Genome Analysis

0. Introduction of Sequencing, History
-Sanger sequencing (Human Genome Sequencing Project), Next-generation sequencing (Sequencing by Synthesis), Long-read sequencing (Oxford Nanopore) and Ion Torrent.

1. Basics of Linux Shell and Shell Scripting
- Different file formats, Download files from repositories.

2. Basics of Sequence Alignment
-Different ways of alignments in Proteins and DNA, Different alignment techniques, BWT (Burrows-Wheeler transform Algorithm), Different Aligners (Bowtie2) and BWA-MEM.

3. ChiP seq Analysis
-SRA toolkit usage to download files, FastQC on data, aligning files, SAM files (Different Operations on SAM/BAM Files), Filtering SAM files.
-Peak calling using MACS2, Bedfile format, Bedtools, Handling replicates (IDR), Differential peak calling (DiffBind), Basics of R.
-Visualising ChiP-seq data on IGV, BigWig file format, Functional analysis like peak annotation, and Motif analysis (using Homer).
-Problems with ChiP-seq and Newer Versions of the ChiP like Cut and Tag, Cut and Run, ChiP-exo, and Brief on data analysis.

4. RNA seq Analysis
-RNA analysis basics and Workflow
-Alignment using STAR (Difference between STAR/Hisat2 vs Bowtie2), Assessing the quality of reads
-Trimmed/Non trimmed Reads, Generating a count matrix using htseqcount or feature counts
-Different gene symbols (Converting Across Different Formats), DESeq2/edgeR (Differential RNA seq Analysis)
-Alternate non-alignment Workflow using salmon
-Nascent RNA-Seq (Like GRO-seq, mNET-seq, BrU-seq)

5. Variant Calling
-Why it is important in population studies, GWAS

6. Different Uses of NGS
-Single Cell RNA Seq
-HiC
-MetaGenomics

Summary:

Total Duration:  6 weeks

Included Materials:
-Course notes and materials
-Access to software tools
-Practical session guides

Additional Benefits:
-Certificate of completion
-Access to online forums and support groups

Advanced Bioinformatics and Molecular Modelling Course

1. Databases and Sequence Search

Introduction to Various Protein and Sequence Databases
-Overview of primary databases (NCBI, UniProt, PDB)
-Specialized databases (Pfam, PASS2)

Data Retrieval
-Methods of querying and extracting data
-Hands-on session on using database search tools (BLAST, FASTA)

2. Protein Modelling

Using Modeller and AlphaFold
-Theory and principles of protein modelling
-Step-by-step guide to using Modeller
-Introduction to AlphaFold and its applications

Structure Validation
-Techniques for validating protein models
-Tools and software for structure validation (ProSA, ProCheck)

3. Protein-Protein Interaction

Guided Docking (e.g., HDDOCK)
-Theory and principles of guided docking
-Practical session on setting up and running guided docking simulations

Blind Docking (e.g., HDOCK)
-Theory and principles of blind docking
-Practical session on setting up and running blind docking simulations

Interface Residues Analysis
-Methods for identifying and analyzing interface residues
-Tools for interface analysis (PISA, PDBePISA)

4. MD Simulation

Setting Up Simulation Runs
-Introduction to molecular dynamics simulations
-Step-by-step guide to setting up simulations (using GROMACS, AMBER)

Post-Trajectory Analysis
-Techniques for analyzing simulation results
-Tools for trajectory analysis (VMD)

5. Protein-Ligand Docking and Stability Analysis

Theory and Principles
-Basics of protein-ligand interactions
-Methods for docking and stability analysis

Practical Session
-Hands-on docking exercises using AutoDock
-Stability analysis techniques (MM-PBSA, MM-GBSA)


6. Amino Acid Network Analysis

Using PyMOL
-Introduction to PyMOL for network analysis
-Practical exercises on visualizing and analyzing protein networks

Using NetworkX
-Basics of network theory
-Practical session on using NetworkX for amino acid network analysis

Summary:

Total Duration: 4 weeks

Included Materials:
-Course notes and materials
-Access to software tools
-Practical session guides

Additional Benefits:
-Certificate of completion
-Access to online forums and support groups


Image analysis

Image Analysis using FIJI

1. Introduction to Image data
-Introduction to bit-depth, dimensions, stacks and channels

2. Introduction to FIJI
-How to visualize image data. Crop, Rotate and Merge Images.
-Define intensity-based Lookup tables for multichannel image generation

3. Intensity-based binary image generation
-Use of smooth, sharpened, and Gaussian filters to denoise images.
-Intensity-based thresholding of images is used to generate binary images.

4. Introduction to Tissue-Analyser
-Basic segmentation using binary images and advanced segmentation of cells and subcellular structures.
-Generate a database for the morphological features of segmented structures.

5. Three-dimensional rendering of image

-3D rendering to generate surface and volume of cellular and subcellular structures.
-Complex shapes and analysis of the morphological characters.

6. Introduction to spatial localization of fluorescence signals
-Colocalization: Introduction to plugins for colocalization statistics (JaCoP).
-Object-based colocalization.
-Pixel-based colocalization.

7. Introduction to time-lapse live cell imaging data
-Generating live videos and analyzing cellular and tissue-scale dynamics.

8. Introduction to FRET and FLIM data
-Conceptualization of dynamics and technique use.
-Acquisition, filtering and analysis of data.

Summary:

Total Duration: 4 weeks

Included Materials:
-Course notes and materials
-Access to software tools
-Practical session guides

Additional Benefits:
-Certificate of completion
-Access to online forums and support groups

 Python & ML



1. Introduction to Python:

-Introduction Variables: How to define them, their data types (integer, float, string) and their uses, String manipulation, Lists, dictionaries and tuples.
-For loop and While loop, If-else condition.
-Defining a function, its uses and execution, Recursion, map, and lambda functions.
-File handling: Open, read, and write in a file with basic Python packages Os, re, time, collections
-Final assignment

2. Numpy, Pandas and Plotting

-Numpy introduction, array and matrix creations, Array manipulation, and Various mathematical and logic operations using Numpy.
-Pandas introduction, series creation and manipulations, Dataframes, Concepts of Dataframe splicing, merge, concat and indexing.
-Basic plotting, introduction to matplotlib and Seborn, Dive deep into seabron: Various plots and their implications and Uses.
-Concept of sub-plotting, direct plotting using pandas and more interactive ways of creating plots.
-Final assignment

3. Exploratory Data Analysis (EDA) and Machine Learning

a) Statistics
-Distributions, mean, median, mode, standard deviations, Z-score, standard error, accuracy, recall, precision, probabilities, Hypothesis testing, odd ratios, p-value,
-Sampling strategies: Random, stratified etc.
-How do you use inferential statistics in Python?
- Assignment.

b) Data handling and curation
-Continuous or discrete Data-processing: dummy variables, avoid dummy trap, etc).

c)Introduction to machine learning (Supervised vs Unsupervised)
-Machine learning Basic: Learning Algorithms Capacity, Overfitting, Underfitting, Hyperparameters, Validation Sets, Estimators, Bias and Variance Maximum Likelihood Estimation

c)  Supervised learning
-Regression Models
-Linear regression
-multiple linear regression
-polynomial regression
-Assignment

d) Classification Models
-Support vector Machines (SVM)
-Logistic regression
-Navie bayes
-Assignment

e) Ensemble methods
-Decision trees
-Random Forest
-Assignment

f) Unsupervised Learning
-Self-organisation Maps
-Expectation maximisation,
-Gaussian Mixture models
-Principal component analysis (PCA)
-Locally linear embedding (LLE)
-Factor analysis
-Assignment


-Final assignment

Summary:

Total Duration: 8 weeks

Included Materials:
-Course notes and materials
-Practical session guides

Additional Benefits:
-Certificate of completion
-Access to online forums and support groups