Advanced Bioinformatician Course

To become an expert Bioinformatician, one must learn how to retrieve and analyze biological data in the most efficient way, to align & analyze biological sequences, to predict the evolutionary histories between them, to find out the conserved patterns, to learn how to predict coding regions or genes from a raw nucleotide sequence and 3D structure prediction of proteins to predict the functionalities of newly discovered proteins, to predict the molecular interactions for generating virtual simulations according to their molecular interactional information of a chemical/biological ligand & the receptor which aids in the identification of lead compound in drug discovery & development.

In BioCode’s Advanced Bioinformatician Course you’ll learn from the very basics of Bioinformatics analysis skills including the most commonly utilized biological databases, their features, data retrieval & analysis, to advanced MD simulation & analysis that provides detailed information about the fluctuations and conformational changes in proteins and nucleic acids. You’ll also be able to learn from the very basics of biological programming in Python, BioPython & R to an advanced level understanding of Bioinformatics Scripting, even if you lack prior knowledge.

You don’t need any prior knowledge in order to take this course. Each and everything will be taught in this course. This course is completely hands-on which means you will learn every little thing about the theoretical as well as the practical aspect of bioinformatics.

This course will include the following sections:

Section 1: Introduction to Bioinformatics Databases

Description: This section will focus on making sure that the students learn about the bioinformatics databases and data retrieval.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Discuss Bioinformatics Databases (NCBI, RefSeq, BLAST, UCSC Genome Browser)
2. Explain Bioinformatics Sub-Databases and their functionalities.
3. Retrieve all sorts of biological data from databases.

Section 2: Introduction to Bioinformatics File Formats

Description: This section will focus on making sure that the students learn about the bioinformatics file formats and how they are used.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Describe Bioinformatics File Formats(FASTA, GenBank, FASTQ, SAM, BAM, Clustal Omega, MEGA, PHYLIP, Stockholm etc) and where they are used.

Section 3: Introduction to Protein Databases and Analysis

Description: This section will focus on making sure that the students learn about protein databases and how to perform analysis on the retrieved protein data.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Explain Protein Databases (UniProt, PDB, InterPro) and their usage.
2. Retrieve data from Protein Databases.
3. Perform Protein Analysis.

Section 4: Sequence Alignment and Analysis

Description: In this section the students will learn how to perform sequence alignment and sequence analysis with the help of different tools. 

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform Sequence Alignment & Analysis using different tools.
2. Perform Multiple Sequence Alignment using various tools.

Section 5: Phylogenetic Analysis

Description: In this section the students will learn how to perform phylogenetic analysis.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform Phylogenetic Analysis using MEGA.
2. Perform Phylogenetic Analysis using iTOL.
3. Perform Phylogenetic Analysis using FigTree.

Section 6: Secondary Structure Prediction

Description: In this section the students will learn how to perform secondary structure prediction.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform Secondary Structure Prediction using Quick 2D, Ali2D, Jpred, HHpredID, DeepCoil.

Section 7: 3D Structure Prediction

Description: In this section the students will learn how to perform 3D structure prediction.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform 3D Structure Prediction using SwissModel, MODELLER, HHPred, M4T, IntFold.
2. Perform Ab initio Protein Structure Prediction using ROBETTA.
3. Perform Homology Modeling using MOE.

Section 8: 3D Structure Visualization

Description: In this section the students will learn how to perform 3D structure visualization.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform 3D Structure visualization using USFC Chimera.
2. Perform 3D Structure visualization using Pymol.

Section 9: 3D Structure Evaluation

Description: In this section the students will learn how to perform 3D structure evaluation.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform 3D Structure Evaluation using WhatCheck, ProCheck, ERRAT, Verify3D, RAMPAGE, SAVES.

Section 10: Molecular Docking and Docking Complex Evaluation

Description: In this section the students will learn how to perform Molecular Docking and Docking Complex Evaluation.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform Molecular Docking using MOE, SwissDock, Auto Dock Vina, ClusPro, Patchdock, PEPFOLD 3, Zdock, MdockPEP, Discovery Studio +.
2. Perform Docking Complex Evaluation.

Section 11: Gene Prediction

Description: In this section the students will learn how to perform Gene Prediction using various tools.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform Gene Prediction using GeneMark, Prodigal, GenScan, AUGUSTUS.

Section 12: Protein-Protein Interaction using STRING

Description: In this section the students will learn how to perform protein-protein interaction using the STRING database.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Describe STRING database.
2. Perform Protein-Protein Interaction on STRING database.

Section 13: Molecular Dynamic Simulation

Description: In this section, students will learn how to perform Molecular Dynamic Simulation.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Understand Molecular Dynamic Simulation.
2. Perform all the steps needed in order to do Molecular Dynamic Simulation.

Section 14: Biological Programming Languages (Python, BioPython, R, BioConductor & Linux)

Description: In this section, students will learn from the very basics of biological programming in Python, BioPython, BioConductor & R to an advanced level understanding of Bioinformatics Scripting, even if you lack prior knowledge.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Install Python, R, BioPython, BioConductor and Linux.
2. Declare variables, store data, implement loops and use data structures.
3. Write functions in programming languages.
4. Download Multiple FASTQ files using Python Script.
5. Perform operations (Trimming, Alignment, Variant calling, Quantification) on FASTQ files via Python Script

Section 15:  Computational Construction of  Vaccines  & Immunoinformatics 

Description: In this section, students will first learn how to perform Target Identification. After that they will learn how to do Epitope Prediction in order to Construct Computational Vaccines.

Learning Outcomes:  Upon completion of this section, students will be able to:

1. Perform Target Selection.
2. Remove Duplicates.
3. Screen Non-Homologous Proteins.
4. Screen the Antigenicity of Protein.
5. Perform Linear B-cell Epitope Prediction.
6. Asses linear B-Cell Epitope.
7. Assess CTL Epitope.
8. Predict and Assess HTL Epitopes.