Module : 1 = Navigating the Bioinformatics Profession: An Orientation
- Introduction to Bioinformatics and its Applications in Life Sciences
- Career Pathways and Opportunities in Bioinformatics
- The Interdisciplinary Nature of Bioinformatics: Bridging Biology and Computer Science
- Organizational Structure and Employment Benefits in the Life Sciences Industry
- Regulatory Framework and Compliance in Bioinformatics
- The Role of Bioinformatics Scientists in Advancing Life Sciences Research
- Essential Skills and Competencies for Bioinformatics Professionals
- Ethical Considerations and Responsibilities in Bioinformatics Practice
- Bioinformatics Tools and Technologies: A Landscape Overview
- Emerging Trends and Future Directions in Bioinformatics Research and Industry
Module : 2 = Introduction to Bioinformatics
- Importance of bioinformatics in modern biology
- Detailed explanation for central dogma of cell : Replication,Transcription,Translation, study about DNA,RNA,Protein ,Strand
- Detailed study about structure of gene, transcript,upstream,downstream regions,CDS,UTR
- Detailed study about protein primary/secondary and tertiary structure
- Detailed study about enzymes,Bonds and Interactions
- Basics of nucleotide and protein sequence, and FASTA format,fastq,SAM/BAM
- Pairwise sequence alignment techniques (local, global)and Introduction to multiple sequence alignment
- Introduction to genomics and Proteomics in Bioinformatics
- What is NGS ? Genome assembly and sequencing techniques (e.g., Sanger sequencing, Next-Generation Sequencing)
- Different applications of NGS( ex. DNAseq, RNAseq, CHIPseq, metagenomics, Methlyseq etc.)
Module : 3 = Introduction to Bioinformatics Databases
- Understanding Data Sources in Bioinformatics: Open Source vs. Paid
- Utilizing Tools for Data Import from Public and Private Databases
- Overview of Bioinformatics Databases: Types and Categories
- Introduction to Types of Databases: primary/secondary/data structure/types of data etc..
- Navigating Genomic Databases: GenBank Database
- Protein Databases: Structure, Function, and Interaction Databases : PDB ,UniProt Database
- Data Retrieval Techniques: Querying Databases Using Keywords, IDs: UCSC Database
- Literature Database: PubMed Database
- ClinVar Database
- Integrated Databases: Resources Combining Multiple Data Types (e.g., KEGG, Reactome)
- Ensemble Database
Module : 4 = Bioinformatics tools
- Introduction to Sequence Alignment
- Types of Alignment(Pairwise & Multiple)
- Local & Global Alignment
- Online Blast
- Standalone BLAST
- MEGA
- ClustalW
- Visualization tools Pymol / Jmol(optional)
Module : 5 = Introduction to LINUX
- Overview of Linux
- Package Management
- Basic Commands for file handling
- Advanced Linux commands
- Introduction to Bash Scripting
Module : 6 = Data Analysis with R Programming
- Getting Ready with R introduction and installation
- “Data Types, Variables, and Basic R Operations”
- Function-built-in and User defined
- Conditional statements
- “Data Wrangling and Cleaning : Importing data into R(e.g., FASTA, GenBank)”
- Package installation from CRAN repository and Bioconductor
- Data manipulation with dplyr for biological datasets
- Working with Strings: Sequence Analysis with seqinr and biostring
- “Statistical Test-t-test ,z-test ,chiSquare and ANOVA”
- Data Visualization
Module : 7 = Introduction to Python language
- Introduction to Python language
- Data types and data structure
- “Control statements: if -else, If-elif-else, for loop, while loop”
- “Python data structure : List, Set, Tuple, Dictionary”
- “Methods of List, Slicing and indexing in List and Tuple”
- “Functions : Function introduction and its requirement, Defining a function, Calling a function”
- “File handling :file handling, OS module”
- “Pandas library: Reading different file formats such as csv, tsv and excel files”
- Biopython
- SeqIO and visualization
Module : 8 = Data Structures and Algorithm
- Introduction to Data Structures and Algorithms in Bioinformatics
- “Fundamentals of Data Structures: Arrays, Linked Lists, Stacks, and Queues”
- Trees and Graphs: Essential Data Structures in Bioinformatics
- “Advanced Data Structures: Dynamic Arrays, Priority Queues, and Hash Tables”
- “Manipulating Data Structures: Adding, Removing, and Editing Data”
- Algorithmic Complexity Analysis in Bioinformatics
- Sorting and Searching Algorithms for Bioinformatics Applications
- Indexing Techniques for Efficient Data Retrieval in Bioinformatics
- Greedy Algorithms and their Applications in Bioinformatics
- Divide and Conquer Techniques for Problem Solving in Bioinformatics
- Dynamic Programming in Bioinformatics: Concepts and Examples
- Bioinformatics Algorithms for Sequence Analysis and Alignment
- Computational Methods for Graph Analysis in Bioinformatics
- Machine Learning Algorithms for Bioinformatics Data Analysis
- Practical Applications: Implementing Data Structures and Algorithms in Bioinformatics Software
Module : 9 = GRAPH Algorithm
- Introduction to Graph Algorithms in Bioinformatics
- Basics of Undirected Graphs: Representation and Exploration
- Understanding Directed Graphs: Acyclic Graphs and Topological Sorting
- Decomposing Graphs: Algorithms for Partitioning Graphs into Parts
- “Finding Shortest Paths in Graphs: BFS, Shortest-Path-Tree, Dijkstra’s, and Bellman-Ford Algorithms”
- Minimum Spanning Trees: Greedy Algorithms such as Kruskal’s and Prim’s
- Fundamentals of Graph Theory in Bioinformatics
- Representing Biological Data as Graphs: Applications and Techniques
- Graph Traversal Algorithms: BFS and DFS for Bioinformatics Applications
- Graph Clustering and Community Detection Algorithms in Bioinformatics
Module : 10 = String Algorithm
- Introduction to String Algorithms in Bioinformatics
- Brute Force Approach for Pattern Matching: Concepts and Applications
- Suffix Trees: Concepts and Algorithms for Pattern Matching
- Approximate Pattern Matching: Suffix Arrays and Burrows-Wheeler Transform
- Exact Pattern Matching: Knuth-Morris-Pratt Algorithm
- Techniques for Constructing Suffix Trees and Arrays
- Sequence Alignment Algorithms: Needleman-Wunsch and Smith-Waterman
- Sequence Similarity and Homology Search Methods: BLAST and HMMER
- String Manipulation Techniques in Bioinformatics: Applications and Tools
- Practical Applications: Implementing String Algorithms in Bioinformatics Software
Module : 11 = Neutral Networks
- Understanding Artificial Neural Networks: Types and Applications
- Building Shallow and Deep Neural Networks: Forward and Back Propagation Techniques
- Convolutional Neural Networks (CNNs): Foundations and Applications in Image Classification
- Object Detection with Convolutional Neural Networks: Techniques and Implementation
- Recurrent Neural Networks (RNNs): Types and Variants including GRUs and LSTMs
- Word Vector Representations and Embedding Layers in RNNs: Training Techniques
- Introduction to Attention Models and Their Applications in Speech Recognition
- Deep Learning Architectures for Bioinformatics: CNNs and RNNs
- Applications of Neural Networks in Sequence Analysis and Prediction
- Training and Evaluation of Neural Networks for Biological Data: Methods and Challenges
Module : 12 = Cheminformatics in Bioinformatics
- Drug Discovery and Development Process: Understanding QSAR Principles
- Introduction to Drug Discovery Process-drug discovery pipeline
- Role of Computational Methods- The significance of computational tools in drug design – Examples of computational methods in drug discovery
- Utilizing Biological Databases and Good Clinical Practices (GCP) Standards
- “Chemical Structure Visualization-ChemDraw / ChemSketch, Basics of chemical structure visualization”
- Visual Representation of Biological Processes and Structures in Data Analysis
- Biomolecules- Properties and function
- Molecular Docking and Molecular Dynamics: Outcomes in Visualization and Evaluation
- Pharmacophore Modeling and Applications
- Pharmacophore Modelling
Module : 13 = Variant Calling Analysis
- Introduction to NGS and DNAseq
- Basic Terminologies in NGS
- Understanding of SRA database
- Tools installation in Linux for Variation Calling
- Quality control (FastQC)
- Trimming of Reads (Trimmomatic)
- Indexing of Genome (BWA) and Alignment of Reads (BWA)
- Variation calling using GATK
- Variant Effect Prediction(VEP)
- Variation Visualization (IGV)
Module : 14 = Gene Expression analysis using Reference Based RNAseq Pipeline
- Introduction to NGS and its’s applications
- Introduction to RNAseq and it’s basic terminologies
- Basic Terminologies in NGS
- Understanding of SRA database
- Tools installation in Linux for Gene Expression analysis
- Quality control (FastQC)
- Trimming of Reads (Trimmomatic)
- Indexing of Genome (STAR) and Alignment of Reads (STAR)
- Normalization of Data (Cufflinks)
- Merging of Data (Cuffmerge) and Differential expression of genes (Cuffdiff)
- Understanding of DEG results
- Annotation of DEG (Uniprot/DAVID)
- Functional and Pathway Enrichment Analysis
- Network Analysis
- Visualization of Differential expressed genes in R (Heatmap & Volcano Plot)
Module : 15 = Introduction to Metagenomics
- Overview of Metagenomics: Concepts and Applications
- Historical Perspective and Evolution of Metagenomics
- Sampling and Sample Preparation Techniques in Metagenomics
- DNA Extraction and Sequencing Technologies for Metagenomics
- Metagenomic Data Analysis Pipeline: From Raw Reads to Biological Insights
- Taxonomic Profiling in Metagenomics: Identifying Microbial Communities
- Functional Annotation and Pathway Analysis in Metagenomics
- Applications of Metagenomics in Biomedical and Environmental Research
- Challenges and Limitations in Metagenomic Data Analysis
- Future Directions and Emerging Trends in Metagenomics
Module : 16 = AWS
- Introduction to Cloud Computing for Bioinformatics: Concepts and Advantages
- Overview of Amazon Web Services (AWS) for Bioinformatics Data Analysis
- Setting up an AWS Account and Access Management for Bioinformatics Workflows
- “Deployment Strategies for Bioinformatics Workflows on AWS: EC2, Lambda”
- “Utilizing AWS Services for Data Storage: S3, EBS, and Glacier”
- Leveraging AWS Compute Services for Bioinformatics Analysis
- Implementing Data Analysis Pipelines on AWS: Using Step Functions and Data Pipeline
- Cost Optimization Techniques for Bioinformatics Workloads on AWS
- Security Best Practices for Bioinformatics Data in AWS: IAM Policies and Encryption
- Monitoring and Management Tools for Bioinformatics Workflows on AWS
Module : 17 = Machine Learning/Artificial Intelligence
- Introduction to Machine Learning and Artificial Intelligence in Bioinformatics
- Supervised Learning Algorithms for Bioinformatic Data Analysis
- Unsupervised Learning Algorithms for Bioinformatic Data Analysis
- Semi-Supervised Learning Techniques in Bioinformatics
- Feature Selection Methods for Biological Data in Machine Learning
- Feature Extraction Techniques for Bioinformatics Analysis
- Model Evaluation and Validation Techniques in Machine Learning
- Applications of Machine Learning and AI in Bioinformatics: Classification
Module : 18 = Research Publication
Module : 19 = Manage Your Work to Meet Requirements
Module : 20 = Work Effectively with Collegues
Module : 21 = Build and Maintain Relationship at Workplace
Module : 22 = Build and Maintain Client Satisfaction
Module : 23 = Employability Skill
Module : 24 = Persuasive Communication
Module : 25 = Identifying Model Risk
Module : 26 = Measuring Model Performance
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