DrOmics Labs


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

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
  • 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

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
  • 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)


Show More