OmicsLogic Bioinformatics & Data Science Training For National Association of IDeA Principal Investigators (NAIPI)

From personalized medicine to population genomics, there are numerous applications of bioinformatics and data science. The power of big data bioinformatics analysis in improving personalized healthcare can be seen through the discovery of patient-specific biomarkers for accurate detection and diagnostics of various diseases. Nowadays, bioinformatics is even used to investigate the role of microbial populations in humans and their associations with health conditions through population metagenomics. 

As a fast-growing interdisciplinary field, bioinformatics will continue to evolve and present us with new applications and opportunities. To upskill students and researchers of NAIPI in the field of bioinformatics and data science, Pine Biotech conducted the OmicsLogic Programs on — Introduction To Bioinformatics, Genomics Data Analysis, Metagenomic Data Analysis, and Data Science For Biomedical Data.

A Brief About Pine Biotech And NAIPI Collaboration

Pine Biotech Team

Team Pine

Team Pine

 

Founded by a team of biologists, clinicians, mathematicians, and computer scientists, Pine Biotech aims to enhance human health and well-being by enabling Biological Research and Discovery with relevant data, solutions, and support. Uniting behind the vision to simplify bioinformatics, Pine Biotech in collaboration with Tauber Bioinformatics Research Center from the University of Haifa, Israel developed a comprehensive and flexible online program called OmicsLogic. The program is designed to help students, faculty, and industry advance their scientific research and participate in the future of biomedicine. 

To address the gaps in skills and awareness of data science in biomedical research, the National Association of IDeA Principal Investigators network which spans across 23 states and Puerto Rico is collaborating with Pine Biotech by launching bioinformatics and data science training programs across the universities in the United States. The collaboration that began in December 2021 will leverage existing resources with the OmicsLogic training resources that are made available to faculty and students. The training programs are also designed to elevate the level of biomedical research in underserved communities.

To learn more about Pine Biotech, what they do, and the solutions they offer, visit the link – https://pine-biotech.com/  

Outcomes of OmicsLogic Bioinformatics And Data Science Training For NAIPI 

The participants who enrolled in the mentor-guided programs consisted of undergraduates, graduates, doctoral candidates, post-doctoral researchers as well as professors from diverse backgrounds. They received access to the OmicsLogic Portal which offers a diverse range of comprehensive and up-to-date online courses on topics ranging from genomics, transcriptomics, metagenomics, epigenomics to single-cell transcriptomics. The portal also offers example projects on oncology, virology, neuroscience, agriculture, and infectious diseases that are sourced from high-impact research publications. For hands-on analytical experience and practice for bioinformatics education, learners also receive access to the AI-guided and user-friendly T-BioInfo platform that combines statistical analysis modules into bioinformatics pipelines. 

 

Now, let us take a look at the statistical outcomes of conducting the programs for students and researchers of NAIPI. 

Fig. 1: Graph depicting the number of  participants enrolled for each of the OmicsLogic Programs

Participants Enrolled

The Introduction To Bioinformatics Program was conducted from 31 Jan – 30 Apr 2022 and was designed for students and researchers who do not have a background in bioinformatics. After the completion of the program, students and researchers will gain an introductory knowledge of big data in biology; DNA variants and mutations in genomics; gene expression data analysis, differential gene expression, and pathway annotation in transcriptomics research; machine learning for NGS data analysis and their application in bioinformatics example projects. In total there were n=34 participants enrolled for the program out of which the highest participation was from LBRN (n=20) followed by NAIPI (n=14).

To know more about the program, visit the link – https://learn.omicslogic.com/programs/introduction-to-bioinformatics-spring-2022 

 

The Genomics Data Analysis Program was conducted from 02 Feb – 30 Apr 2022 and was designed for students, researchers and clinicians to learn about the applications of genomic data analysis for the improvement of translational sciences. After the completion of the course, students and researchers will gain knowledge of genome sequencing techniques and hands-on with datasets; understanding genomic variants using Integrative Genomics Viewer; NGS genomic example projects; mutability analysis, phylogenetic analysis, mutation variant analysis, and genomic example projects. The total number of participants who enrolled for the program was n=20, out of which the majority of the participants were from LBRN (n=13) than from NAIPI (n=7).

To learn more about the program, visit the link – https://learn.omicslogic.com/programs/genomics-data-analysis-spring-2022 

 

The Metagenomic Data Analysis Program was conducted from 07 Feb – 30 Apr 2022 and was designed for students, researchers and clinicians to learn about the bioinformatics applications in metagenomics. After the completion of the course, students and researchers will gain knowledge of 16s metagenomics sequencing; functional bacterial community; DADA2 microbiome variability and composition; and get in-depth hands-on experience through example projects on the topics – “American Gut and the Human Microbiome Variability” and “Microbiome, Diet and Anxiety – the Gut-brain Axis”. For the program, there were n=10 participants enrolled in which there was higher participation from NAIPI (n=9) than from LBRN (n=1). 

To know more about the program, visit the link – https://learn.omicslogic.com/programs/metagenomic-data-analysis 

 

The Data Science for Biomedical Data Program was conducted from 04 Feb – 02 May 2022 and was designed to cover practical and conceptual aspects of data science including data wrangling, statistical analysis, and machine learning in application to high-throughput biomedical omics data using big data analysis tools on the T-BioInfo Platform, R and Python. After the completion of the program, students and researchers will gain knowledge of statistics for machine learning, unsupervised and supervised machine learning analysis; feature selection and gene signature construction; generalized linear models (GLM); network analysis; and finally deep learning – types & application. The program had the highest number of participants (n=39), in which there was significant participation from NAIPI (n=26) followed by LBRN (n=13).

To learn more about the program, visit the link – https://learn.omicslogic.com/programs/data-science-for-biomedical-data-spring-2022 

 

Fig. 2: Graph depicting the number of  participants from NAIPI who completed the OmicsLogic Courses

Course Completion Graph              

 

Let us also take a look at the statistical outcomes of the courses offered on OmicsLogic portal. The course Introduction To Bioinformatics is an introductory course that covers the topics of big data bioinformatics and its uses in basic research, healthcare, and the biotech and pharmaceutical industries. As observed in Fig. 2, out of all the courses this course had the highest number of participants (n=36). The majority of the participants were from LBRN (n=20) followed by NAIPI (n=16). While the Introduction To Bioinformatics covers introductory topics on big data bioinformatics, the Bytes and Molecules is designed to introduce key concepts of molecular biology (molecules) and how they can be studied using data (bytes). This course had the second highest number of participants (n=26) out of which the number of participants from LBRN (n=15) who completed the course did not differ much from that of NAIPI (n=11).  

The course on Genomics serves as an introduction to the bioinformatics sub-discipline of genomics. While a total of n=16 participants had completed the course, there was only a marginal difference between the number of participants from LBRN (n=7) and NAIPI (n=9) who had completed the course. From basic visualization to statistical analysis of differentially expressed genes, the Transcriptomics course teaches about performing transcriptomic (RNA-Seq) data analysis. For the course, there was a total of n=15 participants for which there was only a minor difference in the number of participants from LBRN (n=9) and NAIPI (n=6) who had completed the course.  

The Metagenomics course is designed to teach about processing 16s rRNA data and visualizing the abundance tables for diversity and composition. Out of n=6 participants, there  was a higher number of participants who completed the course from NAIPI (n=5) than  from LBRN (n=1). For the course on Epigenomics, which discusses various aspects of epigenetic analysis such as Chip-Seq, Bisulfite-Seq and specialized RNA-Seq, there was very little participation from LBRN (n=1) and NAIPI (n=2). Similarly, only one participant from LBRN had completed the Single-Cell Transcriptomics course. 

The R Coding Course 1 introduces the learners to the analysis of biological data using R. Out of the total number of participants (n=18), there was only a marginal difference between the number of participants from LBRN (n=8) and NAIPI (n=10) who had completed the course. The R Coding Course 2 takes the learners one step ahead by introducing the elements of data science in R. For the course there was a total of n=26 participants, out of which a significant number of participants were from NAIPI (n=15) followed by LBRN (n=11) who had completed the course. 

For the Python Course 1 which discusses the analysis of biological data using Python, out of n=10 participants there was a major difference in the number of participants from LBRN (n=8) who had completed the course from that of NAIPI (n=2). However, the case was different for Python Course 2, where there was only a slight difference in the number of participants from LBRN (n=7) and NAIPI (n=5) who had completed the course. The Bio ML: Machine Learning For Biomedical Data course is designed to introduce various statistical and machine learning methods that can be applied to high throughput biomedical data. Here, out of n=18 participants, there was higher participation from NAIPI (n=10) followed by LBRN (n=8).  

The Designing A Bioinformatics Research Project course helps the participant identify a research topic, perform a literature review to refine it, and find a dataset for the project, thereby helping you translate your ideas or questions into an analysis plan. Out of the total number of participants (n=4) who had completed, majority of the participants were from LBRN (n=3) when compared to participants from NAIPI (n=1).

 

Fig. 3: Graph depicting the OmicsLogic Lear Points achieved by LBRN and NAIPI.

OmicsLogic LEARN PointsAs observed in Fig. 3, the majority of the participants (n=25) from LBRN and NAIPI achieved points in the range of 0-500. This was followed by the least number of participants (n=5) achieving points in the range of 500-1000. Further, we notice an increase in the number of participants (n=14) achieving points in the range of 1000-5000. The sudden increase in the number of participants achieving points was again followed by a slight decrease for the points in the range of 5000-100000 (n=11) and 10000-30000 (n=10).

Top 5 Participants From LBRN

LBRN Top 5 UsersRobert Mobley, NSF Postdoctoral Researcher in Biology, Louisiana, US achieved 26907 points on his OmicsLogic learn portal earning him the title of the participant who has achieved the highest points from LBRN. He has completed the courses on Course 1: Introduction to Bioinformatics, Course 2: Bytes and Molecules, Course 3: Genomics, Course 4: Metagenomics, Course 5: Transcriptomics, R-Coding Course 1: Getting Started with Bioinformatics, R-Coding Course 2: Introduction to Data Science (BioML), Python Course 2: Introduction to Data Science (BioML), Course 7: BioML-Machine Learning for Biomedical Data,  and Course 9: Designing a Bioinformatics Research Project. Link to his learn portal –  https://learn.omicslogic.com/user/Oy3SqRyHkYUhyXTNTPortvDDzzM2 

Camerin Kimble, Student Research Assistant, Xavier University of Louisiana NSF achieved 22827 points on the OmicsLogic learn portal thereby becoming the participant who has achieved the second-highest points. She has completed the following courses from the learn portal: Course 1: Introduction to Bioinformatics, Course 2: Bytes and Molecules, Course 3: Genomics, Course 5: Transcriptomics, and Course 7: BioML-Machine Learning for Biomedical Data. Link to her learn portal – https://learn.omicslogic.com/user/N2UG625xe9fJZ3BcZJxdPrZaubV2 

Harris McFerrin, Associate Professor of Biology, Xavier of LA, achieved the third position by earning 22646 points on his portal. He has completed the courses on Course 1: Introduction to Bioinformatics, Course 2: Bytes and Molecules, Course 3: Genomics, Course 5: Transcriptomics, R-Coding Course 1: Getting Started with Bioinformatics, R-Coding Course 2: Introduction to Data Science (BioML), Python Course 1: Getting Started with Bioinformatics, Python Course 2: Introduction to Data Science (BioML), Course 7: BioML-Machine Learning for Biomedical Data,  and Course 9: Designing a Bioinformatics Research Project. He also completed example projects on Project 01: COVID-19 Origin & Pathogenesis of SARS-COV2, Project 02: Ebolavirus: Deadly Mutations, and Project 03: TCGA Liver Cancer – Precision Oncology. Link to his learn portal – https://learn.omicslogic.com/user/puY1eHyiEKPh2yDUbuOOsoXwatx2 

Gabela Nelson, Ph.D Candidate in Biology, LSU, achieved the same points as Mr. Harris McFerrin. She has completed the courses on Course 1: Introduction to Bioinformatics, Course 2: Bytes and Molecules, Course 3: Genomics, Course 5: Transcriptomics, R-Coding Course 1: Getting Started with Bioinformatics, R-Coding Course 2: Introduction to Data Science (BioML), Python Course 2: Introduction to Data Science (BioML), Course 7: BioML-Machine Learning for Biomedical Data, and an example projects on Project 06: Patient-Derived Xenograft Models. Link to her learn portal – https://learn.omicslogic.com/user/gk2zphrXapdUjPYbb1mM0Gx2no72 

Come Thieulent, Associate Professor of Biology, Xavier of LA achieved 22456 points by completing the following courses and projects: Course 1: Introduction to Bioinformatics, Course 2: Bytes and Molecules, Course 3: Genomics, Course 5: Transcriptomics, Python Course 1: Getting Started with Bioinformatics, Python Course 2: Introduction to Data Science (BioML), R-Coding Course 1: Getting Started with Bioinformatics, R-Coding Course 2: Introduction to Data Science (BioML), Course 7: BioML-Machine Learning for Biomedical Data, Course 9: Designing a Bioinformatics Research Project, Project 01: COVID-19 Origin & Pathogenesis of SARS-COV2, Project 02: Ebolavirus: Deadly Mutations, Project 03: TCGA Liver Cancer – Precision Oncology, Project 04: EV-D68 and Acute flaccid myelitis in Kids, Project 05: Modeling Cancer Precision Medicine and Project 06: Patient-Derived Xenograft Models. Link to his learn portal – https://learn.omicslogic.com/user/jclHqg8u2pYNGOAPs5JZpvUWP9d2 

Top 5 Participants From NAIPI

NAIPI Top 5 UsersRafael Aniu Peres, Research Laboratory Associate, University of Hawaii achieved 11894 points on his OmicsLogic learn portal earning him the title of the participant who has achieved the highest points from NAIPI. He has completed the courses on Course 1: Introduction to Bioinformatics, Course 2: Bytes and Molecules, Course 3: Genomics, Course 4: Metagenomics, R-Coding Course 1: Getting Started with Bioinformatics, R-Coding Course 2: Introduction to Data Science (BioML) and Python Course 1: Getting Started with Bioinformatics. Link to his learn portal – https://learn.omicslogic.com/user/Su7UXVIma6NS8smtorLM1DIp6W13  

Krit Phankitnirundorn, Computational Biologist, University of Hawaii achieved 22827 points on the OmicsLogic learn portal thereby becoming the participant who has achieved the second-highest points. The following courses from the learn portal were completed: Course 1: Introduction to Bioinformatics, Course 2: Bytes and Molecules, Course 5: Transcriptomics, R-Coding Course 1: Getting Started with Bioinformatics, R-Coding Course 2: Introduction to Data Science (BioML), Course 7: BioML-Machine Learning for Biomedical Data, Python Course 2: Introduction to Data Science (BioML) and Course 9: Designing a Bioinformatics Research Project. Link to the learn portal – https://learn.omicslogic.com/user/sCO9fKLyjkZBroau5idbxXnhyS02 

Eric Peeples, Associate Professor of Pediatrics at UNMC achieved the third position by earning 9457 points on his portal. He has completed the courses on Course 1: Introduction to Bioinformatics, Course 2: Bytes and Molecules, Course 3: Genomics, R-Coding Course 2: Introduction to Data Science (BioML) and Course 7: BioML-Machine Learning for Biomedical Data. Link to his learn portal – https://learn.omicslogic.com/user/HNwLxfRv44X8Lfxak1nVScMaG7y1 

Nina P. Allan, Ph.D Student, the University of Hawaii, achieved 8278 points thereby gaining the fourth position. She has completed the courses on Course 1: Introduction to Bioinformatics, Course 3: Genomics, Course 4: Metagenomics, and Course 8: Epigenomics. Link to her learn portal – https://learn.omicslogic.com/user/PEaForUaN6YTWpdLwVklnIKt8n92  

Noelle Rubas, Graduate Student, University of Hawaii achieved 7524 points by completing the following courses and projects: Course 2: Bytes and Molecules, Course 5: Transcriptomics, Course 8: Epigenomics, R-Coding Course 1: Getting Started with Bioinformatics, R-Coding Course 2: Introduction to Data Science (BioML), Python Course 2: Introduction to Data Science (BioML), Course 7: BioML-Machine Learning for Biomedical Data, Course 9: Designing a Bioinformatics Research Project and Project 06: Patient Derived Xenograft Models. Link to her learn portal – https://learn.omicslogic.com/user/hzxaa39CktggfT9aA521l1gMgLz2 

 

To learn more about the OmicsLogic programs on bioinformatics and data science, visit the link – https://learn.omicslogic.com/programs 

 

If you have any queries, write to us at marketing@pine.bio. Happy learning!

 

Link to the above-mentioned bioinformatics and data science courses -;

  1. Course 1: Introduction to Bioinformatics – https://learn.omicslogic.com/courses/course/course-1-introduction-to-bioinformatics 
  2. Course 2: Bytes and Molecules – https://learn.omicslogic.com/courses/course/course-2-bytes-and-molecules 
  3. Course 3: Genomics – https://learn.omicslogic.com/courses/course/course-3-genomics 
  4. Course 4: Metagenomics – https://learn.omicslogic.com/courses/course/course-4-metagenomics 
  5. Course 5: Transcriptomics – https://learn.omicslogic.com/courses/course/course-5-transcriptomics 
  6. Course 6: Single Cell Transcriptomics – https://learn.omicslogic.com/courses/course/course-6-single-cell-transcriptomics 
  7. Course 7: BioML-Machine Learning for Biomedical Data – https://learn.omicslogic.com/courses/course/course-7-bioml-machine-learning-for-biomedical-data 
  8. Course 8: Epigenomics – https://learn.omicslogic.com/courses/course/course-8-epigenomics 
  9. Course 9: Designing a Bioinformatics Research Project – https://learn.omicslogic.com/courses/course/course-9-designing-a-bioinformatics-research-project 
  10. R-Coding Course 1: Getting Started with Bioinformatics – https://learn.omicslogic.com/courses/course/r-coding-course-1-getting-started-with-bioinformatics 
  11. R-Coding Course 2: Introduction to Data Science (BioML) – https://learn.omicslogic.com/courses/course/r-coding-course-2-introduction-to-data-science-bioml 
  12. Python Course 1: Getting Started with Bioinformatics – https://learn.omicslogic.com/courses/course/python-course-1-getting-started-with-bioinformatics  
  13. Python Course 2: Introduction to Data Science (BioML) – https://learn.omicslogic.com/courses/course/python-course-2-introduction-to-data-science-bioml 

 

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