Topic outline

  • Welcome to Bioinformatics

        

    Loving students,

    Assalamu Alaikum. Welcome to Introduction to Bioinformatics course! I am Md. Rafidul Hasan Khan and I'll be your instructor of this course for the semester Fall 2022. Here we will discuss, learn, share and explore together to enrich ourselves. Wishing you a great experience!


    • Calender : Click here

    • Instructions/Guideline for the course:

      • All the students of section G, H, I for this course have to enroll in Moodle.
      • Students can find all the course materials from Moodle.
      • All the students have to submit the soft copy of their "Assignment" in Moodle under assignment section created here and for this they will be graded here.
      • One discussion or feedback forum is created under each of the lecture Students have to give their feedback on these forum and marks will be given for their feedback
      • Any announcement regarding the class will be posted on Moodle. So they have to keep themselves always active on Moodle.
      • All the quizzes and presentation will be conducted here and will be announced prior.
      •  The question pattern and the syllabus for the quizzes, midterm and final exam will be given prior that exam (quizzes, midterm and final).

    • Course Rationale:

       Bio-Informatics has been the most used methods of incorporating intelligence of biological world to computer science. It is therefore necessary to develop a good understanding of their operation and how they can be used as building blocks for computerized application of biology. This course explores the inner workings of a biological world from the programmer’s perspective by implementing different algorithms of Computer Science.


    • Course Objectives

      • To gather knowledge about biological world and relation with Computer Science and Engineering 
      • To analyze some existing methods and algorithms for specific biological problems
      • To grow the research interest in the field of Bioinformatics
      • To know the scope of bioinformatics research area and motivate them for research work.

    • Course Outcomes (CO’s): 

      • CO1: Understand the basics of Bioinformatics such as molecular and cellular biology, DNA sequencing, Gene duplication, Paralog, Ortholog, Homolog, Selectivity, Sensitivity, Phylogenetic Tree. 
      • CO2: Analyze algorithms for various existing methods for specific topic such as Global and Local Alignment, FASTA, HMM, Parsimony, Distance Approach, Maximum Likelihood Estimation. 
      • CO3: Ability to design and evaluate algorithms for specific biological problems. 


    • Assessment Plan File

      Grading Scheme

                         Attendance          :7%

                           Class Test          :15%

                       Presentation          :8%

                         Assignment         :5%

      Mid Term Examination        :25%

               Final Examination      :40%

                                Total            : 100%


    • Text Book Folder

      Book-1                                                 Book-2           

    • Reference Book Folder

      Book-1                                                            Book-2  

    • Course Outline:  Click Here

  • Week-1: Introduction to Bioinformatics


    • Topics to Discussion
      1.  Basics of Bioinformatics- Cell, Gene, Genome
      2.  Scope of Bioinformatics

      Expected Learning Outcome

      1.  Able to understand the relation between computer Scientists with Biologists
      2.  Able to grow research interest in this field

    • Lecture Content

    • Additional Materials

      • ▹Cell Structure -

    • ***Self Test***

      1. Write 5 differences between DNA and RNA.


  • Week-2: Molecular and Cellular Biology

    • Topics to Discussion
      1.  Basics of Molecular Biology-DNA, RNA
      2.  Structure of DNA and RNA, DNA Replication 
      3.  Cell Division- Mitosis and Meiosis

      Expected Learning Outcome

      1.  Able to understand the structural and functional difference between DNA and RNA
      2.  Able to know how the cell division is occurred in a cell and DNA Replication

    • Lecture Content

    • Additional Materials

      ▹DNA Structure -
      ▹DNA Replication - 


    • ***self test***
      Annotate the deoxyribose sugar, nitrogen bases, hydrogen bond and phosphate in the following figure.


  • Week-3: Gene Structure and Splicing

    • Topics to Discussion
      1.  Gene Structure, Gene regulation and Splicing  
      2.  DNA --> RNA --> Protein

      Expected Learning Outcome

      1.  Able to understand of Functional and non-functional parts of Gene 
      2.  Realize the process of Protein synthesis through translation and transcription process

    • Lecture Content

    • Additional Materials


      ▹Central Dogma - 


  • Week-4: DNA Sequencing

    • Topics to Discussion
      1.  Basic terminologies to perform DNA sequencing
      2.  DNA Sequencing Process- Sanger Method
      3. Different G      enerations of Sequencing

      Expected Learning Outcome

      1.  Able to understand the DNA Sequencing Process
      2.  How Gnome Sequencing is performed in different generations

    • Lecture Content

    • Additional Material

      ▹Sanger Sequencing -

  • Week-5: Sequence Alignment

    • Topics to Discussion
      1.  Sequence Alignment- why it is needed
      2. Sequence Alignment Method
      3. Global and Local Alignment for pairwise sequence 

      Expected Learning Outcome

      1.  Able to know Sequence Alignment, different methods of pairwise sequence
      2.  Able to perform Global and Local alignment for pairwise sequence

    • Lecture Content

    • Additional Materials

      ▹Global Alignment Part 1 -
      ▹Global Alignment Part 2 -
      ▹Local Alignment -

    • Self Test

      Implement Global Alignment algorithm on GCTCA & CTTA Where Gap, Match, and Mismatch are correspondingly -3, 2 & -1.

    • Assignment on Global Alignment
      Opened: Monday, 12 September 2022, 12:00 AM
      Due: Saturday, 17 September 2022, 11:59 PM
      Restricted Not available unless:
      • The activity **Self Test is marked complete
      • It is before 18 September 2022, 10:00 PM

  • Week-6: Multiple Sequence Alignment

    • Topics to Discussion
      1.  Multiple Sequence Alignment- importance, motivation, challenge
      2.  MSA methods-Dynamic Programming, Greedy Approach, Progressive, Iterative method

      Expected Learning Outcome

      1.  Able to know the importance, challenges of MSA 
      2.  Able to analyze different methods of MSA

    • Lecture Content

  • Topic 7: Presentation

    • Topic 8: MID Exam

      • Topic 9: All Quizzes

        • Week-8: Gene Duplication and Read Mapping

          • Topics to Discussion

            1.      Different types of mutations
            2.      Gene Duplication- Homolog, Ortholog, Paralog and Speciation
            3.      Read Mapping- Keyword tree, suffix tree, suffix array, Burrows Wheeler Transform

             Expected Learning Outcome

            1.      Able to know different types of mutations
            2.      Analyze and differentiate clearly ablout Homolog, Paralog, Ortholog and Speciation
            3.      Able to understand suffix tree, suffix array, Burrows wheeler transform

          • Lecture Content 

          • Video Content

            1. Gene Duplication


            2. Keyword Tree and Suffix Tree



            3. BWT, BWM, LF Mapping

          • **Self Test


        • Week-9: Database Searching

          • Topics to Discussion
            1. TP, TN, FP, FN
            2. Selectivity, Sensitivity
            3. Hash Table used in FASTA

            Expected Learning Outcome

            1.  Able to understand TP, TN, FP, FN, Sensitivity and Selectivity
            2. Able to create hash table in FASTA.

          • Lecture Content

            1.

            2.  Lecture 2


        • Week-10: HMM- Forward Algorithm

          • Topics to Discussion
            1.  Markov Chain Model, Notation
            2.  Probability of a Sequence for a Given Markov Chain Model
            3.  CpG Island
            4.  Hidden Markov Model- Forward Algorithm

            Expected Learning Outcome

            1.  Able to clear understand about Markov Chain Model, CpG Island
            2.  Able to implement Forward Algorithm using HMM

          • Lecture Content

        • Week 11: HMM-Viterbi Algorithm

          • Topics to Discussion
            1. Hidden Markov Model, Notations
            2. Implement of Hidden Markov Model - Viterbi Algorithm

            Expected Learning Outcome

            1.  Able to understand HMM
            2.  Able to apply Viterbi algorithm by using HMM

          • Lecture Content

        • Week-12: Maximum Likelihood Estimation

          • Topics to Discussion
            1.  Maximum Likelihood Estimation
            2.  Calculate Maximum Likelihood of a Phylogenetic Tree with known history

            Expected Learning Outcome

            1.  Able to understand Maximum Likelihood estimation
            2.  Able to calculate Maximum Likelihood of a Phylogenetic Tree with known history

          • Lecture Content

          • Final Assignment
            Opened: Wednesday, 30 November 2022, 12:00 AM
            Due: Tuesday, 6 December 2022, 11:59 PM
            Make a submission

        • Week 13

          • Quiz-4
            Opened: Friday, 16 December 2022, 9:30 PM
            Closed: Friday, 16 December 2022, 10:10 PM

        • Topic 16

          • Topic 17

            • Topic 18

              • Topic 19

                • Topic 20