Sister Nivedita University Bioinformatics Training Program
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.
Upcoming Events
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20August
Introduction to OmicsLogic Program
Introduction and commencement of the program by VC, SNU Kolkata Introduction to the Faculty & Trainers Overview of the tools and resources for this...
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22August
Introduction to Bioinformatics
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
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27August
Genomics
Cell, Nucleus and Chromosomes The DNA molecule and its structure Genome variations: A detailed understanding Targeted Sequencing, Whole Exome Sequencing and Whole Genome Sequencing...
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29August
Transcriptomics
Processing Raw NGS Data (read mapping and quantification) Analysis (differential gene expression, factor regression analysis) Interpretation (Gene Ontologies and Gene Set Enrichment Analysis)
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05September
Transcriptomics 2
Filtering, removing noise and Normalization Techniques, Exploring multi-dimensional data using PCA visualization, Principal Components and variance – outliers, filtering, normalization
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10September
Workshop II
Role of Genomics and Transcriptomics in Modern Biology.
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17September
Metagenomics
What is Metagenomics and why study Metagenomics Microbiome and taxonomy The Gut Microbiota and Human Health NGS of the Metagenome and 16S metagenomic data...
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24September
Metagenomics 1
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
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17October
Machine Learning
What is machine learning, categories of methods Regression, factors, and features – Factor Regression Analysis Challenges associated with different kinds of machine learning
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22October
Machine Learning 1
Decision Trees, Discriminant Analysis and Support Vector Machines, Feature Selection and expanding the list of features, Data Visualization
This is a private organization. To join you must be a registered site member and request organization membership.