Unlock Your Bioinformatics Potential: Join the Super 30 Bioinformatics Batch!

Dear Bioinformatics Enthusiast,

Join us in shaping the future of bioinformatics by registering for the Super 30 Bioinformatics Batch of the Government of India’s Bioinformatics Analyst course. We are thrilled by your decision and excited to guide you on this amazing journey.
📚 Course Details:
● Duration: 6 months
● Start Date: 22nd July 2024 (BOOK YOUR SLOT asap)
● Timing: 8:00 PM – 9:00 PM (IST) / 2:30 PM – 3:30 PM (GMT) (LIVE CLASSES, Monday to Friday)
●🌟 Course Overview:
● Super 30 Bioinformatics Batch
● LSSSDC Indian Govt Certified Course
● 28 Credits Comprehensive Course
● Third Party Examination
● 100% Job Placement Assistance
● Exclusive Interview Opportunities (2 interviews)
● Research Guidance and Publication
● Hands-on Training with Practical Approach
● Live Interaction with Industry Experts

📖 Topics Covered:
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