DrOmics Labs



“Embark on a transformative journey in bioinformatics with our LSSSDC (Govt. of India) Certified Course in Bioinformatics Science. Explore foundational principles and cutting-edge techniques, including genomics, data analysis, and machine learning. From sequence alignment to cloud computing, this comprehensive program covers essential skills for modern bioinformatics scientists. Prepare for a rewarding career and unlock endless opportunities in the dynamic intersection of biology and computer science.”


About Course

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,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 : 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

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
  • 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
  • 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
  • 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 Colleagues

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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