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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

History of Bioinformatics2
Some recent events in the history of bionformatics3
Applications of Bioinformatics-I4
Applications of Bioinformatics - II5
Cell Organelles - I6
Cell Organelles - II7
Central Dogma8
DNA Structure9
Replication of DNA10
Structure of RNA11
DNA Transcription12
Protein Translation13
Multiplication of Integers15
Karatsuba multiplication16
Recursive Algorithm17
Recursive Algorithm - Application18
Types of Sorting Algorithms19
Sorting Algorithms20
Introduction to Bioinformatics Algorithm21
Simple Algorithms Operations - I23
Simple Algorithms Operations - II24
Pseudocode with Simple Example25
Biological Algorithsm Versus Computer Algorithsm - I26
Biological Algorithsm Versus Computer Algorithsm - II27
Algorithms and Complexity28
US Change Problem29
Better Change Problem30
Correct vs. Incorrect Algorithm31
Brute Force change32
Tower of Hanoi33
Selection Sort34
Big O notation35
Assignment No. 1
Algorithm Design Techniques I36
Algorithm Design Techniques II37
Dynamic Programming I38
Dynamic Programming II39
Algorithm for Dynamic Programming40
Restriction Mapping I41
Restriction Mapping II42
Partial Digest Problem43
Practical Restriction Mapping Algorithm44
Partial Digest Algorithm I45
Partial Digest Algorithm II46
Regulatory Motifs in DNA Sequences47
Profiles I48
Profiles II49
Profiles III50
Motif Finding Problem I51
Motif Finding Problem II52
Search Trees-Introduction53
Search Trees-Best Alternative54
Algorithm for Search Trees I55
Algorithm for Search Trees II56
Next Vertex Algorithm57
Bypass Algorithm58
Finding Motifs59
Simple Motif Search Algorithm60
Branch and bound Algorithm61
Brute Force Median Search62
Assignment No. 2
Genome Rearrangements63
Sorting by reversals64
Reversal Distance Problem65
Simple Reversal Sort Algorithm66
Approximation Algorithms I67
Approximation Algorithms II68
Approximation Algorithms III69
Breakpoint Reversal Sort Algorithm70
Theorem 1-Permutation I71
Theorem 1-Permutation II72
Theorem 2-Improved break point reversal sort73
A greedy approach to motif finding74
Dynamic Programming75
The Power of DNA Sequence Comparison76
The Change Problem Revisited77
Recursive Change Algorithm78
Dynamic Programming Algorithm79
Manhattan Tourist Problem I80
Brute Force vs Greedy Algorithm81
Dynamic Programing82
Weight of the paths83
Dynamic Programing84
Calculation of weights I85
Calculation of weights II86
Manhattan Tourist Algorithm87
Directed Acyclic Graphs88
Longest path in DAG problem89
DAG in daily life90
Sequence Similarity91
Edit Distance92
Edit Graph I94
Edit Graph II95
Longest Common Sequences I96
Longest Common Sequences II97
Recurrence for LCS problem I98
Recurrence for LCS problem II99
Global Sequence Alignment100
Mid Term Examinations
Scoring Alignments101
PAM matrix102
Local Sequence Alignment I103
Local Sequence Alignment II104
Local Alignment Problem105
Multiple Sequence Alignment106
Dynamic programming I107
Dynamic programming II108
Progressive Multiple Alignment109
Gene Prediction I110
Gene Prediction II111
One Approach for Gene Prediction112
Second Approach for Gene Prediction113
Statistcal Approach to Gene Prediction 1114
Statistical Approach to Gene Prediction 11115
Statistical Approach to Gene Prediction 111116
Similarity based Approached to Gene Prediction 1117
Similarity based Approached to Gene Prediction 11118
Gene Prediction119
Graph alogrithms120
Graph terminlogy122
4x4 Chess board123
Types of Graphs124
Graph theory125
Eulerian cycle Problem126
Graph theory in chemistry127
Exon Changing Alogrithm128
Hamiltonian Cycle Problem129
shortest path problem130
graph and genetics131
Assignment No. 3
interval graph132
DNA Sequencing 1133
DNA Sequencing 11134
DNA Sequencing 111135
DNA Array136
sequencing by hybridization137
SBH as a Hamiltonian Path Problem138
SBH as an Eulerian Path Problem139
Fragment Assemly in DNA sequencing140
Stategy for Sequencing141
protein sequence and identification142
computational protein sequencing143
Mass spectrophotometry144
The peptide sequencing problem 1145
The peptide sequencing problem 11146
protein sequencing and identification147
spectrum Graphs 1148
spectrum Graphs 11149
protein identification via database search150
sequest Alogrithm151
modified protein identification problem152
R and bioconductor153
Installing extra packages154
Graphical user interfaces155
Basics of R language156
Mathematical functions158
Data input and output159
Calculation with vectors160
Object types161
Generating sequencing and repeats162
sorting and ordering164
Changing color and symbols167
scatter and panel plot169
Graphiacl settings170
Adding new object to the graph171
Assignment No. 4
Affymetrix data (CEL files)172
Agilent data173
Reading one color data files174
Illumina Data175
Normalizing DNA microarray data176
Normalizing Affymetrix data177
Normalizing agilent Data178
Normalizing one and two color Data179
Getting raw data and saving expression values180
Checking Affymetrix Data181
Checking Agilent data182
one and two color data183
Checking illumina data184
Statistical analysis185
Model matrix for a two group comparison186
Model matrix for a three group comparison187
Ananlysis using a linear model188
Differential expression and p-value189
Introduction-gene enrichment dat190
Gene set enrichment analysis for Go categories191
Gene set enrichment analysis for KEGG192
KEGG pathways193
Go categories194
Introdution-Annotation and clustering195
Getting the Report196
Heat Map197
K-means clustering198
Finding optimal number of clusters199
Visualizing the K-means clustering200
Final Term Examinations
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