On this page, you will find information on the program training topics, syllabus and ways to register. Introduction to Bioinformatics - this training program is designed for everyone, including students who don't have a background in bioinformatics, as well as life science researchers. The objective is to introduce topics and examples to help participants understand Omics data, and the use of bioinformatics in life science research. As a result of this training, you will learn about Next Generation Sequencing (NGS) data analysis. This includes processing and preparing data for analysis in application to Genomics, Transcriptomics, and Metagenomics. You will also get an overview of downstream analysis and interpretation of various types of -omics data using bioinformatics, including commonly used annotation databases, statistical analysis and machine learning techniques.
Why Next Generation Sequencing? With the decreasing cost of Next Generation Sequencing (NGS) and the increasingly broad range of applications, this technology has transformed biomedical research, the biotechnology industry, and now is becoming increasingly becoming popular in clinical use. Analysis of NGS data can help identify pathogenic, germline, and somatic DNA variants; measure gene expression; detect methylation patterns, and even study microbial communities on human skin, in the gut, lungs, and other organs. That is why this program can help anyone who is getting started with life science research and bioinformatics to understand these techniques, their applications and a broad overview of various methods to know getting started with bioinformatics.
To learn more, we welcome you to view the video below and register for a free orientation session:
Key Topics Covered:
Learn about statistical analysis for Big Data, including how to use appropriate analysis techniques to measure differences between groups of samples. See examples of advanced data analysis methods and ways to perform visualization, annotation and interpretation of analysis results.
Understanding of analytical methods for processing, visualization and analysis of complex biomedical data, Learning terminology for machine learning and artificial intelligence in biomedical discovery
Bioinformatics Project Examples: Machine Learning for Biomedical Data
Program Syllabus: Introduction to Big Data Bioinformatics
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Introduction & Orientation
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Introduction to Genomics: DNA Variants & Mutations
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Introduction to Metagenomics: 16s metagenomics sequencingSession Topics:
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Microbiome - functional bacterial communitSession Topics:
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Introduction to Transcriptomics: Gene Expression Data Analysis
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Transcriptomics in Research: Differential Gene Expression & Pathway annotation
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Introduction to Single Cell Transcriptomics
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Biomedical Data Science: Introduction to Machine Learning for NGS Data
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Overview of Machine Learning Projects
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Case Studies: Project examples and publicly available RNA-Seq datasets on NCBI, GEO, SRA, examples in oncology, infectious diseases, and agriculture
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Register for the Upcoming Webinar Session:


