Introduction, History of Bioinformatics-I (Till 2000), History
of Bioinformatics-II (After 2000), Applications of Bioinformatics-I, Applications of Bioinformatics-II, Cell organelles-I, Cell organelles-II, Central Dogma, DNA Structure, Replication
of DNA, Structure of RNA, DNA Transcription, Protein Translation, First
Algorithm-Multiplication of integers, Karatsuba Algorithm, Recursive
Algorithm-Theory, Recursive Algorithm-Application, Sorting
Algorithms-Classification, Sorting Algorithms-Example, Introduction to Bioinformatics Algorithm, Bioinformatics
algorithm-Example, Simple algorithms operations-I,
Simple algorithms operations-II, Pseudocode with simple example, Biological
Algorithms versus Computer, algorithms-I, Biological Algorithms versus Computer
algorithms-II, Algorithm and
complexity-The Change Problem-I, The Change Problem-II, Better Change Problem, Correct
versus incorrect algorithm, Brute Force change, Tower of Hanoi, Selection Sort,
Big O notation, Algorithm Design Techniques I, Algorithm Design Techniques II, Restriction
Mapping I, Restriction Mapping II, Dynamic Programming I Partial Digest Problem,
Dynamic Programming II, Algorithm for Dynamic Programming, Partial Digest
Problem, Practical Restriction Mapping Algorithm, Partial Digest Algorithm I, Partial
Digest Algorithm II, Regulatory Motifs, Profiles I, Profiles II, Profiles III, Motif
Finding Problem I, Motif Finding Problem II, Search Trees-Introduction, Search Trees-Best Alternative, Algorithm for
Search Trees I, Algorithm for Search Trees II, Next Vertex Algorithm, Bypass
Algorithm , Finding Motifs, Simple Motif Search Algorithm, Branch and bound Algorithm, Brute Force Median Search, Genome
Rearrangements, Sorting by reversals, Reversal Distance Problem, Simple Reversal Sort Algorithm, Approximation
Algorithms , Breakpoint Reversal Sort Algorithm, Theorem – Permutation, A
greedy approach to motif finding, The Power of DNA Sequence Comparison, The Change Problem Revisited, Recursive Change
Algorithm, Dynamic Programming Algorithm, Manhattan Tourist Problem I, Brute
Force vs Greedy Algorithm, Weight of the paths, 85. Calculation of weights, Manhattan
Tourist Algorithm, Directed Acyclic Graphs, Directed Acyclic Graphs, DAG in
daily life