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Basic Membership

Categories: Bioinformatics Programming, In-Depth

About Course

Basic Membership

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:

Discuss Bioinformatics Databases (NCBI, RefSeq, BLAST, UCSC Genome Browser)
Explain Bioinformatics Sub-Databases and their functionalities.
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:

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:

Explain Protein Databases (UniProt, PDB, InterPro) and their usage.
Retrieve data from Protein Databases.
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:

Perform Sequence Alignment & Analysis using different tools.
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:

Perform Phylogenetic Analysis using MEGA.
Perform Phylogenetic Analysis using iTOL.
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:

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:

Perform 3D Structure Prediction using SwissModel, MODELLER, HHPred, M4T, IntFold.
Perform Ab initio Protein Structure Prediction using ROBETTA.
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:

Perform 3D Structure visualization using USFC Chimera.
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:

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:

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

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:

Describe STRING database.
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:

Understand Molecular Dynamic Simulation.
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:

Install Python, R, BioPython, BioConductor and Linux.
Declare variables, store data, implement loops and use data structures.
Write functions in programming languages.
Download Multiple FASTQ files using Python Script.
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:

Perform Target Selection.
Remove Duplicates.
Screen Non-Homologous Proteins.
Screen the Antigenicity of Protein.
Perform Linear B-cell Epitope Prediction.
Asses linear B-Cell Epitope.
Assess CTL Epitope.
Predict and Assess HTL Epitopes.

Course Content

Bioinformatics Databases

Introduction to National Center of Biotechnology Information (NCBI)

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

FASTA (Sequence Format)

06:13
GenBank (Sequence Annotation Format)

07:08
FASTQ Format

18:02
Gene File Format/Gene Transfer Format

11:07
BED (Gene Structure Format)

04:27
SAM

09:07
BAM

09:07
Clustal Omega Alignment Format

05:32
MEGA (Alignment Format)

05:32
PHYLIP – Multiple Sequence Alignment Format

04:35
Stockholm Alignment Format

03:10

Protein Databases & Analysis

Introduction to UniProt

09:56
UniProtKB & Protein Analysis

39:30
UniProteome & Retreieval of an Entire Proteome

13:05
UniProt BLAST – Database Searching

12:33
ID Mapping & Making Analysis Easier

07:17
Introduction to Protein Data Bank (PDB) (Copy 1)

06:45
Accurately Searching for a Protein Structure on PDB & Protein Analysis (Copy 1)

13:56
Biological Annotation and Protein Features View & Analysis (Copy 1)

08:18
Browsing PDB According to Annotation (Copy 1)

06:52
Digging Out Categorized & Specific Protein Structures from PDB Archives (Copy 1)

06:23
Alignment Between Two PDB Sequences & Structures (Copy 1)

06:08
3D Structure Visualization on PDB (Copy 1)

10:49
Mapping Genomic Position to Protein Sequence and 3D Structure (Copy 1)

04:35
Genomic Discovery of Protein Structure Through Gene (Copy 1)

04:07
PDB – Protein Symmetry (Copy 1)

02:34
Introduction to InterPro (Copy 1)

04:10
InterPro – Protein Family Classification and Analysis (Copy 1)

14:35
InterPro – Protein & Protein Domain Analysis (Copy 1)

09:29
HMMER – Hidden Markov Model Based Protein Profiles Database (Copy 1)

13:16
SignalP: Prediction of Signal Peptides (Copy 1)

07:57
TargetP: Prediction of Protein Localization (Copy 1)

09:22
Pfam – Understand the Relation of a Protein to its Family and Clan (Copy 1)

15:56
PROSITE – A Database of Protein Domain, Families and Functional Sites (Copy 1)

13:46
ScanProsite – Scanning Protein for Important Protein Sites Against PROSITE Database (Copy 1)

07:36
Marcoil – Predict Coiled Coil Domains in Proteins (Copy 1)

04:06
SMART (Copy 1)

06:45
PDB – Ligands (Copy 1)

05:23

Sequence Alignment & Analysis

EMBOSS NEEDLE: Global Alignment of Sequences (Copy 1)

20:02
Clustal Omega: Most Reliable Multiple Sequence Alignment Tool (Copy 1)

19:18
EMBOSS Water (Copy 1)

09:10
Jalview (Copy 1)

13:42
T-Coffee: Iterative Multiple Sequence Alignment Tool (Copy 1)

08:38
MUSCLE: Accurate Multiple Sequence Alignment Tool (Copy 1)

21:08
MEGA – Multiple Sequence Alignment (Copy 1)

04:23
MAFFT – Fastest Multiple Sequence Alignment Tool (Copy 1)

08:22
Aln2Plot (Copy 1)

02:31

Phylogenetic Analysis

Secondary Structure Prediction

3D Structure Prediction

3D Structure Visualization

3D Structure Evaluation

Molecular Docking

MOE: Protein Ligand Docking (Copy 1)

09:23
MOE: Protein Protein Docking (Copy 1)

11:39
SwissDock Protein Ligand Docking (Copy 1)

19:16
Autodock Vina Protein Ligand Docking (Copy 1)

15:49
MOE: Docking Library of Compounds (Copy 1)

19:48
MOE: Structure Based Drug Designing (Copy 1)

16:19
ClusPro Protein Protein Docking (Copy 1)

21:44
Patchdock Protein Protein Docking (Copy 1)

17:40
PEPFOLD 3 Peptide Structure Prediction (Copy 1)

13:15
Zdock Protein Protein/Ligand docking (Copy 1)

19:35
MDockPEP Protein Peptide Docking (Copy 1)

10:06
Discovery Studio+ (Copy 1)

12:03

Docking Complex Evaluation

Gene Prediction

PPI Database

Genomics Tools

Molecular Dynamics Simulation

Getting Started With Molecular Dynamics Simulation – Pre-processing of Protein Structure and Removal of Unnecessary Structural Features (Copy 1)

12:34
pdb2gmx – Construction of Topology File for Simulation (Copy 1)

09:01
Defining a Solvant Box for Simulation (Copy 1)

04:14
Solvation – Adding Water Molecules in Solvant Box (Copy 1)

05:31
Generating Input Run File Replacement of Water Molecules With Ions (Copy 1)

06:55
Genion – Replacement of Water Molecules With Ions (Copy 1)

04:19
Energy Minimization – Relaxing and Fixing the Structure for Simulation (Copy 1)

11:25
GRACE – Visualization and Analysis of Minimized Structure (Copy 1)

04:12
Equibiliration of Protein Structure NVT ENSEMBLE Phase 1 (Copy 1)

08:38
Equibiliration of Protein Structure NPT ENSEMBLE Phase 2 (Copy 1)

08:10
mdrun – Executing Simulation Analysis (Copy 1)

03:47

Python

Introduction to Python and it’s Installation (Copy 1)

08:25
Why Python in Bioinformatics (Copy 1)

09:16
Comments (Copy 1)

05:43
Basic Input and output (Copy 1)

15:38
Mathematical Operations (Copy 1)

07:20
Strings (Copy 1)

21:51
Dictionaries (Copy 1)

10:57
Lists (Copy 1)

28:48
Lists (pt 2) and Tuples (Copy 1)

10:38
Sets (Copy 1)

07:36
If-Else (Copy 1)

09:19
For Loop and Calculation of Molecular Weight of Proteins (Copy 1)

10:56
While Loop and Biological Data Analysis (Copy 1)

09:37
Reading Files (Copy 1)

13:45
CSV (A special kind of file in Bioinformatics) (Copy 1)

08:42
Writing Files (Copy 1)

07:18
Consolidate (merge) multiple DNA and Protein Sequences into one FASTA file (Copy 1)

09:25
OS (Copy 1)

31:47
Function (Copy 1)

26:41
With (Copy 1)

08:50
Error Handling (Copy 1)

15:31

BioPython

Introduction to BioPython & Installation (Copy 1)

10:19
Bio.Seq Seq Object Behaves Like a String (Copy 1)

09:54
Bio.Seq Create a Seq Object (Copy 1)

07:34
Bio.Seq Central Dogma in Play Through Python (Copy 1)

08:41
Bio.Seq Unknown & Mutable Sequences (Copy 1)

06:54
Bio.Alphabet Understanding the Alphabets of Biology (Copy 1)

07:38
Bio.Alphabet IUPAC and Types of Sequence Representations (Copy 1)

07:38
Bio.Alphabet Concatenation of Multiple Seq Records Using Generic Alphabets (Copy 1)

09:47
SeqRecord Creating Seq Records (Copy 1)

12:28
SeqRecords & FASTA (Copy 1)

04:36
SeqRecords & GenBank (Copy 1)

03:29
SeqRecord Formatting Records (Copy 1)

03:06
SeqRecord Comparison & Reading Multiple FASTA Files from Directory (Copy 1)

05:47
SeqIO Reading a Sequence File (Copy 1)

10:32
SeqIO – Write Sequences and SeqRecords Into Files (Copy 1)

11:43
SeqIO Extracting Annotations and Pattern-wise Sequence Data Extraction (Copy 1)

10:35
AlignIO – Writing Alignments and Multiple Sequence Alignment Records (Copy 1)

05:29
AlignIO – Conversion of Alignment Formats (Copy 1)

04:02
AlignIO – Manipulating Alignments (Copy 1)

02:57
AlignIO – ClustalW Python Wrapper – Align Multiple Sequences (Copy 1)

07:47
AlignIO – Pairwise2 – Align Two Sequences (Copy 1)

07:31
AlignIO – Information Mapping of Alignments (Copy 1)

02:33
AlignIO – Format Alignments (Copy 1)

03:36
AlignIO – Slicing Alignments (Copy 1)

06:06
Bio.Blast – Querying NCBI BLAST Through Python (Copy 1)

11:15
Bio.Blast – Parsing BLAST Results (Copy 1)

14:52
Bio.Entrez – Accessing ENTREZ Using Python (Copy 1)

09:32
Bio Entrez Use Esummary To Get Summary Of Your Accessions (Copy 1)

08:59
Bio.Entrez – Use EFetch to Download Complete Records (Copy 1)

13:57
Bio.Entrez – Use EGQuery to Do Global Quries for Search Counts (Copy 1)

07:24
Bio.Entrez – Use Elink To Search For Database Links Of Records (Copy 1)

03:42
Bio.Entrez – Use ESearch to Search the Entrez Databases (Copy 1)

08:20
Bio.Entrez – Use Espell To Get Correct Spellings For Your Search Terms (Copy 1)

05:21
Bio.Entrez – Download GenBank and Entrez Records (Copy 1)

14:17
Bio.Entrez – Taxonomy Database Searching (Copy 1)

07:05
Bio.Entrez – Download PubMed Articles (Copy 1)

08:28
Bio.PDB – Reading a PDB (3D Structure) File (Copy 1)

11:59
Bio.Phylo – Calculating Distance Matrix Between Sequences For Phylogenetic Analysis (Copy 1)

04:18
Bio.Phylo – Converting Phylogenetic Tree Data Formats (Copy 1)

03:29
Bio.Phylo – Printing Out Phylogenetic Tree in ASCII (Copy 1)

02:17
Bio.Phylo – Reading Phylogenetic Trees (Copy 1)

06:29
Bio.Phylo – Visualization And Manipulation Of Phylogenetic Trees (Copy 1)

09:36
Bio.motifs – Creating a WebLogo of Motifs (Copy 1)

10:47
Bio.Phylo – Writing Out Phylogenetic Data (Copy 1)

04:04
Bio.motifs – MEME Analysis (Copy 1)

09:49

R

Introduction to R in Bioinformatics & R Installation (Copy 1)

09:48
The R User Interface (Copy 1)

06:23
Comments (Copy 1)

04:17
Variable Declaration and Objects (Copy 1)

05:24
Built-in Functions & ARGS (Copy 1)

04:32
Sample & Replacement (Copy 1)

09:09
Write Your Own Functions And Arguments (Copy 1)

05:39
Scripts (Copy 1)

07:36
Packages (Copy 1)

04:00
Install Packages (Copy 1)

05:25
Library & Initialize Packages (Copy 1)

02:28
Getting Help with Help Packages (Copy 1)

03:43
Atomic Vectors (Copy 1)

02:43
Doubles (Copy 1)

03:31
Integers (Copy 1)

03:23
Characters (Copy 1)

04:43
Logicals (Copy 1)

02:27
Attributes and Names (Copy 1)

04:46
Dim & Dimensions (Copy 1)

05:46
Matrix & Matrices (Copy 1)

04:43
Arrays (Copy 1)

03:42
Class (Copy 1)

03:13
Factors (Copy 1)

06:41
Lists (Copy 1)

06:42
Coercion (Copy 1)

04:27
Data Frames (Copy 1)

06:30
Loading Biological Data (Copy 1)

07:56
Saving Biological Data (Copy 1)

05:27
R Notation & Selecting Values from Biological Dataset (Copy 1)

04:09
Negative Integers for subsetting Biological Dataset (DataFrame) (Copy 1)

05:28
Positive Integers for subsetting Biological Dataset (DataFrame) (Copy 1)

05:26
Zero Notation for subsetting Biological Datasets (DataFrames) (Copy 1)

01:09
Blank Spaces For Biological Data Subsetting (Copy 1)

03:21
Dollar Signs for Biological Dataset Subsetting (Copy 1)

02:58
Modifying Values in Existing DataFrames/Datasets (Copy 1)

07:06
NA Values in Biological Dataset (Copy 1)

05:25
Figuring out NA Values in Biological Dataset (Copy 1)

02:06
Logical Subsetting in Biological Datasets (Copy 1)

09:46
If Else Statement (Copy 1)

04:15
For Loops & Biological Data Binding (Copy 1)

16:30
While Loops & Reading Multiple Biological Datasets (Copy 1)

16:16
Introduction to ggplot2 for Biological Datasets (Copy 1)

10:46
ggplot2: Key components (Copy 1)

08:26
ggplot2: Human Mitochondrial Proteome & Aesthetics (Size, Shape, Color) (Copy 1)

26:06
ggplot2: Facetting of Human Genome (Copy 1)

22:25
ggplot2: Smooth Out the Biological Data (Copy 1)

08:43
ggplot2: Boxplots for Human Mitochondrial Proteome (Copy 1)

07:56
ggplot2 :Histograms for Human Mitochondrial Pattern Finding (Copy 1)

06:02
ggplot2: Frequency Plots for Human Mitochondriala Frequency Mining (Copy 1)

06:13
ggplot2: Bar Charts Human Mitochondrial Knowledge Mining (Copy 1)

10:43
ggplot2 – Scaling and Limiting Data Visualization (Copy 1)

03:53
ggplot2 – Changing Labels and Finalizing Visualization (Copy 1)

08:42
ggtree – Phylogenetic Tree Visualization (Copy 1)

05:41
ggplot2 – Saving the Visualizations in High Resolution (Copy 1)

04:45

Linux

Introduction to Linux for Bioinformatics (Copy 1)

22:32
PWD – Print Working Directory (Copy 1)

01:26
CD – Changing Directories (Copy 1)

05:03
MKDIR – Making Directories (Copy 1)

08:13
MV – Moving Files, Directories and Data (Copy 1)

05:11
RM – Deleting Files and Directories (Copy 1)

01:24
Which & Whereis – Find Programs You Installed (Copy 1)

03:43
LS – Listing Files and Directories on Linux (Copy 1)

06:46
Find – Finding User Created Files (Copy 1)

03:39
Piping and Redirection of Data (Copy 1)

06:35
Cat – Visualization and Inspection of Text Data (Copy 1)

03:56
Head – Reading Specified Number of Lines from Top (Copy 1)

03:50
Tail- Reading Specified Number of Lines from Bottom (Copy 1)

02:23
Touch – Modifying File Statistics and Creating Files (Copy 1)

07:04
Stat – Statistics of File & Directories (Copy 1)

02:43
Wget – Retrieval of Genome Assemblies (Copy 1)

06:48
Curl – Retrieval of Bioinformatics Files (Copy 1)

02:25
Vim – Create and Edit Text Files (Copy 1)

05:59
Diff – Find Sequence Differences in Files (Copy 1)

02:35
GZIP – Compress and Archive Files Efficiently (Copy 1)

06:05
Tar – Create Archives of Genome Data (Copy 1)

04:19
GUNZIP – Extract Compressed Content (Copy 1)

02:14
Grep – Finding Uncharacterized Proteins in Human Genome (Copy 1)

08:55
Cut – Subsetting Required Textual Data from Text Files (Copy 1)

05:49
Sort – Sorting Data (Copy 1)

04:23
Uniq – Finding Unique Data Items (Copy 1)

10:33
WC – Statistics of the Data Within File (Copy 1)

02:46
CP – Copying Files and Files Contents (Copy 1)

03:43
Column – Proper Visualization of Delimited Datasets (Copy 1)

04:38

R

Introduction to BioConductor (Copy 1)

10:28
Installing Packages from BioConductor (Copy 1)

04:12
Reading and Writing the FASTA File (Copy 1)

06:58
Getting the Detail of a Sequence Composition (Copy 1)

07:46
Pairwise Sequence Alignment (Copy 1)

08:08
Multiple Sequence Alignment (Copy 1)

09:47
Handling BLAST Results (Copy 1)

03:54
Pattern Finding in a Sequence (Copy 1)

05:51
Performing ID Conversions (Copy 1)

05:45
The GO Annotation of Genes (Copy 1)

04:33
The GO Enrichment of Genes (Copy 1)

09:28
The KEGG Enrichment of Genes (Copy 1)

07:16
Introduction to dplyr (Copy 1)

15:38
Filter Rows with filter() (Copy 1)

20:13
Select Columns with select() (Copy 1)

28:30
Add New Variables with mutate() (Copy 1)

21:19
Grouped Summaries with summerize () (Copy 1)

18:30
Grouped Mutates (and Filters) (Copy 1)

19:58
Introduction to tidyr (Copy 1)

11:35
Data Spreading Function (Copy 1)

13:51
Data Gathering Function (Copy 1)

19:30
Data Separating & Pull (Copy 1)

17:24
Missing Values (Copy 1)

29:58
Introduction to ArrayExpress – Getting Started With MicroArray Analysis (Copy 1)

09:56
Introduction to BioConductor – Installing MicroArray Packages (Copy 1)

05:06
Getting Started with R Studio Project for MicroArray Analysis (Copy 1)

04:51
Downloading MicroArray Raw Data from ArrayExpress (Copy 1)

04:19
Creating Raw Intensities MicroArray Data Structure and Log2 Transformation (Copy 1)

14:41
Principle Component Analysis of Raw Expression Dataset (Copy 1)

15:44
Box Plot Visualization of Raw Intensity Data to Interpret the Median Intensities of the Samples (Copy 1)

03:11
ArrayQualityMetrics – Automated Quality Control for Microarray Datasets NNNNN (Copy 1)

05:38
Annotating the Probe IDs with Gene Symbols and Names (Copy 1)

04:19
Excluding Probe IDs with Multiple Mappings from the ExpressionSet (Copy 1)

04:39
Filtering out the Genes that are Above Threshold (Copy 1)

06:02
Heatmap Visualization of the Normalized Gene Expression Values (Copy 1)

11:52
Intensity-based Filteration of Low-Intensity Transcripts (Copy 1)

06:19
Normalization of Raw Intensities Values (Copy 1)

04:36
Relative Log Expression Analysis and Visualization (Copy 1)

08:57
Removal of the Probe IDs that Match to Multiple Genes (Copy 1)

04:04
Robust Multi-Array Summarization and Background Correction of the Raw MicroArray Data (Copy 1)

03:47
LIMMA – Data Preparation for Linear Modelling (Copy 1)

11:49
Factors Preparation (Copy 1)

10:26
Analysis of Gene Expression Levels of a Single Gene Among Different Conditions (Copy 1)

11:60
LIMMA – Applying Linear Model on a Single Gene Expression Data (Copy 1)

05:34
Data Visualization of the Gene Expression of a Single Gene (Copy 1)

00:00
Applying t-test to Find if Genes are Differentially Expression (Copy 1)

06:51
LIMMA – Applying Linear Model for Differential Gene Expression Analysis (Copy 1)

11:38
Extraction of Differentially Expressed Genes from the Fitted Linear Model (Copy 1)

03:35
Setting a Threshold for Differentially Expressed Genes (Copy 1)

04:17
Volcano Plot – Visualization of the Genes that are Differentially Expressed (Copy 1)

04:36
Matching the DEGs with Background Genes to Find Overlap (Copy 1)

00:00
topGO – Gene Enrichment & Ontology Analysis (Copy 1)

28:02
topGO – KEGG & REACTOME Pathway Analysis (Copy 1)

08:44

Target Identification

Immunoinformatics Approach for Epitope Prediction

Computational Construction of the Vaccine

Evaluation

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11 months ago

Thank you for the course. Very detailed and accurate

Beginner
50 hours 20 minutes Duration
Certificate of completion

The target audience for the Advanced Bioinformatician Course are biologists, beginner or intermediate Bioinformaticians or data analysts with no or little experience in applications of computational bioinformatics and analysis.
However, a superficial understanding of molecular biology and logic development for coding is expected from you before you join the course.
Bioinformatics is quite easy to get started in, even if you lack a proper understanding of the underlying concepts of Bioinformatics databases, servers, tools and the algorithms working behind them.

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