The aim of this course is twofold: to provide an overview of the most common statistical methods for molecular genomics and transcriptomics data analysis, and to provide the necessary information for solving the complex biological problems and achieving the satisfactory score of sustainable development goal (SDG) index from the agriculture and health sectors.

to understand statistical modeling on molecular genomics and transcriptomics datasets

to learn most common statistical methods for genomics and transcriptomics data analysis

to develop the capability of statistical model building strategies for genomics and transcriptomics data analysis

hands-on training on genomics and transcriptomics data analysis to understand how to provide the necessary information to solve the complex biological problems

to analyze genomics and transcriptomics datasets to provide the necessary information to solve the complex biological problems that are associated with the genetic factors

to select appropriate statistical algorithms for analyzing genomics and transcriptomics datasets

to contribute to the development of high yielding varieties and to achieve the satisfactory score of SDG index from the agricultural sector.

to contribute to the discovery of new drugs/vaccines for the complex diseases and to achieve the satisfactory score of SDG index from the health sector.

Basic Genomics: Introduction. Classification of Genomics. Genes and Chromosomes. Cell division, Nucleic acid,  Molecular Genetics (Marker, DNA sequence, SNPs). Genotype and Genotyping technology. Central dogma (DNA/Gene transcription, RNA translation and Protein synthesis).

DNA Sequence Analysis: DNA sequencing. Classification of sequencers including NGS. Analysis of DNA patterns, Overlaps counted, Overlaps not counted and motifs, Sequence accuracy, Sequence formats, Conversions of one sequence format to another. Single and multiple sequence alignments approaches. Phylogenetic analysis DNA Sequence. Some bioinformatics databases including GeneBank, NCBI, PDB, BLAST and FASTA.

QTL Analysis: .Introduction. Marker Analysis of Phenotypes. Whole-Genome Marker Analysis. The Structure of QTL Mapping (Population and  Quantitative Genetic Structure of the Mixture Model). Interval Mapping Approaches for QTL Analysis (Linear regression and maximum likelihood approaches for QTL analysis with backcross and F2 populations). Composite and multiple interval mapping approaches for QTL analysis.

Gene-Expression Data Analysis: Introduction to different types of microarray gene expression data. Preprocessing (Transformation, Normalization, Image analysis and filtering).  Identification of differential expressed (DE) genes in two or more groups using statistical test. Clustering and Classification for Gene-Expression Data Analysis.   Inferring genetic regulatory networks from microarray experiment with Bayesian networks. Modeling genetic regulatory networks using gene expression profile. Gene-set enrichment analysis.

Genome-wide Association Studies (GWAS): Introduction .  QTL and SNP analysis with Gene-Expression data.  SNP analysis using contingency table.  GWAS using linear mixed models and GeneABLE.  Haplotype Estimation. Regional multilocus association models. Linkage disequilibrium and tagging. Practical guide to linkage disequilibrium analysis and tagging using Haploview.

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