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LSSSDC (GOVT. OF INDIA ) CERTIFIED COURSE BIOINFORMATICS ANALYST

The LSSSDC (Government of India) Certified Course in Bioinformatics Analyst equips learners with essential skills in analyzing biological data using computational tools and techniques. Participants gain proficiency in interpreting genetic information, analyzing sequences, and employing software for genomics research. This certification validates expertise in the dynamic field of bioinformatics.

120,000.00

About Course

Module 1 : Orientation for Bioinformatics Occupation

  • 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,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 installtion
  • Data Types, Variables, and Basic R Operations
  • Function-buit-in and User defined
  • Conditional statments
  • 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 : Machine Learning and Image Analysis

  • Introduction to Machine Learning Fundamentals for Bioinformatics
  • Linear Models and Nearest Neighbors: Learning Algorithms and Regularization
  • Basics of Probabilistic Machine Learning and Its Applications in Bioinformatics
  • Implementing Support Vector Machines (SVM) and Kernel SVM in Python
  • Introduction to Naive Bayes Classifier and Its Use in Bioinformatics
  • Decision Tree Classifier and Random Forest Classifier: Theory and Implementation
  • Logistic Regression in Bioinformatics: Concepts and Practical Applications
  • Introduction to Clustering Algorithms: K-Means and Its Application in Bioinformatics
  • Validation of Machine Learning Models: Techniques and Accuracy Metrics
  • Theoretical Concepts and Practical Aspects of Machine Learning for Image Analysis in
    Bioinformatics

Module 9 : Statistical Methods and Tools for data extraction and preparation

  • Introduction to Statistical Methods for Data Extraction and Preparation in Bioinformatics
  • Exploring Data Characteristics and Distribution: Descriptive Statistics and Data Structures
  • Understanding Correlation and Regression Analysis in Bioinformatics
  • Probability and Bayes Theorem: Foundations for Statistical Inference
  • Sampling Techniques and Distribution Theory in Bioinformatics
  • Hypothesis Testing: Concepts and Methods for Data Analysis
  • Statistical Tools for Data Management, Analysis, and Visualization in Bioinformatics
  • Inferential Statistics: Making Valid Generalizations from Sample Data
  • Interpreting Statistical Outputs for Informed Decision Making in Bioinformatics
  • Practical Applications: Applying Statistical Methods to Solve Bioinformatics Problems

Module 10 : Data Mining

  • Introduction to Data Mining in Bioinformatics: Concepts and Applications
  • Understanding Data Warehousing: Life Cycle and Implementation
  • Classification and Clustering Techniques for Data Analysis in Bioinformatics
  • Outlier Analysis: Identifying Anomalies in Bioinformatics Data
  • Overview of Forecasting Techniques in Bioinformatics
  • Introduction to Hadoop and its Role in Big Data Analytics
  • Exploring the R Language for Statistical Computing and Data Analysis
  • Data Analytics Project Life Cycle: Planning, Execution, and Evaluation
  • Strategies for Importing Data from Different Databases for Analysis
  • Practical Applications: Performing Data Mining from Large Data Sources in Bioinformatics

Module 11 : Basics of Algorithm Development and Implementation

  • Introduction to Program Design: Principles and Methods
  • Basic Structures for Algorithm Development
  • Pros and Cons of Efficient and Naïve Algorithms
  • Structured Programming Rules
  • Divide and Conquer Technique for Problem Solving
  • Algorithm Definition in Structured Language
  • Algorithm Correctness Verification
  • Data Validation and Error Handling in Algorithm Design
  • Optimization Techniques for Algorithm Efficiency
  • Practical Application of Program Design Principles in Bioinformatics

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)

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