Tag Archives: biotech

Cell Lines: multi-omics network of associations to model precision treatment

Modern advances in personalized medicine have used technology to characterize a patient’s fundamental biology, in terms of DNA, RNA, and protein. This can be used to classify a disease (such as breast cancer subtype) or to characterize important details of the patient’s disease(such as genes related to drug response for a particular treatment). These techniques can also be used in research for diseases such as cancer and genetic diseases.

 

The high variety of cancer mutations for each individual patient means that effective diagnosis and treatment of cancer must take into account a high degree of complexity. By sequencing individual cancer genomes, researchers and physicians may develop more targeted medical solutions. Cancer is the second major cause of mortality in the United States and targeted cancer therapies are a growing treatment type for many cancers as it brings an exponential increase in effectiveness over traditional cancer therapies.

 

Breast cancer can arise from many different types of mutations. As a result of these mutations, it  can be subdivided into a number of subtypes. Six major subtypes, previously identified and documented, are considered particularly useful for prognosis and treatment strategy. These subtypes respond differently to chemotherapy and hormone treatments. Currently, doctors only test for a handful of molecular signatures and over 40% of those patients’ cancers do not fit into one subtype. Cell lines are often used first in research for pre-clinical models, as they mirror many of the molecular characteristics of tumors, and are a less complicated model than a human.Cell lines are used to study cancer in a lab without human or animal subject involvement and are utilized to  model interactions between cell types and various drugs and therapeutics.

 

This project was inspired by Daemon et al., 2013, “Modeling precision treatment of breast cancer”, which focuses on over 70 different Breast cancer cell lines and over 90 different therapeutic agents. The project included SNP array (a type of microarray that discovers variations in the genome), RNA-seq (which looks at the whole transcriptome), exome-seq (exome capture, which looks at all of the expressed genes at a given point in time), genome-wide methylation (study of epigenetic alterations), and as well as integrating a number of algorithmic methods to identify molecular features,using advanced machine learning algorithms.

 

The TBioInfo platform has a number of advanced machine learning algorithms including,  Biassociation algorithm, which was used to integrate a number of different omics data types.This includes RNA expression, cell mutations, and drug effectiveness to find relationships and better understand how medications affect the breast cancer cells.This work was able to develop predictive drug response signatures and this research can be built upon with future clinical models. One issue with this study is a cell panel does not capture features such as tumor microenvironment, which is critical to understanding tumors.

We’re in the News! We’re excited to share a funding update and new plans in developing and commercialization of a proprietary biomedical data analysis and machine learning platform. Genomeweb, Silicon Bayou, Nola.com, The New Orleans Advocate, and other local and national publications covered the story.

Pine Biotech recently announced that it secured over a million USD in seed funding from investors this May in support of the development and commercialization of their proprietary biomedical data analysis and machine learning platform. Incorporated in the end of 2014, Pine Biotech is commercializing a biomedical data analysis platform in collaboration with the Tauber Bioinformatics Research Center and the University of Haifa.

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The platform is designed to serve unmet needs in clinical studies, academic research and education. This solution is different than other biomedical analysis software currently available. The platform enables researchers to conduct comprehensive analysis of large genomic, transcriptomic, proteomic, structural and phenotyic data using an intuitive interface. Molecular data reveals important mechanisms of actions that are best studied as a system, making this integrative approach critical for understanding and treating disease. The machine learning platform utilizes algorithms developed over years of research and trained in many academic projects.

In addition to making multi-omics analysis accessible for non-bioinformaticians, the platform includes a machine learning toolkit and interactive visualization. “Our company’s focus is on analysis of molecular data, or “omics” data, because it contains information on an unprecedented level of precision,” says CEO Elia Brodsky, “By enabling researchers and clinicians to extract real insight from omics data, we hope that new and more effective approaches to diagnostics and therapeutics will be developed.” The funding comes in support of newly secured collaborations with government agencies, academic medical centers and technology partners. “Now our team we will be able to move our work out of the research space and start addressing clinical challenges together with our partners.”

img_5781“Integration of multi-omics and clinical data will be key to implementation of precision medicine. Innovative startups like Pine partnering with academic health centers are the engine that will produce the novel algorithms necessary for this quantum leap in health care.”

Dr. Lucio Miele, Lucio Miele, M.D., Ph.D.Professor and Department Head, LSU School of Medicine, Department of Genetics Director for Inter-Institutional Programs, LSU Stanley Scott Cancer Center and Louisiana Cancer Research Consortium

 

 

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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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/

Pine Biotech’s T-BioInfo: One of theTop 5 best tools for biomedical data visualization

The completion of the Human Genome Project in 2006 proved to the world the importance and infinite potential of large medical data collection and analysis. Developments in next-generation sequencing and other advances in biotechnology generated a wealth of data — so much that it is sometimes considered a glut, with a bottleneck between data generation and meaningful analysis. A massive amount of data exists online, freely available to anyone, for either direct data mining or for combined analysis with self-generated laboratory data.

Harnessing the power of big data is the next frontier for biomedical research. Our product, T-BioInfo is cutting edge bioinformatic analytics software, capable of bringing genomic and microbial bioinformatic data to the greater public through our user-friendly interface.

The platform includes analysis tools for RNA-seq, ChIP-seq, bisulfite sequencing, de novo genome and transcriptome assembly, CirSeq, mass spectroscopy for proteomics and metabolomics, 3D biopolymer structures and similarity-based docking, unsupervised analyses (machine learning), and more. The platform streamlines the algorithmic steps needed to conduct these analyses and facilitates integration of multiple “omics” analyses. The GUI-based interface is designed so that researchers with little or no bioinformatics background can easily learn to use it.

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Check out the article from Labs Explorer here: https://www.labsexplorer.com/c/top-5-best-tools-for-biomedical-data-visualization_45