Upcoming Workshop
Short Course on Research Data Analytics
Research Data Analytics
This comprehensive guide covers the entire research data lifecycle: from pre-processing (cleaning and preparing data), through analysis (using statistical and computational methods), to interpretation (drawing meaningful conclusions) and manuscript writing (documenting and publishing findings). It provides detailed strategies and best practices for each stage to ensure rigorous and reproducible research outcomes.
Research Data Analytics
This comprehensive guide covers the entire research data lifecycle: from pre-processing (cleaning and preparing data), through analysis (using statistical and computational methods), to interpretation (drawing meaningful conclusions) and manuscript writing (documenting and publishing findings). It provides detailed strategies and best practices for each stage to ensure rigorous and reproducible research outcomes.
Course Content
Basic Fundamental of statistics:
- Parametric test: t-test (Sample, Pooled or Unpaired and Paired), ANOVA, (One way and Two way).
- Correlation: Definition, Karl Pearson’s coefficient of correlation.
- Regression modeling: Hypothesis testing in Simple and Multiple regression models
Basics of R and Python
Building upon foundational skills on R and Python, you’ll explore data analysis, visualization techniques, and statistical analyses tailored specifically for bioinformatics applications. With Biopython, you’ll harness powerful libraries for sequence analysis, and sequence manipulation. You’ll also explore tools for data analysis, visualization used in bioinformatics
Data analysis and visualization tools:
- EXCEL
- R
- SPSS
- GRAPH PAD PRISM
- Data interpretation
- AI tools for MS writing
- Research paper writing
Short Course on Molecular Breeding Analytics
Molecular Breeding Analytics
This course provides a comprehensive understanding of molecular breeding techniques and data analysis, equipping you with the knowledge and tools needed to enhance breeding programs through genetic insights.
Course Content
Molecular Breeding Analytics Course Overview:
- Basic Concept of Molecular Marker and QTL Mapping:
A brief overview of QTL mapping, linkage disequilibrium (LD) analysis, and linkage analysis, Understand the process of QTL mapping to locate regions of the genome linked to quantitative traits.
- Various Molecular Diversity Analysis:
Explore techniques to assess genetic variation within and between populations using molecular markers. This includes cluster analysis, principal component analysis (PCA), and structure analysis.
- Molecular Marker Data Handling:
Master the skills for collecting, storing, and preprocessing molecular marker data. Learn quality control measures like checking for missing data, minor allele frequency, and Hardy-Weinberg equilibrium.
- SNP Data Analysis:
Gain proficiency in identifying and analyzing Single Nucleotide Polymorphisms (SNPs), including SNP calling, filtering, and annotation.
- Genome-Wide Association Studies (GWAS) with TASSEL and GAPIT in R:
Conduct GWAS to associate genetic variations with traits using TASSEL software and the GAPIT R package. Learn to scan genomes for SNPs and relate them to phenotypic traits.
- Haplotype Analysis:
Understand haplotypes and their analysis to study genetic structure and evolutionary history.
- Basics of R and Data Analysis:
Develop foundational skills in R programming, focusing on data manipulation, statistical analysis, and visualization.
- Data Representation and Interpretation:
Learn to present data clearly using tables, graphs, and charts. Develop skills to interpret results effectively for meaningful conclusions and decisions.
