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BIF733 : Bioinformatics I (Essentials of Genome Informatics)

Course Overview

Course Synopsis

The course will provide basic knowledge to the beginners in bioinformatics. Mainly this course is designed to acquaint the students with bioinformatics, its methods and goals. The students would learn how bioinformatics differs from traditional biology and how traditional research methods can be improved using bioinformatics. What are modern day Genome Sequencing and analysis techniques and their applications? They will also be introduced with major Algorithms used for solving Biological problems.

Course Learning Outcomes

At the end of the course, you should be able to:

  • Recognize the importance of Bioinformatics in modern day research
  • What are main kinds of Biological data and how this data is stored into and retrieve from Biological databases?
  • Clear understanding of different types of Biological problems and major computational approaches to address these issue.
  • Have insights into Genomes and complexities of their analysis.
  • Relate the role of overlapping areas like Statistics and Mathematics in Bio-Computing
  • Explore modern day Genome sequencing technologies and the analysis of the high throughput data they generate.
  • Clear understanding of new requirements of Biologists (end users) and the approaches to find the most probable answers.

Course Calendar

1 Introduction
2 Introducing Bioinformatics
3 Scope of Bioinformatics
4 Challenges in Bioinformatics
5 Collaborating expertise
6 Deoxy Ribose Nucleic Acid (DNA)
7 Ribose Nucleic Acid (RNA)
8 Protein
9 Central dogma of life
10 Exceptions to central dogma of life
11 Introduction to Bioinformatics Algrorithms
12 Tasks in Bioinformatics
13 Introducing Algorithms I
14 Introducing Algorithms II
15 Biological vs Computer Algorithms
16 Types of Algorithms
17 Approaches to design Algorithms
18 Nucleotide Sequence databases
19 Protein databases
20 Genome and organism specific databases
21 Gene expression databases
22 Medical databases
23 Sequence submission
24 DNA Sequence Retrieval
25 Protein Sequence Retrieval
26 Sequence Formats
27 Data Retrieval
28 Genome Informatics
29 Prokaryotic Genome
30 Eukaryotic Genomes
31 Epichromosomal elements (EEs)
32 Genome repeats
33 Transposable Elements (TEs)
34 Eukaryotic Gene Structure
35 Comparative Genomics
36 Comparative Proteomics
37 Between proteome comparison
38 Horizontal Gene Transfer
39 Gene Order (Synteny)
40 Genome annotations
41 Genome Sequencing
42 Developments for sequencing 1
43 Developments for sequencing 2
44 Cloning Techniques
45 Sequencing Techniques
46 Automated Sequencing
47 Shot Gun Sequencing
48 HGP: Introduction
49 HGP: Methods
50 HGP: Outcomes
51 Characteristics of Human genes
52 HGP: Applications
53 Next Generation sequencing (NGS)
54 Exraction protocols
55 Library Preparation
56 Amplification
57 NGS Platforms
58 454 Pyrosequencing
59 Illumina Sequencing
60 Ion Semiconductor sequencing
61 3rd Generation Sequencing
62 Helicose Sequencing
63 Pacbio Sequencing
64 Oxford Nanopore Sequencing
65 Comparison of NGS methods
66 Scope of NGS
67 Applications of NGS
68 Genome Sequencing II
69 Transcriptome Sequencing
70 Epigenome Sequencing
71 Metagenome sequencing
72 Five Big Projects in NGS
73 NGS Output Formats
74 NGS Quality Scores
75 NGS Data Quality Control
76 NGS Data Analysis
77 Galaxy
78 Galaxy: Tools
79 NGS: Mapping and Visualization
80 Genome Assembly
81 Genome Assembly Overview
82 Genome Assembly Planning
83 Effect of Repeats
84 Related species Assembled
85 Greedy Graph Algorithm
86 ALLPATHS-LG
87 Lander Waterman Curve
88 Assembly Pipeline1
89 Assembly Pipeline2
90 Quality of Assembled Genome
91 Graphs
92 Overlap Layout Consensus
93 De Bruijn Graph
94 De Bruijn Graph Example
95 Assembly Issues
96 Introduction to Statistics
97 Variables
98 Distributions
99 Measure of Central Tendency
100 Data Presentation
101 Box plot
102 Probability
103 Mutually Exclusive Events
104 Union and Intersections
105 Types of Probabilities
106 Bayes Theorem
107 Occasionally dishonest casino
108 Maximum Likelihood Estimation
109 Binomial Distribution
110 Normal Distribution
111 Markov Chains
112 Probabilities in Markov Models
113 Markov Chain Model
114 Markov Chains for Discrimination
115 Occasionally Dishonest Casino 2
116 Defining Genes
117 Finding Genes
118 Gene Prediction
119 ORF Prediction
120 ORF Finders
121 TSS Prediction
122 Prediction of Splice Junctions
123 Prediction of Exons
124 Annotation of assembeled Genome
125 Pattern Finding in a Genome
126 Pattern Finding Algorithms
127 Brute Force Approach
128 Knuth-Morris-Pratt Algorithm
129 KMP: Prefix function
130 KMP: Execution
131 Boyer Moore Algorithm
132 Pair Sequence Alignment
133 Scoring Schemes
134 Subsitution Matrices
135 Optimal Alignment Methods
136 Needleman-Wunsch Algorithm
137 Needleman-Wunsch Execution
138 Smith-Waterman Algorithm
139 PSA: Complex Models
140 DOT PLOT
141 FASTA Algorithm
142 BLAST
143 Doing BLAST locally
144 P Value
145 Significance of Scores
146 Extreme value Distribution (EVD)
147 Hidden Markov Model (HMM)
148 HMM: Generating Sequences
149 HMM: Classical problem
150 HMM: Viterbi Algorithm
151 HMM: Viterbi Example
152 HMM Example Casino Revisited
153 Multiple Sequence Alignment
154 MSA: Steps
155 MSA: Approaches
156 Dynamic Programming
157 MSA Algorithm
158 MSA: Scoring
159 MSA: Progressive Method
160 MSA: Iterative Methods
161 Simulated Annealing
162 Profiling
163 Assessing Quality of MSA
164 CLUSTALW
165 Phylogenetics
166 Phylogenetic Analysis
167 Phylogenetic Analysis and MSA
168 Evolutionary Tree
169 Moleculer Phylogenetics
170 Tree Reconstruction Methods
171 Maximum Parsimony Method
172 Maximum Likelihood Method
173 Distance Matrix
174 UPGMA
175 Fitch Morgolish Algorithm
176 Neighbor Joining Method
177 RNA Structure
178 RNA Secondary Structure
179 RNA Secondary Structure Prediction
180 Base Pair Maximization
181 Nussinov Algorithm
182 Transformational Grammars
183 Microarray
184 Microarray Types
185 Array Design
186 Array Applications
187 Experimental Design
188 Microarray Image Processing
189 Microarray Normalization
190 Within Array Normalization
191 Between Array Normalization
192 RNASeq
193 RNASeq Normalization
194 Data Analysis
195 t Test
196 t Test Example
197 Two sample t Test
198 Paired t Test
199 Multiplicity Issue
200 GO functional Analysis
201 Gene Networks
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