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All-Access Membership

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About Course

The field of bioinformatics is rapidly growing and offers numerous opportunities for students to advance their knowledge and skills. With the help of BioCode, students can have access to the latest advancements in bioinformatics and become more competitive in the global job market, ultimately contributing to the growth and development of the field. 

With BioCode’s All-access membership plan, students will have complete access to every bioinformatics course on the website, empowering them to learn about the latest advancements in this rapidly growing field. The All-access membership plan offers students a plethora of benefits, including unlimited access to all courses, personalized learning paths, and interactive tools and resources that make learning more engaging and effective. 

As an All-access member, students will have complete access to every course on the BioCode website. This includes foundational courses that cover the basics of bioinformatics, as well as advanced courses that explore cutting-edge topics such as machine learning and single-cell genomics. With unlimited access to all courses, students can learn at their own pace and customize their learning experience to suit their needs.

BioCode’s All-access membership plan offers students personalized learning paths based on their interests and skill level. Students can take a self-assessment to identify their strengths and weaknesses, and then receive recommendations for courses that will help them achieve their goals. This personalized approach to learning ensures that students get the most out of their membership and can advance their knowledge of bioinformatics quickly and efficiently.

BioCode’s All-access membership plan also includes 24/7 support from expert bioinformatics instructors. Students can ask questions, receive feedback on their work, and get personalized guidance on their learning journey. This level of support ensures that students always have the resources they need to succeed in their studies.

Overall, BioCode’s All-access membership plan is an excellent choice for students who want to expand their knowledge of bioinformatics and stay up-to-date on the latest advancements in the field. With unlimited access to all courses, personalized learning paths, interactive tools and resources, and 24/7 support, students can achieve their goals quickly and efficiently.

Mentioned below are some of the important sections of this membership plan:

Section 1: Bioinformatics Databases

Description: This section will focus on making sure that the students gain an understanding of the various bioinformatics databases that are used frequently during biological data analysis. Students will also learn about the structure and content of bioinformatics databases, including the types of data stored, the format of the data, and the tools and interfaces used to access and analyze the data. This section covers popular databases such as NCBI, BLAST, and GenBank, and how to search and retrieve data from these databases using various search methods and filters.

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

  1. Discuss NCBI.
  2. Perform Sequence Analysis.
  3. Describe GenBank.
  4. Discuss Gene Database.
  5. Explain RefSeq Database.
  6. Discuss BLAST Database Searching.
  7. Describe the Molecular Modelling Database.
  8. Explain the Short Genetic Variations Database (dbSNP).
  9. Discuss Homologene.
  10. Explain Taxonomy.
  11. Describe UCSC Genome Browser.
  12. Discuss Table Browser.
  13. Explain ENSEMBL.
  14. Perform Genome Assembly Retrieval and Analysis.
  15. Perform Gene Analysis & Annotation.
  16. Describe Variation Analysis.
  17. Interpret Plant Genome Records
  18. Visualize a Plant Genome Using JBrowse
  19. Explain Gene Reference Consortium.
  20. Describe Sequence Read Archive.
  21. Discuss BioProject, BioSystem, and BioSample.
  22. Explain Gene Expression Omnibus Database.

 

Section 2: Bioinformatics File Formats

Description: This section will focus on making sure that the students gain an understanding of the various bioinformatics databases that are used frequently during biological data analysis and their importance in storing and sharing biological data. This section provides an overview of file formats such as FASTA, FASTQ, SAM/BAM, VCF, GFF/GTF, and BED, and their specific uses in different areas of bioinformatics, such as sequence analysis, alignment, variant calling, and genome annotation.

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

  1. Explain FASTA (Sequence Format).
  2. Discuss GenBank (Sequence Annotation Format).
  3. Describe FASTQ Format.
  4. Explain Gene File Format/Gene Transfer Format.
  5. Describe BED (Gene Structure Format).
  6. Discuss SAM.
  7. Discuss BAM.
  8. Explain Clustal Omega Alignment Format.
  9. Describe MEGA (Alignment Format).
  10. Explain PHYLIP – Multiple Sequence Alignment Format.
  11. Explain Stockholm Alignment Format.

 

Section 3: Protein Databases and Analysis

Description: This section will make sure students learn about the different types of protein databases and their role in protein research and analysis. This section provides an overview of popular protein databases such as UniProt, PDB, and InterPro, and how to search and retrieve protein data from these databases using various search methods and filters.

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

  1. Explain UniProt Database and Protein Analysis.
  2. Discuss UniProt BLAST.
  3. Describe Protein Data Bank (PDB).
  4. Perform Alignment Between Two PDB Sequences & Structures
  5. 3D Structure Visualization on PDB.
  6. Calculate the Mapping Genomic Position to Protein Sequence and 3D Structure
  7. Evaluate Genomic Discovery of Protein Structure Through Gene.
  8. Discuss HMMER.
  9. Desribe SignalP & TargetP.
  10. Explain Pfam.
  11. Describe PROSITE.
  12. Discuss ScanProsite.
  13. Describe Marcoil.
  14. Explain SMART 6.

 

Section 4: Sequence Alignment and Analysis

Description: This section will make sure students will learn about the principles and methods of sequence alignment, a fundamental tool in bioinformatics used to compare two or more sequences of DNA, RNA, or proteins. This section covers different types of sequence alignment methods, including pairwise alignment and multiple sequence alignment, and the algorithms used to perform these alignments. The section covers the tools and techniques used for sequence alignment and analysis, including BLAST, ClustalW, and MUSCLE, and how to interpret and analyze the results of sequence alignments.

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

  1. Perform Global Alignment of Sequences Using EMBOSS NEEDLE.
  2. Perform Multiple Sequence Alignment Using Clustal Omega.
  3. Perform Sequence Alignment Using EMBOSS Water.
  4. Perform Sequence Alignment Using Jalview.
  5. Perform Iterative Multiple Sequence Alignment Using T-Coffee.
  6. Perform Accurate Multiple Sequence Alignment Using MUSCLE.
  7. Perform Multiple Sequence Alignment Using MEGA.
  8. Perform Multiple Sequence Alignment Using MAFFT.
  9. Perform Multiple Sequence Alignment Using MAFFT Aln2Plot.

 

Section 5: Phylogenetic Analysis

Description: This section will make sure students will learn about the tools and techniques used for phylogenetic analysis. The section covers the interpretation and analysis of phylogenetic trees, including branch lengths, node support, and tree topology.

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: This section will make sure students will learn about the principles and methods of predicting the secondary structure of proteins from their amino acid sequences. This section covers the importance of protein secondary structure in protein function, stability, and folding, and how secondary structure prediction can aid in the understanding and analysis of protein structure and function. Students will also learn various tools that are used to perform secondary structure prediction.

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

  1. Perform Secondary Structure Prediction Using Quick2D.
  2. Perform Secondary Structure Prediction Using Ali2D4.
  3. Perform Secondary Structure Prediction Using Jpred.
  4. Perform Secondary Structure Prediction Using HHrepID.
  5. Perform Secondary Structure Prediction Using DeepCoil.

 

Section 7: 3D Structure Prediction

Description: This section will make sure students will learn about the principles and methods of predicting the three-dimensional (3D) structure of proteins from their amino acid sequences. This section covers the importance of protein structure in protein function, and how predicting protein structures can aid in the understanding and analysis of protein function, interactions, and drug design. Students will learn about the different methods for predicting protein structure, including comparative modeling, ab initio modeling, and threading. Students will also learn how to use different software tools and databases to perform 3D structure prediction.

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

  1. Perform 3D Structure Prediction Using MODELLER.
  2. Perform 3D Structure Prediction Using SwissModel.
  3. Perform 3D Structure Prediction Using HHPred.
  4. Perform 3D Structure Prediction Using M4T.
  5. Perform 3D Structure Prediction Using IntFold.
  6. Perform 3D Structure Prediction Using ROBETTA.
  7. Perform Homology Modeling Using MOE.

 

Section 8: 3D Structure Visualization

Description: This section will make sure students will learn about the principles and methods of visualizing and analyzing protein structures. Students will learn about the different types of software tools and databases used for 3D structure visualization and analysis, including PyMOL and Chimera. The section covers the use of different visualization techniques, such as surface rendering, ribbon diagrams, and electrostatic potential maps, to highlight different aspects of protein structure and function.

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

  1. Perform 3D Structure Visualization Using UCSF Chimera.
  2. Perform 3D Structure Visualization Using PyMol.

 

Section 9: 3D Structure Evaluation

Description: This section will make sure students will learn about the principles and methods of evaluating the quality and reliability of protein structures. This section covers the importance of protein structure evaluation in assessing the accuracy and usefulness of predicted or experimentally determined structures, and how it can aid in the interpretation and analysis of structural data. The section covers the use of different software tools and databases for structure evaluation, as well as the impact of various factors on structure quality, such as sequence length, homology, and experimental method.

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

  1. Perform 3D Structure Evaluation Using WhatCheck.
  2. Perform 3D Structure Evaluation Using ProCheck.
  3. Perform 3D Structure Evaluation Using ERRAT.
  4. Perform 3D Structure Evaluation Using Verfiy3D.
  5. Perform 3D Structure Evaluation Using RAMPAGE.
  6. Perform 3D Structure Evaluation Using SAVES.
  7. Perform 3D Structure Evaluation Using PROSA.

 

Section 10: Molecular Docking

Description: This section will make sure students will learn about the different types of docking algorithms and scoring functions used for molecular docking, including rigid-body, flexible, and induced fit docking, as well as the impact of various factors on docking accuracy, such as target and ligand flexibility, solvation, and receptor conformational changes. The section also covers the use of different software tools and databases for molecular docking including

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

  1. Explain Protein-Ligand Docking.
  2. Perform Protein-Ligand Docking Using MOE.
  3. Perform Protein-Ligand Docking Using AutoDock Vina.
  4. Perform Protein-Ligand Docking Using SwissDock.
  5. Prepare Docking Library of Compounds Using MOE.
  6. Perform Structure-Based Drug Designing Using MOE.
  7. Explain Protein-Protein Docking.
  8. Perform Protein-Protein Docking Using MOE.
  9. Perform Protein-Protein Docking Using ClusPro.
  10. Perform Protein-Protein Docking Using PatchDock.
  11. Predict Peptide Structure Using PEPFOLD3.
  12. Perform Protein-Protein and Protein-Docking Using Zdock.
  13. Explain Protein-Peptide Docking.
  14. Perform Protein-Peptide Docking Using MDockPEP.
  15. Use Discovery Studio+.

 

Section 11: Docking Complex Evaluation

Description: This section will make sure students will learn about the assessment of the quality and accuracy of the docking results obtained in the Molecular Docking section, as well as the evaluation of the ADMET properties of potential drug candidates. The evaluation of the docking complex is an essential step in the molecular docking process as it provides insight into the reliability of the predicted binding modes and affinities. Students will learn about different criteria used for the evaluation of docking complexes, including geometric fit, binding energy, and interface properties.

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

  1. Perform Docking Complex Evaluation Using PDBsum.
  2. Perform Docking Complex Evaluation Using Pdbepisa.
  3. Assess the ADMET Properties Using SwissADME.

 

Section 12: Gene Prediction

Description: This section will make sure students will learn about the computational methods and algorithms used for identifying and predicting genes in DNA sequences. Students will learn about the different types of gene prediction methods, including ab initio, homology-based, and transcriptome-based approaches, and their advantages and limitations. The section also emphasizes the role of machine learning and artificial intelligence techniques in gene prediction and covers various software and tools used in the field, including GeneMark, AUGUSTUS, and GenScan.

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

  1. Perform Gene Prediction from Eukaryotic Genomes Using GeneMark.
  2. Perform Gene Prediction from Microbial Genomes Using Prodigal.
  3. Predict Genes From GeneMonkey Using GenScan.
  4. Find a Novel Gene Using GenScan.
  5. Predict Novel Genes in Star Fish or Any Genome Using AUGUSTUS.

 

Section 13: Protein-Protein Interaction (PPI) Database

Description: This section will make sure students will learn about the fundamental concepts of PPIs, including their significance in cellular processes, disease pathways, and drug discovery. Students will learn about the STRING PPI databases and they will also learn how to perform protein-protein interaction analysis on STRING.

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

  1. Perform PPI Using STRING Database.

 

Section 14: Molecular Dynamics Simulation

Description: This section will make sure students will learn about the fundamental concepts of molecular dynamics simulation, including the equations of motion, force fields, and integration methods. Students will learn about the different types of molecular systems that can be simulated, including proteins, nucleic acids, and small molecules. The section also emphasizes the role of molecular dynamics simulation in understanding the structure and function of biomolecules and covers various software and tools used in the field.

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

  1. Pre-process Protein Structure and Remove Unnecessary Structural Features.
  2. Construct a Topology File for Simulation Using pdb2gmx.
  3. Define a Solvant Box for Simulation.
  4. Perform Solvation (Adding Water Molecules in Solvant Box).
  5. Generate Input Run File (Replacement of Water Molecules With Ions).
  6. Replace Water Molecules With Ions Using Genion.
  7. Perform Energy Minimization.
  8. Visualize and Analyze the Minimized Structure Using GRACE.
  9. Perform Equibiliration of Protein Structure NVT ENSEMBLE Phase.
  10. Perform Equibiliration of Protein Structure NPT ENSEMBLE Phase.
  11. Execute Simulation Analysis Using mdrun.

 

Section 15: Biological Programming Using Python, BioPython, R, and BioConductor.

Description: This section will make sure students will learn about programming languages used in bioinformatics, including Python, BioPython, R, and BioConductor. It covers the fundamental concepts of programming, including data structures, control structures, and functions, and provides an introduction to programming languages commonly used in bioinformatics. Students will learn about the basics of Python programming and how to use BioPython to perform common bioinformatics tasks such as sequence alignment, sequence analysis, and database querying. The section also covers the basics of R programming and BioConductor packages that are used in biological data analysis.

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

  1. Perform Functions Using Bio.Seq Module in BioPython.
  2. Perform Various Functions Using the SeqRecord Module in BioPython.
  3. Perform Functions Using the SeqIO Module in BioPython.
  4. Perform Functions Using the AlignIO Module in BioPython.
  5. Perform Functions Using the Bio.Blast Module in BioPython.
  6. Perform Functions Using the Bio.Entrez Module in BioPython.
  7. Create a Phylogenetic Tree Using the Bio.Phylo Module in BioPython.
  8. Perform Motif Analysis Using Bio.motifs Module in BioPython.
  9. Perform Biological Data Manipulation Using R.
  10. Perform Biological Data Visualization Using ggplot2 in R.
  11. Utilize Linux Commands for Biological Data Manipulation and Interpretation.
  12. Perform Microarray Analysis Using R.
  13. Perform Sequence Alignment Using BioConductor packages.
  14. Perform Functional Enrichment Using Packages in R Language.

 

Section 16: Computational Vaccine Designing

Description: This section will make sure students will learn about the different computational methods and tools used in vaccine design, including immunoinformatics, structural vaccinology, and reverse vaccinology. Students will learn about the different steps involved in vaccine design, including antigen identification, epitope prediction, antigen structure determination, and vaccine formulation.

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

  1. Perform Target Identification.
  2. Use Immunoinformatics Approach for Epitope Prediction.
  3. Construct the Vaccine Computationally.
  4. Perform Molecular Dynamics and Immune Simulation.
  5. Perform Codon Optimization.
  6. Perform Disulfide Engineering.
  7. Perform Molecular Docking Between TLR4 and a Protein.

 

Section 17: In-Depth Introduction to Next-Generation Sequencing, RNA-Sequencing (RNA-Seq), and RNA-Seq Pipeline

Description: This section will make sure students will learn about the principles and applications of next-generation sequencing (NGS) technologies, with a focus on RNA-Seq. The section also covers the different steps involved in RNA-Seq data analysis, including quality control, read mapping, quantification, and differential gene expression analysis. Students will learn about the different bioinformatics tools and pipelines used in RNA-Seq data analysis, such as STAR, HISAT2, StringTie, and DESeq2.

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

  1. Explain NGS and RNA-Seq.
  2. Describe the RNA-Seq Analysis Pipeline.
  3. Perform Data Retrieval for RNA-Seq.
  4. Perform Quality Control Using FastQC.
  5. Pre-process Raw Reads.
  6. Perform Alignment of the Reads Against the Reference Genome.
  7. Perform Post-Alignment Processing.
  8. Perform Transcript Assembly and Quantification Using StringTie.
  9. Perform Differential Gene Expression Analysis Using DESEQ2.
  10. Perform Functional Enrichment Analysis Using enrichR.

 

Section 18: Hands-on NGS Whole-Genome Sequencing and Whole-Exome Sequencing Variant Calling.

Description: This section will make sure students will learn about the principles and applications of whole-genome sequencing (WGS) and whole-exome sequencing (WES) technologies. Students will learn about the different types of variant calling, including single-nucleotide polymorphisms (SNPs), insertion/deletions (indels), and structural variants. The section also covers the different steps involved in variant calling, including quality control, read mapping, variant calling, and annotation. Students will learn about the different bioinformatics tools and pipelines used in variant calling and annotation, such as GATK and Samtools.

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

  1. Discuss Next-Generation Sequencing, Whole-Genome Sequencing, and Whole-Exome Sequencing.
  2. Describe the WGS Variant Calling Pipeline.
  3. Describe the WES Variant Calling Pipeline.
  4. Perform WGS Variant Calling.
  5. Perform WGS Variant Calling for Microbial Genomics.
  6. Perform WES Variant Calling.

 

Section 19: In-Depth Introduction to Single Cell RNA-Sequencing (ScRNA-Seq), Pipeline and Single-Cell Technologies

Description: This section will make sure that the students learn about the principles and applications of single-cell RNA sequencing (ScRNA-Seq) technologies. The section also covers the different steps involved in ScRNA-Seq data analysis, including quality control, read mapping, feature counting, normalization, dimensionality reduction, clustering, and cell-type identification. Students will learn about the different bioinformatics tools and pipelines used in ScRNA-Seq data analysis, such as Seurat and ScanPy.

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

  1. Discuss scRNA-seq.
  2. Explain the Complete end-to-end scRNA-seq Analysis Pipeline.
  3. Differentiate Between Cellular and Tumor Heterogeneity.
  4. Differentiate Between Bulk vs. Single Cell RNA-seq Analysis.
  5. Describe the Single-Cell RNA-seq Technologies.
  6. Explain the 10x Genomics Complete Pipeline.
  7. Pre-process the Raw Datasets.
  8. Identify Cell Sub-populations.
  9. Perform Normalization, Quality Control, and Dimension Reduction.
  10. Perform Cell Clustering and Cell Annotation.
  11. Perform Differential Gene Expression Analysis.
  12. Perform Downstream Analysis.
  13. Explain the Seurat Package Complete Guidelines.
  14. Explain the ScanPy Package Complete Guidelines.

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What Will You Learn?

  • NCBI
  • Sequence Format
  • UCSC
  • UniProt
  • PDB
  • ENSEMBL
  • InterPro
  • Phytozome
  • Pairwise Sequence Alignment & Analysis
  • Multiple Sequence Alignment & Analysis
  • Alignment Format
  • Sequence Analysis
  • Phylogenetic Analysis
  • Phylogenetic Tree Visualization & Analysis
  • Secondary Structure Prediction
  • Protein Analysis
  • Protein Family Database
  • Motif & Domain Analysis
  • 3D Structure Prediction
  • 3D Structure Visualization
  • 3D Structure Evaluation
  • Molecular Docking
  • Docking Complex Evaluation
  • Gene Prediction
  • PPI Database
  • Genomic Tools
  • Molecular Dynamics Simulation
  • Molecular Dynamics Simulations: GROMACS
  • Vaccine Development
  • Introduction to Python
  • Iterable Objects
  • Control Flow
  • File Handling
  • Functions & Modules
  • Error Handling
  • Introduction to BioPython
  • Sequence Analysis in BioPython
  • Sequence Data Parsing
  • Sequence Data Extraction
  • Alignment Parsing and Analysis
  • BLAST Database Searching
  • Parsing BLAST results
  • Biological Data Retrieval
  • Parsing a PDB Structure file
  • Phylogenetic Analysis in BioPthon
  • R
  • Linux
  • Basics of Cellular Biology
  • Genetic Information and Its Flow
  • Brief Introduction to Genome and Model Organism
  • Cell Metabolism and Energy Production
  • Protein Structure, Shape and Characteristics
  • Chromsomes & Their Characteristics
  • Evolutionary & Mutation Rates
  • DNA Replication
  • Introduction to Immunology
  • Innate Immunity: Early Defense
  • Components of Innate Immunity
  • Innate Immune Reactions
  • Antigen Capture and Presentation to Lymphocytes
  • Antibodies: Antigen Recognition in the Adaptive Immune System
  • T-Cell Mediated Immunity
  • B Lymphocyte Mediated Immunity Humoral Immune Responses
  • Antibodies: Antigen Recognition in the Adaptive Immune System
  • Autoimmunity, Vaccines, Tolerance and Cancer Immunotherapy
  • Hypesenstivity: Disorders Caused By Immune Responses
  • Clinical Cases

Course Content

Bioinformatics Databases

  • 18:02
  • Sequence Analysis
    17:59
  • Sequence Retrieval from NCBI
    16:17
  • PubMed Central & ENTREZ
    11:07
  • GenBank: Nucleotide Database on NCBI
    06:50
  • FASTA vs GenBank
    18:26
  • Gene Database: A Comprehensive Gene Database
    30:21
  • NCBI Genomes & NCBI Assembly: Retrieval of Genomes
    36:14
  • RefSeq Database: Retrieval of Single Reference Sequences
    11:16
  • BLAST Database Searching
    25:37
  • Introduction to Molecular Modeling Database
    08:07
  • Database of Short Genetic Variations (dbSNP)
    12:16
  • HomoloGene: Discovery of Gene and Protein Families
    06:11
  • Taxonomy
    09:57
  • Introduction to UCSC Genome Browser & SARS-CoV-2 Viral Genome
    13:40
  • Retrieve an Entire Genome & Retrieval of SARS-CoV-2 Viral Genome
    09:41
  • Retrieval of Genomic Data & Annotation of SARS-CoV2 Viral Genome
    05:30
  • Table Browser & SARS-CoV-2 Viral Genome
    12:16
  • Visualization of Genomic Data on the Genome Browser & SARS-CoV-2 Genome
    10:51
  • UniProt BLAST – Database Searching
    12:33
  • UniProt Peptide Search – Find Regions Within UniProt Database
    03:15
  • Introduction to ENSEMBL
    07:50
  • Retrieval of a Gene-Protein-Chromosomal Region
    18:02
  • Genome Assembly Retrieval and Analysis
    10:24
  • Gene Analysis & Annotation
    34:40
  • Variation Analysis
    24:37
  • ENSEMBL BLAST/BLAT
    15:08
  • Regulation – Understand the Influence of Regulatory Elements on Genes
    04:19
  • Comparative Genomics Analysis
    05:35
  • Introduction to Phytozome
    09:39
  • Interpret Plant Genome Records
    09:07
  • Download an Entire Plant Genome & Proteome
    26:41
  • Keyword or BLAST Search in a Plant Genome
    15:58
  • Visualize a Plant Genome Using JBrowse
    17:38
  • UniProt Align – Pairwise & Multiple Sequence Alignment and Annotation
    03:48
  • UniRef And Retrieve Protein Clusters
    11:36
  • UniParc And Find the Non-Redundant Entries
    04:59
  • Genome Reference Consortium (GRC)
    07:48
  • BioProject
    06:40
  • BioSystems
    04:16
  • BioSample
    02:56
  • Sequence Read Archive (SRA)
    07:15
  • Introduction to Gene Expression Omnibus Database
    09:16
  • Gene Expression Omnibus – Platforms
    05:42
  • Gene Expression Omnibus – Samples
    04:16
  • Gene Expression Omnibus – Series
    04:01
  • Gene Expression Omnibus – Datasets
    04:45

Bioinformatics File Formats

Protein Databases & Analysis

Sequence Alignment & Analysis

Phylogenetic Analysis

Secondary Structure Prediction

3D Structure Prediction

3D Structure Visualization

3D Structure Evaluation

Docking Complex Evaluation

Docking Complex Evaluation

Gene Prediction

PPI Database

Genomics Tools

Molecular Dynamics Simulation

Python

BioPython

R

Linux

R

Target Identification

Immunoinformatics Approach for Epitope Prediction

Computational Construction of the Vaccine

Molecular Dynamics and Immune Simulation

Supplementary 1

Supplementary 2

Supplementary 3

Introduction to NGS, RNA-Seq Pipeline & GALAXY

Practical RNA-Seq Differential Gene Expression Analysis

In-Depth Introduction to RNA-Seq

Genomic Databases for Raw Data

File Formats

Introduction to Linux for RNA-Seq

RNA-Seq Data Analysis Pipeline (Theoretical & Practical)

Basics of Cellular Biology

Genetic Information and Its Flow

Brief Introduction to Genome and Model Organism

Cell Metabolism and Energy Production

Protein Structure, Shape and Characteristics

Chromsomes & Their Characteristics

Evolutionary & Mutation Rates

DNA Replication

PyMol MasterClass: Perform Expert Level 3D Structure Visualization for Drug Discovery

Hands-on: NGS (Whole Genome Sequencing) Variant Calling for Microbial Genomics
An NGS data analysis course on specifically microbial genomes (haploid in nature). In this course, the student will learn NGS from the very basics and will have proper hands-on understanding of whole-genome variant calling of microbial genomes such as parasites, bacterial or viral species. In this course, we will find both substitutions (snps) and insertions/deletions (indels).

Introduction to Anaconda and Jupyter-Notebook for Bioinformatics

Hands-on: Single-Cell RNA-Sequencing Data Analysis Using Python

Hands-on: NGS (Whole Exome Sequencing) Variant Calling Using Linux

Evaluation

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Student Ratings & Reviews

5.0
Total 2 Ratings
5
2 Ratings
4
0 Rating
3
0 Rating
2
0 Rating
1
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LJ
6 months ago
Excellent Advanced Bioinformatics program
MS
8 months ago
It was up to the mark and standard course

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