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Bioinformatics

Bioinformatics

  1. Introduction to Bioinformatics:
  • Definition and scope of bioinformatics in genetics and genomics.
  • Use of computational technology to collect, store, analyze, and disseminate biological data.
  • Analysis of DNA and amino acid sequences, along with their annotations.

  1. Applications and Scope of Bioinformatics
  • Exploring the wide-ranging applications in research, medicine, and biotechnology.
  • Tools and techniques for sequence analysis and database searches.

  1. Sequence Analysis and Alignment Tools
  • Tools for similarity searches in sequence databases.
  • FASTA
  • BLAST (including BLASTP, BLASTN, BLASTX, TBLASTX, TBLASTN)
  • Multiple sequence alignment tools: Cluster-W.

  1. Advanced Data Visualization
  • Visualizing large biological datasets using R, Python (Matplotlib, Seaborn), and Galaxy.
  • Creation of interactive visualizations using D3.js, Plotly.
  • Presentation and interpretation of bioinformatics results using clear and informative graphics.

 

  1. Advanced Data Visualization
  • Visualizing large biological datasets using R, Python (Matplotlib, Seaborn), and Galaxy.
  • Creation of interactive visualizations using D3.js, Plotly.
  • Presentation and interpretation of bioinformatics results using clear and informative graphics.

  1. Biological Databases

    Overview of primary and secondary databases.

    Protein structure and family databases:

  • PDB (Protein Data Bank)
  • CATH (Class, Architecture, Topology, Homology)
  • SCOP (Structural Classification of Proteins)
  • Pfam (Protein Families Database)
  • PIR (Protein Information Resource)
  • PROSITE (Protein Patterns and Profiles)
  • Swiss-Prot (Manually Annotated Protein Sequence Database)

    Sequence databases:

  • GenBank
  • EMBL (European Molecular Biology Laboratory)
  • DDBJ (DNA Data Bank of Japan)
  1. Phylogenetic Analysis

Comprehensive understanding of phylogenetic analysis methods and their applications in evolutionary studies.

Fee Structure

Module: One-Month Training on Bioinformatics and Metagenomics

Week 1: Foundations of Linux and Programming
  • Day 1: Introduction to Command Line
  • Day 2: Advanced Linux/Unix Commands
  • Day 3: Package Managers – Setting up Conda Environments
  • Day 4–6: R Programming Basics
    • Syntax and data structures
    • Data import/export
    • Basic plotting and statistical operations
Week 2: NGS Fundamentals and Metagenomics Resources
  • Day 7:
    • Introduction to NGS
    • File Formats (FASTQ, BAM, SAM, etc.)
    • Quality Control Tools (e.g., FastQC, MultiQC)
  • Day 8–9:
    • Metagenomics Resources Overview:
      • MGnify: Visualization & analysis of metagenomics datasets
      • MG-RAST: Rapid annotations and functional analysis
      • CAMERA: Tools for microbial ecology and metagenomics
      • HMP: Human Microbiome Project resources and datasets
  • Day 10:
    • Microbial Genomic Databases:
      • NCBI, KEGG, GTDB, SILVA, EzBioCloud, RDP
    • QIIME for Gene-based Amplicon Sequencing:
      • Sequence clustering
      • Phylogenetic diversity overview
Week 3: QIIME2 Amplicon Workflow and Diversity Analysis
  • Day 11:
    • Introduction to QIIME2
    • Preprocessing and Quality Control
    • Sequence Clustering with DADA2
  • Day 12–13:
    • Phylogenetic Diversity Analysis
    • Taxonomic Analysis
  • Day 14:
    • Comparative Genomics: Pangenome Analysis
  • Day 15–16:
    • Introduction to Metatranscriptomics
    • Data processing pipelines and expression profiling
Week 4: Shotgun Metagenomics and Genome Assembly
  • Day 17–18:
    • Shotgun Metagenome Sequencing
    • Taxonomic and Functional Analysis
  • Day 19:
    • Whole Genome Assembly
  • Day 20:
    • Genome Assembly Statistics
    • Quality Assessment
    • Gene Prediction
  • Day 21:
    • Bacterial Species Identification
    • Sequence Typing
    • Serotyping Techniques
Select this Module for Registration

Bioinformatics Learning Programs Institute of Eminence Science and Research

About this program: The field of Life Sciences is rapidly advancing, with new tools and technologies continually emerging. In recent years, Bioinformatics has experienced remarkable growth and has become a vital component of nearly every biological research project. As a result, gaining proficiency in
Bioinformatics tools, techniques, and methodologies is now crucial for anyone working in the Life Sciences. To address this growing need, we have launched the NGS Learning Programs IESR (NGS-LP). 

NGS-LP IESR are designed to equip participants with the knowledge and practical skills needed to excel in the fast evolving world of genomics and NGS data analysis. With flexible online options ranging from 1 to 6 months, learners can choose a training path that fits their goals and schedule. These programs provide hands-on experience with real NGS datasets and cover essential tools, techniques, and workflows used in modern sequencing analysis. From quality control to variant calling and downstream interpretation, participants gain a solid foundation to apply NGS skills in both academic research and industry settings. Advanced modules also offer training in key programming languages like Python and R, empowering learners to automate workflows and perform custom analyses. The course culminates with a capstone project, allowing participants to apply their learning to a real-world NGS research challenge— preparing them for careers in genomics, diagnostics, and precision medicine.

Prerequisites: Basic understanding of biology and enthusiasm to learn. No computing or programming experience is required.

Who can take this course: Bachelors and Master’s degree students, PhD. Scholars, Clinicians, Researchers and Teachers from Pharma, Biochemistry, Microbiology, Biotechnology, Bioinformatics and other allied sciences

Programs under BLP

3/6 months Training with Project

Many students aspire to deepen their understanding of Bioinformatics and prepare themselves for industry roles. To support these goals, we’ve designed intensive 3- and 6-month training programs under the Bioinformatics Learning Programs (BLP).These programs focus on specialized topics in Next Generation Sequencing (NGS) Data Analysis, covering both DNA-Seq and RNA-Seq datasets. Participants will also gain proficiency in essential skills like Linux command-line operations, Python programming, and R programming—all tailored for biological data analysis.The training is structured into three focused modules, allowing learners to choose a path that best suits their interests and career objectives.

Module 1: Setting the Foundation

  • Introduction to key Bioinformatics Databases and their applications
  • Basics of Sequence Analysis:
  • Pairwise Sequence Alignment and use of BLAST tools
  • Understanding and performing Multiple Sequence Alignment (MSA)
  • Fundamentals of Molecular Phylogenetics and tree construction
  • Overview of Next-Generation Sequencing (NGS) concepts and workflows
  • Getting started with Python Programming for biological data tasks

Module 2: Variant Discovery

  • Linux for Biologists
  • Sequencing Technologies and Terminologies
  • De Novo Genome assembly (for Prokaryotes)
  • Genome Annotation (for Prokaryotes)
  • NGS Data Analysis workflow

            Different file formats
            Tools and their usage on the Linux command line
            Variant Calling workflow
            Variant annotation and interpretation

  • Pipeline development and hosting on GitHub

Module 3: From Genes to Pathways

  • Utilizing R programming for Bioinformatics applications
  • Understanding sequencing platforms and related terminology
  • Step-by-step RNA-Seq data analysis pipeline
  • Read alignment and gene count generation
  • Normalization of expression data and visual representation
  • Performing differential gene expression analysis
  • Conducting gene enrichment and pathway mapping
  • Building a complete analysis pipeline using R

3 months Training:

  • The first module is common for all the programs.
  • Participants opting for 3 months training program will have the option to
    select any one module from Modules 2 and 3.
  • The training will be completed in 10 weeks and the remaining time will be
    given to work on a project.

6 months Training:

  • Participants will be trained in all the 3 modules.
  • The training schedule will span over 4 months and the participant has to select a topic to work on the project for the rest of the time.
  • The successful participants will be awarded an e-certificate.
  • Participants can solve the assignments in their free time.
    The training offered is one-to-one. So, participants can start the training on any day.
    Each participant will be provided with presentations, and reference material.
    These training programs can be tailored as per the requirement of the participant
  • There will be 2 sessions of 2 hours every week. The sessions can be organized during the weekdays. Participants can solve the assignments in their free time.
  • The training offered is one-to-one. So, participants can start the training on any day.