Online trainings at NEGenome is designed to offer both theoretical and practical sessions, utilizing a variety of tools and resources. Participants will engage in hands-on assignments and short projects throughout the course. Upon completion, they will gain the skills to independently analyze their own datasets.

Training Programs Offered:

Basic Training
Duration: 15 days
Deliverables: Certificate
Summer Training
Duration: 60 days
Deliverables: Certificate
Advanced Training
Duration: 180 days
Deliverables: 1 Research Article, Certificate

Training Fees: For detailed information on course fees, please contact us at negenome@gmail.com/8135038866

💡Trainging Modules

RNAseq Data Analysis

This module comprises two subsections: reference-based and de novo RNA-Seq analysis. Each subsection includes the following components: retrieval of NGS data from the NCBI SRA, quality checking of reads using FastQC and Cutadapt, de novo transcriptome assembly with Trinity or alignment with the reference genome using Tophat or STAR. Additionally, it covers quantification of gene expression using metrics like Coverage and FPKM, and differential expression analysis employing Cufflink, cuffmerge, and cuffdiff. The module also features KEGG pathway and gene ontology (GO) enrichment analysis, network analysis utilizing STRING DB for protein-protein interactions (PPI) and Cytoscape, as well as graphical representation of results, including heat maps and volcano plots

DNA-seq Data Analysis

This module consists of two sections: de novo assembly and reference-based analysis, covering essential topics in next-generation sequencing (NGS). In the de novo section, participants will learn about gene prediction and functional annotation, equipping them with the skills to assemble genomes from scratch and interpret their biological significance. The module also covers the retrieval of NGS data from the NCBI Sequence Read Archive (SRA) and performing quality checks on reads using FastQC and Cutadapt. Participants will gain experience in aligning reads to a reference genome using tools like BWA and Bowtie, as well as managing mapping outputs in SAM/BAM formats with Samtools and Bedtools. The course will include hands-on training in variant detection and visualization using GATK and IGV, along with variant effect prediction using ENSEMBL VEP.

miRNA-seq Data Analysis

The miRNAseq analysis course provides a comprehensive overview of the processes involved in analyzing microRNA sequencing data. The course begins with an introduction to next-generation sequencing (NGS) and covers the retrieval of NGS data from the NCBI Sequence Read Archive (SRA). Participants will learn to align reads to a reference genome using miRDeep2, a specialized tool for miRNA analysis. The course includes instruction on detecting both known and novel miRNAs using miRDeep2, as well as predicting miRNA targets through a combination of in-house scripts and online tools. Additionally, participants will conduct differential expression analysis using edgeR, and perform KEGG and Gene Ontology (GO) analysis to interpret biological significance. The course will also cover various visualization techniques, including heatmaps and volcano plots, to effectively present and interpret the analysis results.

Metagenomics Data Analysis

The Metagenomics Data Analysis course offers a thorough exploration of the fundamental concepts and methodologies in metagenomics. Participants will begin with the basics of metagenomics, laying a solid foundation for understanding the complexities of microbial communities. The course covers essential data quality checks and trimming using tools like FastQC and Cutadapt, as well as demultiplexing and quality filtering of sequence reads. Students will learn to identify Operational Taxonomic Units (OTUs) using Qiime and conduct diversity analysis, focusing on both alpha and beta diversity metrics. For those selecting the shotgun metagenome data analysis track, the course will include taxonomy classification using tools such as Kraken2 or MetaPhlAn4. Participants will also learn genome assembly followed by functional annotation, further enriching their understanding of microbial functions within complex environments. The course will delve into taxonomic composition and relative abundance plots, enabling participants to visualize the structure of microbial communities effectively. Additionally, students will gain experience in creating various visual representations of their data, including heatmaps and KRONA plots, to enhance their analysis and interpretation skills in metagenomic studies.

Microarray Data Analysis

The Microarray Data Analysis course provides a comprehensive overview of the methodologies used to analyze gene expression data from microarray experiments. Participants will start with data retrieval, ensuring they can effectively gather the necessary datasets for analysis. The course includes detailed instruction on gene expression analysis using R and various online tools, equipping participants with the skills to manipulate and interpret their data effectively. Quality control and normalization techniques will be emphasized to ensure data reliability, followed by differential expression analysis to identify up-regulated and down-regulated genes. Participants will also engage in KEGG and Gene Ontology (GO) analysis to explore the biological significance of their findings. Additionally, the course will cover graphical representation of results, allowing students to create clear and informative visualizations to effectively communicate their analysis outcomes. This course is designed to empower participants with the practical skills needed for robust microarray data analysis.

Codon Usage Bias (CUB) Analysis

The Codon Usage Bias (CUB) Analysis course offers an in-depth exploration of codon usage patterns and their implications in genomics. Participants will begin with a fundamental understanding of codons, establishing a solid foundation for further analysis. The course will cover data retrieval techniques from the NCBI database, ensuring participants can effectively access the necessary genomic data for their studies. Key topics include Relative Synonymous Codon Usage (RSCU) analysis and Effective Number of Codons (ENC) analysis, which help in quantifying codon usage bias within sequences. Participants will also learn about correspondence analysis to visualize relationships between codon usage and various factors, as well as codon context analysis to investigate how neighboring codons influence usage patterns. To enhance the understanding of their findings, students will generate a variety of plots, including ENC, neutrality, and parity plots, facilitating clear visual representation of codon usage bias. This course is designed to equip participants with the analytical skills and knowledge necessary for conducting comprehensive CUB analysis in genomic studies.

Advanced Bioinformatics Training

The Advanced Bioinformatics Training course provides a comprehensive and in-depth understanding of the key concepts and tools in the field of bioinformatics. Participants will start with basic concepts in bioinformatics, establishing a solid foundation for more advanced topics. The course will cover essential databases and tools, including NCBI, UCSC, BLAST, and BLAT, enabling participants to navigate and utilize these resources effectively. Key topics include Open Reading Frame (ORF) and gene prediction, genome annotation, and functional annotation, which are crucial for interpreting genomic data. Participants will also learn about primer design and validation techniques, ensuring they can effectively plan and execute experimental work. Additionally, the course will cover genome visualization methods to help students interpret complex genomic information. The training emphasizes hands-on practical experience, with sessions focused on programming in Perl and BioPython, equipping participants with the necessary skills to automate and enhance their bioinformatics analyses. Basic Unix commands will also be covered, providing participants with the foundational skills needed to work efficiently in a Unix-based environment. This course is designed to empower participants with the advanced skills and knowledge required for a successful career in bioinformatics.

Google Sites

Report abuse