effectiveness over traditional cancer therapies. Traditional cancer treatment, such as chemotherapy, has come a long way in the past five decades, and care can be delivered comfortably, in an outpatient setting with manageable side-effects.

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Cancer is an important challenge for which personalized molecular medicine shows great promise. Recent advances in immunotherapy and genetic testing have been proposed to help transform care from one-size-fits-all to a highly specialized range of options that could be adapted to fit an individual’s molecular features. However, we are still far from understanding and navigating cancer.

 

The variety of cancer mutations means that effective diagnosis and treatment of cancer must take an individual approach. By sequencing individual cancer genomes, researchers and physicians may develop more effective treatment solutions. A multi-omics approach based on big data analyses could lead to substantial advances in cancer treatment, ushering in an exciting new paradigm in cancer treatment.

 

Accessibility of new technologies such as next-generation sequencing remains a barrier for many patients. The skills needed, and associated costs of  technology have prevented physicians and patients from using precision oncology to its full potential so far. The skills and equipment required to collect and analyze genomic data can be expensive. And generally, Bioinformaticians have to go through rigorous training in linux, python, R, databases, visualization and an endless list of new algorithms to even understand the complex datasets.

 

To encourage more researchers to make use of the potential and power afforded by big data, we designed a free series of lessons which simplify the computational aspects of analysis to allow scientists from all backgrounds to move forward in the world of big data. The cancer series has practical, hands-on projects that allow students to practice analyses with data adapted from real datasets.

 

 

The course will cover some of the important aspects of breast cancer:

  1. molecular indicators of cancer: deregulation of cellular checks and balances, uncontrollable growth, alternate signaling, altered immune responses, etc.
  2. Factors that contribute to cancer heterogeneity – “levels of biological regulation”
  3. Response to treatment studied with cancer cell lines.
  4. Beyond cell lines – PDX models: the role of microenvironment and the use of animals in research.
  5. TCGA data – real patients: miRNA-seq, RNA-seq, Exome-seq and clinical data.
  6. Deeper look into clinical data: combinations of treatments, many ways to diagnose cancer, why molecular data is critical.
  7. Future of Cancer: New treatments and findings that could change current cancer treatments.

Exponential growth of data in biomedicine needs a skilled and experienced workforce

A wealth of public domain biomedical data is available to anyone online, however, data analysis technology can only be utilized by those trained to work with it, leaving researchers without the proper skills and tools drowning in the overwhelming amount of information. It might sound intimidating, but in reality, this is GOOD NEWS!

Data science is a fast-growing branch of the technology landscape, and with IT and analytic skills highly sought after by employers, data scientists pull in hefty salaries for their expertise. There is an urgent need for quick, cost-effective, and accurate data analysis in the field of biomedical research as well. This need is driven by the continually expanding wealth of patient data, coupled with the need to synthesize and understand the data’s importance in medicine.  One of the most exciting areas where new discoveries are being made all the time is in the molecular data field. The datasets there are huge, but every new project brings us closer to understanding complex diseases, evolution and human longevity.

Today, biomedical data is typically analyzed by a trained bioinformatician – a tech-era mash-up of “biologist” and “statistician” who breaks down and interprets large sets of medical data in a clinical or research setting. But for the most part, bioinformaticians have to go through rigorous training in linux, python, R, databases, visualization and an endless list of new algorithms that are complex and require deep understanding to use. Just to get to a meaningful project might take years depending on how well you are versed in all of these technologies. All of these skills essentially turn you into a computer scientist first and then place you under a biologist’s or clinicians’ authority to guide “real research”.

But more and more biologists, clinicians and even patients want to play a role in these discoveries. At some point, you had to know Fortran to use a computer – that is until a mac came out or Windows with their visual interfaces making it a household item. And then kids could play with the infinite scripts (games) that leveraged this complex technology. Well, that’s exactly what we would like to do – let’s make bioinformatics easy, visual and intuitive. We think we can do it together.

To start, we’ve put together a number of projects that one can analyze using our visual bioinformatics platform. Here are a few examples:

“PDX Models: Tumor-stroma interaction” inspired by the publication, Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumors and stromal specific biomarkers by Dr. James Bradford. The project’s approach focused on comparing several different breast cancer types using RNA-seq and machine learning methods including 79 PDX mouse models with human primary tumors. PDX models maintain more similarities to the parental tumors than a traditional cell line does. Subsequently, alternative analysis of experimental data provided deeper insight into the problem and identified new biologically meaningful group-wise associations between tumor and stroma genes.

“Cell Lines: multi-omics network of associations” rests on the publication of Expression Profiling of Macrophages Reveals Multiple Populations with Distinct Biological Roles in an Immunocompetent Orthotopic Model of Lung Cancer. The approach focused on the molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. This project included both RNA-seq, mutations, and IC50 drug values. During analysis, biassociation was utilized with the expression and mutation variant results as well as drugs (GI50) to find relationships between the datasets.

Explore these datasets online, and learn more about our bioinformatics platform T-BioInfo, at http://edu.t-bio.info/projects/