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BIF732 : Advanced Computing Approaches

Course Overview

Course Synopsis

This course is designed keeping in view the back ground of biological science students. The course focuses on the computing techniques used to solve the biological problems. The main techniques include Algorithms, Mathematical Models and R Language. Algorithms that will be utilized to find solution of the biological problems are Exhaustive Search (Brute Force) Algorithms, Dynamic Programming, Greedy Algorithm, Branch and Bound Algorithm, Divide and conquer Algorithm, Machine Learning and Randomized Algorithm. In Statistical models like Hidden Markov Model, recent applications like pair wise and multiple alignment, protein homology detection, protein structure prediction and genome annotation will be analyzed. The course also highlights the area of data analysis using R language.

Course Learning Outcomes

At the end of the course students will have better understandings of followings:

  • Explain various algorithm techniques used to solve common biological issues and problems
  • Design a basic algorithm for a given biological problem
  • Understand and analyze certain biological problems by using Hidden Markov Model
  • Analyze a genomics data sets and other large data sets by using R Language
  • Propose the solution to any biological problem with reference to computing techniques

Course Calendar

1 Introduction
2 History of Bioinformatics-I (Till 2000)
3 History of Bioinformatics-II (After 2000)
4 Applications of Bioinformatics-I
5 Applications of Bioinformatics-II
6 Cell organelles-I
7 Cell organelles-II
8 Central Dogma
9 DNA Structure
10 Replication of DNA
11 Structure of RNA
12 DNA Transcription
13 Protein Translation
14 Agorithm
15 First Algorithm-Multiplication of integers
16 Karatsuba Algorithm
17 Recursive Algorithm-Theory
18 Recursive Algorithm-Application
19 Sorting Algorithms-Classification
20 Sorting Algorithms-Example
21 Introduction to Bioinformatics Algorithm
22 Algorithm-Introduction
23 Simple algorithms operations-I
24 Simple algorithms operations-II
25 Pseudocode with simple example
26 Biological Algorithms versus Compute Algorithms-I
27 Biological Algorithms versus Computer Algorithms-II
28 Algorithm and complexity-The Change Problem-I
29 The Change Problem-II
30 Better Change Problem
31 Correct vs. Incorrect Algorithm
32 Brute Force change
33 Tower of Hanoi
34 Selection Sort
35 Big O notation
36 Algorithm Design Techniques I
37 Algorithm Design Techniques II
38 Dynamic Programming I
39 Dynamic Programming II
40 Algorithm for Dynamic Programming
41 Restriction Mapping I
42 Restriction Mapping II
43 Partial Digest Problem
44 Practical Restriction Mapping Algorithm
45 Partial Digest Algorithm I
46 Partial Digest Algorithm II
47 Regulatory Motifs
48 Profiles I
49 Profiles II
50 Profiles III
51 Motif Finding Problem I
52 Motif Finding Problem II
53 Search Trees-Introduction
54 Search Trees-Best Alternative
55 Algorithm for Search Trees I
56 Algorithm for Search Trees II
57 Next Vertex Algorithm-I
58 ByPass Algorithm
59 Finding motif
60 Simple Motif Search Algorithm.
61 Branch and bound algorithm.
62 Brute Force Median search.
63 Genomic rearrangements
64 Sorting by reversals.
65 Reversal distance problem.
66 Simple reversal sort algorithm.
67 Approximation Algorithms.
68 Approximation Algorithms-II
69 Approximation Algorithms-III
70 Breakpoint Reversal Sort Algorithm.
71 Theorem permutation-I
72 Theorem Permutation II
73 Improved breakpoint reversal sort algorithm
74 A greedy approach to motif search
75 Dynamic Programming,
76 The power of DNA sequence comparison.
77 Change Problem Revisited
78 Recursive Change Algorithm.
79 Dynamic Programming Algorithm.
80 Manhattan Tourist Problem.
81 Brute Force VS. Greedy Algorithm
82 Dynamic Programming.
83 Weight of the paths.
84 Dynamic Programming
85 Calculation of weights-I
86 Calculation of weights-II
87 Manhattan Tourist Algorithm.
88 Directed Acyclic Graphs.
89 Longest path in DAG problem.
90 DAG in daily life.
91 Sequence Similarity.
92 Edit Distance.
93 Alignment.
94 Edit Graph.
95 Edit graph-II
96 Longest Common Sequences.
97 Longest Common Sequences-II
98 Recurrence for LCS problem
99 Recurrence for LCS problem.
100 Global sequence alignment
101 Scoring Alignments-II
102 PAM Matrix
103 Local Sequence Alignment
104 Local Sequence Alignment II
105 Local Alignment Problem
106 Multiple Sequence Alignment
107 Dynamic Programming-I
108 Dynamic Programming-II
109 Progressive Multiple Alignment
110 Gene Prediction I
111 Gene Prediction II
112 One approach of Gene Prediction
113 Second Approach for Gene Prediction
114 Statistical Approach to Gene Prediction I
115 Statistical Approach to Gene Prediction II
116 Statistical Approach to Gene Prediction III
117 Similarity-Based Approaches to Gene Prediction
118 Similarity-Based Approaches to Gene Prediction I
119 Exon chaining problem
120 Graph Algorithm
121 Graph
122 Graph Terminology
123 4x4 Chess Board
124 Types of Graphs
125 Graph Theory
126 Eulerian Cycle Problem
127 Graph Theory in Chemistry
128 Exon Changing Algorithm
129 Hamiltonian Cycle Problem
130 Shortest Path Problem
131 Graphs and Genetics
132 Interval Graphs
133 DNA Sequencing I
134 DNA Sequencing II
135 Shortest Superstring Problem
136 DNA Array
137 Sequencing by Hybridization
138 SBH as a Hamiltonian Path Problem
139 SBH as an Eulerian Path Problem
140 Fragment Assembly in DNA Sequencing
141 Strategy for Sequencing
142 Protein Sequencing and Identification
143 Computational Protein Sequencing
144 Mass Spectrophotometry
145 The Peptide Sequencing Problem
146 The Peptide Sequencing Problem II.
147 Protein Sequencing and Identification.
148 Spectrum Graphs.
149 Spectrum Graphs I.
150 Protein Identification via Database Search.
151 SEQUEST Algorithm.
152 Modified Protein Identification Problem.
153 R and Bioconductor.
154 Installing extra packages.
155 Graphical user Interfaces
156 Basics of R language.
157 Commands.
158 Mathematical functions.
159 Data input and output.
160 Calculations with vectors.
161 Object types.
162 Generating sequence and repeats.
163 Searching, merging and transposition.
164 Sorting and ordering.
165 Loops
166 Plot.
167 Changing colors and symbols.
168 Histogram
169 Scatter and Panel plot
170 Graphical settings.
171 Adding new objects to graphs.
172 Affymetrix data (CEL files).
173 Agilent data.
174 Reading one color data files.
175 Illumina data.
176 Normalizing DNA microarray data.
177 Normalizing Affymetrix data.
178 Normalizing Agilent data.
179 Normalizing one and two color data.
180 Getting raw data and saving expression values.
181 Checking Affymetrix data
182 Checking Agilent data
183 One and two color data
184 Checking Illumina data.
185 Statistical Analysis.
186 Model matrix for a two group comparison
187 Model matrix for a three group comparison
188 Analysis using a linear model.
189 Differential expression and p-values.
190 Introduction- Gene set enrichment data.
191 Gene set enrichment analysis for GO categories.
192 Gene set enrichment analysis for KEGG
193 KEGG pathways.
194 GO categories.
195 Introduction- Annotation and clustering
196 Getting the reports
197 Heat map.
198 K-mean Clustering
199 Finding Optimal Number of Clustering
201 Visualizing the K-means clustering
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