Short Term Bioinformatic NGS Course

ArrayGen Technologies offers comprehensive bioinformatics training and workshops designed for students, researchers, and professionals in life sciences. The programs include short-term virtual workshops, in-depth NGS courses, scripting modules, and extended BICP industrial program. The virtual workshops 30 days with 2-hour daily sessions, covering foundational and advanced topics in NGS data analysis, quality control, differential expression, pathway enrichment, and R-based visualization. Participants can choose from specialized modules such as RNA-seq, DNA-seq, single-cell RNA-seq, ATAC-seq, metagenomics, and more.

The 30-day Advanced Bioinformatics Training Program offered by ArrayGen Technologies is an intensive, hands-on course designed for students, researchers, and professionals in the life sciences who want to build strong practical skills in next-generation sequencing (NGS) data analysis and bioinformatics workflows. Delivered through live One to one 100% industrial online sessions (or optionally in-person in Pune), the program is divided into two modules.

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Participants in the 30-day Advanced Bioinformatics Training Program will gain a solid foundation in bioinformatics principles, including proficiency with Linux command-line tools and basic programming in Python and R tailored for biological data analysis. They will learn to efficiently handle and process NGS datasets through quality control, alignment, variant detection, and differential gene expression analysis.

By the end of the program, learners will be able to design and execute complete analysis pipelines for RNA-seq, DNA-seq, or other specialized NGS applications, interpret biological results using statistical and visualization techniques, and apply pathway and network enrichment methods. The hands-on practical work ensures that participants develop practical skills in data management, troubleshooting, and reporting, enabling them to confidently undertake research projects, thesis work, or professional tasks involving genomics and transcriptomics data. Graduates will also be prepared to extend their skills into internships or advanced research, supported by a certificate and personalized mentoring.

👉 Enroll now and take the first step toward a future in data-driven biology and beyond.

## 10% Discount if you register before 15th October, 2025. Hurry up!!


Course Information
Course Short Term Bioinformatic NGS Course
Duration 30 Days Training [ 2 Hours Daily [ Monday To Friday ] ]

Slots

Our working Time is 9:00 AM to 6:00 PM Indian Time Available slots - 9:00 AM to 11:00 AM / 11:00 AM to 1:00 PM / 2:00 PM to 4:00 PM / 4:00 PM to 6:00 PM
For training slots after 6 PM or before 9 am as well as weekends training kindly mention during registration accordingly it will be scheduled.

Mode

Online (one to one individual Focused Training)
👉 For online training candidate have to install ZOOM (with remote control on candidate system which makes 100% interactive)
👉 Run time video recording candidate can make as well as pdf manual will be provided for future reference.
👉 All our training is 100% practical and 100% industrial and 100% interactive which provides same as offline learning.
👉 For doubt clear there will be extra support will be provided based on the requirement.
👉 Certificate will be provided
Sequencing Platform Illumina /Ion Torrent/PacBio/Nanopore
Raw data ArrayGen’s in‑house demo datasets will be taken during the training sessions.
Training Fees
Module-I Advanced Bioinformatics & basic programming
Topics
    📘 Introduction to Bioinformatics
    - Overview of bioinformatics and its applications
    - Key concepts in computational biology
    - Role of bioinformatics in genomics, transcriptomics, and proteomics

    📘 Understanding NGS and Genomics Bioinformatics
    - Basics of Next-Generation Sequencing (NGS)
    - Types of NGS data (RNA-seq, WGS, WES)
    - Overview of NGS data formats: FASTQ, BAM, VCF
    - Introduction to pipelines and tools for NGS data analysis

    📘 Databases & Data Retrieval (NCBI and UCSC)
    - Learning how to retrieve biologically correct data
    - Performing complete batch retrieval (e.g., whole exome)
    - NCBI: understanding gene-level data retrieval
    - UCSC: handling large-scale data retrieval
    - UCSC Genome Browser and Table Browser usage
    - Batch Coordinate Retrieval and Genomic Data downloads
    - GFF/GTF gene annotation formats and how to retrieve them
    - Using BLAT for sequence-based search and alignment

    📘 Gene Prediction and Functional Annotation
    - Gene prediction approaches and tools
    - Functional annotation using Gene Ontology (GO)
    - Pathway analysis using KEGG, Reactome
    - Interpreting gene sets and biological relevance

    📘 Standalone/Offline BLAST for Large-Scale Genomic Data
    - Installing and setting up standalone BLAST
    - Running local BLAST for batch sequence alignment
    - Applications in genome-wide homology searches
    - Custom BLAST databases and performance optimization

    📘 PCR Primer Designing and Specificity Check
    - Designing accurate primers for PCR amplification
    - Tools: Primer3, NCBI Primer-BLAST
    - Checking primer specificity using genome-wide BLAST
    - Avoiding non-target amplification through design best practices

    📘 Understanding Python Programming
    - Introduction to Python for bioinformatics
    - Scripting basics: variables, loops, functions
    - Libraries like Biopython, pandas for biological data handling
    - Automating genomic workflows with Python scripts
AND
Module-II Microarray Data Analysis
Topics
    📘 Introduction to Microarray
    - Microarray Techniques: Detailed Understanding
    - Microarray Probe Designing

    📘 Microarray Data Analysis with R
    Getting Started with R
    - Introduction to R for Microarray Data Analysis
    - Hands-On Practical Sessions using Real Datasets

    Data Preprocessing & DEG Identification
    - Quality Control of Microarray Data
    - Normalization Techniques (e.g., RMA, quantile)
    - Differentially Expressed Genes (Upregulated & Downregulated)

    Data Visualization with R
    - Heatmap
    - Volcano Plot
    - PCA and Clustering Plots
    - Additional Visualizations for Gene Expression

    📘 Functional Analysis
    - Pathway & Gene Ontology (GO) Enrichment Analysis
    - Interpretation of enriched GO terms using tools like DAVID and clusterProfiler

    📘 Pathway Network Analysis
    - Using StringDB for Protein-Protein Interaction (PPI) Network Construction
    - Network Visualization and Analysis using Cytoscape
    - Pathway Mapping using KEGG Mapper for all DEG genes

    📘 Understanding R programming
    - R for statistical computing and data visualization
    - Using Bioconductor for bioinformatics workflows
    - Creating interactive plots with ggplot2
    - Performing statistical analysis on genomic data
AND
Module-III Next Generation Sequencing Applicqtion (NGS) - (RNASeq/Chip-Seq/DNASeq/ATAC-seq Data Analysis)
(Candidates may select any one application from RNA-Seq, ChIP-Seq, DNA-Seq, or ATAC-Seq data analysis. Selection is restricted to these NGS applications only.)
Topics
    📘 Introduction to Bioinformatics and NGS
    - Definition and scope of Bioinformatics
    - NGS Technologies: Illumina, PacBio, Nanopore
    - Types of NGS Application: RNA-Seq ref based and Understanding of all different NGS applications like chipseq, WGS, metagenome etc
    - Overview of Ref Based NGS data analysis Algorithm and pipeline

    📘 Linux Basics and Tool Installation
    - Basic Linux commands for bioinformatics
    - Installing tools using conda, apt, or source
    - Setup and Install NGS tools

    📘 Data Retrieval and File Types
    - Download data from NCBI SRA or EBI SRA using SRA Toolkit
    - Understanding FASTQ, SAM/BAM, GTF/GFF, FASTA formats

    📘 Read Quality Control
    - Assess Raw read quality
    - Trim adapters and low-quality bases

    📘 Read Alignment
    - Align reads
    - Generate and process SAM/BAM files
    - Post-alignment QC using samtools

    📘 Visualization of Aligned Reads
    - Use UCSC Genome Browser for custom tracks
    - View BAM files in IGV

    📘 Gene Expression Quantification
    - Calculate read coverage and gene counts
    - Compute RPKM/FPKM/TPM

    📘 Differential Expression Analysis (Known and Novel Transcripts Detection)
    - Differential calculation
    - Obtain logFC, p-values, adjusted p-values

    📘 Enrichment Analysis
    - GO and pathway Enrichment analysis
    - GSEA (significantly enriched Analysis)

    📘 Network Analysis (PPI)
    - Use STRINGdb for protein interaction networks
    - Visualize networks in Cytoscape with plugins

    📘 Pathway Mapping using KEGG Mapper
    - Map DEGs to KEGG pathways
    - Visual representation of active pathways (UP and Down regulated Genes)

    📘 Basics of R Programming
    - Data types, control structures, and packages
    - Installing Bioconductor tools

    📘 Coexpression Analysis

    📘 Data Visualization with R
    - Generate Heatmaps, Volcano plots, PCA plots
    - Use ggplot2, DESeq2, ComplexHeatmap

Course links

- RNAseq reference: https://www.arraygen.com/RNASeq_R.php
- RNASeq Denovo : https://www.arraygen.com/RNASeq_D.php
- ChipSeq : https://www.arraygen.com/ChipSeq.php
- DNASeq : https://www.arraygen.com/DNASeq.php
- ATAC-Seq : https://www.arraygen.com/ATAC-Seq.php
- Microarray : https://www.arraygen.com/microarray-data-analysis-training.php
- R Programming : https://www.arraygen.com/R-programming.php

Preparation

- RNAseq Reference: https://en.wikipedia.org/wiki/RNA-Seq
- NCBI : https://pmc.ncbi.nlm.nih.gov/articles/PMC5389949/

Instructor

Industry Experienced

Target Audiance

This course is designed for graduate students, postdoctoral researchers, and professionals working in the fields of conservation biology, evolutionary genomics, and population genetics or any life sciences who are interested in applying genomic tools to real-world conservation challenges.

Contact

Please write us at info@arraygen.com or call or whatsapp us on mobile +91-9673625446 if you need any clarification or for any custom training based on candidate reference paper or candidate own content/tools.