Introduction | 1 | | |

History of Bioinformatics | 2 | | |

Some recent events in the history of bionformatics | 3 | | |

Applications of Bioinformatics-I | 4 | | |

Applications of Bioinformatics - II | 5 | | |

Cell Organelles - I | 6 | | |

Cell Organelles - II | 7 | | |

Central Dogma | 8 | | |

DNA Structure | 9 | | |

Replication of DNA | 10 | | |

Structure of RNA | 11 | | |

DNA Transcription | 12 | | |

Protein Translation | 13 | | |

Algorithm | 14 | | |

Multiplication of Integers | 15 | | |

Karatsuba multiplication | 16 | | |

Recursive Algorithm | 17 | | |

Recursive Algorithm - Application | 18 | | |

Types of Sorting Algorithms | 19 | | |

Sorting Algorithms | 20 | | |

Introduction to Bioinformatics Algorithm | 21 | | |

algorithm-Introduction | 22 | | |

Simple Algorithms Operations - I | 23 | | |

Simple Algorithms Operations - II | 24 | | |

Pseudocode with Simple Example | 25 | | |

Biological Algorithsm Versus Computer Algorithsm - I | 26 | | |

Biological Algorithsm Versus Computer Algorithsm - II | 27 | | |

Algorithms and Complexity | 28 | | |

US Change Problem | 29 | | |

Better Change Problem | 30 | | |

Correct vs. Incorrect Algorithm | 31 | | |

Brute Force change | 32 | | |

Tower of Hanoi | 33 | | |

Selection Sort | 34 | | |

Big O notation | 35 | | |

Algorithm Design Techniques I | 36 | | |

Algorithm Design Techniques II | 37 | | |

Dynamic Programming I | 38 | | |

Dynamic Programming II | 39 | | |

Algorithm for Dynamic Programming | 40 | | |

Restriction Mapping I | 41 | | |

Restriction Mapping II | 42 | | |

Partial Digest Problem | 43 | | |

Practical Restriction Mapping Algorithm | 44 | | |

Partial Digest Algorithm I | 45 | | |

Partial Digest Algorithm II | 46 | | |

Regulatory Motifs in DNA Sequences | 47 | | |

Profiles I | 48 | | |

Profiles II | 49 | | |

Profiles III | 50 | | |

Motif Finding Problem I | 51 | | |

Motif Finding Problem II | 52 | | |

Search Trees-Introduction | 53 | | |

Search Trees-Best Alternative | 54 | | |

Algorithm for Search Trees I | 55 | | |

Algorithm for Search Trees II | 56 | | |

Next Vertex Algorithm | 57 | | |

Bypass Algorithm | 58 | | |

Finding Motifs | 59 | | |

Simple Motif Search Algorithm | 60 | | |

Branch and bound Algorithm | 61 | | |

Brute Force Median Search | 62 | | |

Genome Rearrangements | 63 | | |

Sorting by reversals | 64 | | |

Reversal Distance Problem | 65 | | |

Simple Reversal Sort Algorithm | 66 | | |

Approximation Algorithms I | 67 | | |

Approximation Algorithms II | 68 | | |

Approximation Algorithms III | 69 | | |

Breakpoint Reversal Sort Algorithm | 70 | | |

Theorem 1-Permutation I | 71 | | |

Theorem 1-Permutation II | 72 | | |

Theorem 2-Improved break point reversal sort | 73 | | |

A greedy approach to motif finding | 74 | | |

Dynamic Programming | 75 | | |

The Power of DNA Sequence Comparison | 76 | | |

The Change Problem Revisited | 77 | | |

Recursive Change Algorithm | 78 | | |

Dynamic Programming Algorithm | 79 | | |

Manhattan Tourist Problem I | 80 | | |

Brute Force vs Greedy Algorithm | 81 | | |

Dynamic Programing | 82 | | |

Weight of the paths | 83 | | |

Dynamic Programing | 84 | | |

Calculation of weights I | 85 | | |

Calculation of weights II | 86 | | |

Manhattan Tourist Algorithm | 87 | | |

Directed Acyclic Graphs | 88 | | |

Longest path in DAG problem | 89 | | |

DAG in daily life | 90 | | |

Sequence Similarity | 91 | | |

Edit Distance | 92 | | |

Alignment | 93 | | |

Edit Graph I | 94 | | |

Edit Graph II | 95 | | |

Longest Common Sequences I | 96 | | |

Longest Common Sequences II | 97 | | |

Recurrence for LCS problem I | 98 | | |

Recurrence for LCS problem II | 99 | | |

Global Sequence Alignment | 100 | | |

Scoring Alignments | 101 | | |

PAM matrix | 102 | | |

Local Sequence Alignment I | 103 | | |

Local Sequence Alignment II | 104 | | |

Local Alignment Problem | 105 | | |

Multiple Sequence Alignment | 106 | | |

Dynamic programming I | 107 | | |

Dynamic programming II | 108 | | |

Progressive Multiple Alignment | 109 | | |

Gene Prediction I | 110 | | |

Gene Prediction II | 111 | | |

One Approach for Gene Prediction | 112 | | |

Second Approach for Gene Prediction | 113 | | |

Statistcal Approach to Gene Prediction 1 | 114 | | |

Statistical Approach to Gene Prediction 11 | 115 | | |

Statistical Approach to Gene Prediction 111 | 116 | | |

Similarity based Approached to Gene Prediction 1 | 117 | | |

Similarity based Approached to Gene Prediction 11 | 118 | | |

Gene Prediction | 119 | | |

Graph alogrithms | 120 | | |

Graph | 121 | | |

Graph terminlogy | 122 | | |

4x4 Chess board | 123 | | |

Types of Graphs | 124 | | |

Graph theory | 125 | | |

Eulerian cycle Problem | 126 | | |

Graph theory in chemistry | 127 | | |

Exon Changing Alogrithm | 128 | | |

Hamiltonian Cycle Problem | 129 | | |

shortest path problem | 130 | | |

graph and genetics | 131 | | |

interval graph | 132 | | |

DNA Sequencing 1 | 133 | | |

DNA Sequencing 11 | 134 | | |

DNA Sequencing 111 | 135 | | |

DNA Array | 136 | | |

sequencing by hybridization | 137 | | |

SBH as a Hamiltonian Path Problem | 138 | | |

SBH as an Eulerian Path Problem | 139 | | |

Fragment Assemly in DNA sequencing | 140 | | |

Stategy for Sequencing | 141 | | |

protein sequence and identification | 142 | | |

computational protein sequencing | 143 | | |

Mass spectrophotometry | 144 | | |

The peptide sequencing problem 1 | 145 | | |

The peptide sequencing problem 11 | 146 | | |

protein sequencing and identification | 147 | | |

spectrum Graphs 1 | 148 | | |

spectrum Graphs 11 | 149 | | |

protein identification via database search | 150 | | |

sequest Alogrithm | 151 | | |

modified protein identification problem | 152 | | |

R and bioconductor | 153 | | |

Installing extra packages | 154 | | |

Graphical user interfaces | 155 | | |

Basics of R language | 156 | | |

Commands | 157 | | |

Mathematical functions | 158 | | |

Data input and output | 159 | | |

Calculation with vectors | 160 | | |

Object types | 161 | | |

Generating sequencing and repeats | 162 | | |

Searching,merging,transpostion | 163 | | |

sorting and ordering | 164 | | |

loops | 165 | | |

plot | 166 | | |

Changing color and symbols | 167 | | |

histogram | 168 | | |

scatter and panel plot | 169 | | |

Graphiacl settings | 170 | | |

Adding new object to the graph | 171 | | |

Affymetrix data (CEL files) | 172 | | |

Agilent data | 173 | | |

Reading one color data files | 174 | | |

Illumina Data | 175 | | |

Normalizing DNA microarray data | 176 | | |

Normalizing Affymetrix data | 177 | | |

Normalizing agilent Data | 178 | | |

Normalizing one and two color Data | 179 | | |

Getting raw data and saving expression values | 180 | | |

Checking Affymetrix Data | 181 | | |

Checking Agilent data | 182 | | |

one and two color data | 183 | | |

Checking illumina data | 184 | | |

Statistical analysis | 185 | | |

Model matrix for a two group comparison | 186 | | |

Model matrix for a three group comparison | 187 | | |

Ananlysis using a linear model | 188 | | |

Differential expression and p-value | 189 | | |

Introduction-gene enrichment dat | 190 | | |

Gene set enrichment analysis for Go categories | 191 | | |

Gene set enrichment analysis for KEGG | 192 | | |

KEGG pathways | 193 | | |

Go categories | 194 | | |

Introdution-Annotation and clustering | 195 | | |

Getting the Report | 196 | | |

Heat Map | 197 | | |

K-means clustering | 198 | | |

Finding optimal number of clusters | 199 | | |

Visualizing the K-means clustering | 200 | | |