Lego based structure
Each level has many modules. Each Module has many topics.
- The Program duration including the introductory session is about 17 weeks (34 sessions)
- In the advanced level, the trainee can select either Machine Learning based track or Big Data based track.
Program Contents and Syllabus
An introductory session about the field of bioinformatics. Managers, fresh graduates, students and interested audience are invited to attend to know more about this strongly evolving field and how they can make use of it for the progress of own individual career or for the expansion of the enterprise business and research.
Linux and computing (1 session)
You will learn the basics of Linux and its major tools. You will also learn the basics of computing and programming.
Python (5 sessions)
As programming language, you will learn Python. You will learn the language structure, and it major data types, functions, and libraries.
Molecular Biology: You will learn or refresh your knowledge in molecular biology such as genome,, genes,, etc. You will learn about the central dogma of molecular biology and the basics of cell and systems biology.
Biological Databases: You will also learn about biological data types, databases and repositories. You will learn the biological data file formats. How to search and retrieve data items from the repositories in different formats.
Sequence Analysis: Pairwise and multiple sequence alignment; global and local sequence alignment; conserved domains;. You will also learn about searching DNA and Protein databases using BLAST.
Phylogeny: You will learn how to infer or reconstruct phylogenetic trees using DNA or protein sequences with or without time points.
Descriptive and Inferential Statistics:
You will learn data science basics including probability and descriptive statistics. You learn inferential statistics and hypothesis testing.
Scripting and visualization: You will learn how to use R and Python statistical and visualization packages
Microarray based analysis: Microarray technology and data generation, normalization, and standardization; fold change; volcano plots; differentially expressed genes
RNAseq based analysis:
NGS technology and RNAseq data generation, counting, normalization, and standardization; fold change; volcano plots; differentially expressed genes.
Pathways and Gene Ontology
You will learn more about biological pathways and gene Ontologies and related public data bases.
Gene set enrichment:
You will learn how to identify relevant set of genes in pathways or gene ontology affected by changes in set of gene expressions.
You will learn the foundations of the Next Generation Sequencing (NGS) technology and important NGS platforms.
NGS data: You will know data types and formats and how to handle them.
Genome Assembly: You will do genome assembly: de novo and reference based strategies.
Variant Analysis: You will learn identifying variations between genomes in terms of point mutations and structure changes.
You will learn the basics of data clustering for unsupervised learning to identify novel patterns in your data. You will learn supervised methods for data classification based on different techniques of relevance like deep learning, linear regression, and random forests.
You will learn, by examples, the methods for predicting sequence motifs in DNA and protein sequences. You also learn how to predict genes. You will also learn associations and prediction of outcomes for certain features related to disease. You will also how to predict taxonomy in metagenomics experiments
You will learn the basics of database and high performance databases. You will also learn about cloud computing and how to setup your resources in the cloud. You will also learn about high performance computing and use of computer cluster. You will develop more skills towards management of your source code and deployment of your programs using containerized schemes.