Bioinformatics is a discipline that combines biology, statistics, and computer science. Data analysis skills are essential for modern biology and are at the core of scientific discovery. In this bioinformatics training program, we will explore bioinformatics concepts in a project setting, offering insights into the typical problems addressed by a bioinformatician and explaining the analysis logic. The program does not require any prerequisites, apart from basic biology concepts. The program will span for three months, comprising eleven workshops and one Team Project.
Introduction and commencement of the program by VC, SNU Kolkata Introduction to the Faculty & Trainers Overview of the tools and resources for this...
Introduction to multi-omics (Definitions), Knowledge-Driven vs. Data-Driven Discovery and Examples, Applications of Bioinformatics in Agriculture, Neuroscience, Cancer Biology, Immunology, Defense and Healthcare
Cell, Nucleus and Chromosomes The DNA molecule and its structure Genome variations: A detailed understanding Targeted Sequencing, Whole Exome Sequencing and Whole Genome Sequencing...
Processing Raw NGS Data (read mapping and quantification) Analysis (differential gene expression, factor regression analysis) Interpretation (Gene Ontologies and Gene Set Enrichment Analysis)
Filtering, removing noise and Normalization Techniques, Exploring multi-dimensional data using PCA visualization, Principal Components and variance – outliers, filtering, normalization
Role of Genomics and Transcriptomics in Modern Biology.
What is Metagenomics and why study Metagenomics Microbiome and taxonomy The Gut Microbiota and Human Health NGS of the Metagenome and 16S metagenomic data...
The current research areas in Metagenomics, The biological background of the Metagenomics, Logical Steps for Metagenomics Data Analysis and associated Algorithms, Interpretation of the results and visualization
What is machine learning, categories of methods Regression, factors, and features – Factor Regression Analysis Challenges associated with different kinds of machine learning
Decision Trees, Discriminant Analysis and Support Vector Machines, Feature Selection and expanding the list of features, Data Visualization
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