BIO 3350: ELEMENTS OF BIOINFORMATICS PARTIALLY ONLINE SYLLABUS

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1 BIO 3350: ELEMENTS OF BIOINFORMATICS PARTIALLY ONLINE SYLLABUS NEW YORK CITY COLLEGE OF TECHNOLOGY The City University Of New York School of Arts and Sciences Biological Sciences Department Course title: Course code: BIO 3350 Credit Hours: Prerequisite: Text: Official Course Description (from the College Catalog) Elements of Bioinformatics 4 credit hours Course Information 4 hours combined laboratory and lecture per week; 2 hours extra lab modules per week; 15 weeks total. BIO 1101, MAT 1275, CUNY proficiency in reading and writing Optional text: Understanding Bioinformatics, 2008, by Zvelebil and Baum, Garland Science. The course will use various internet-based sources as reference text. This course develops awareness of Internet-based information, and encourages exploration and use of the wide range of databases available to those working in the field of Biology, Biotechnology and Pharmaceutical industries. Different tools and computational methods are used to analyze DNA, RNA and protein structures. The course is designed to meet the increasing demand for individuals skilled in using computers to manipulate and analyze the growing quantities of genetic information available to bioscientists and the medical profession. This course is run partially online. Many concepts and techniques taught in this course are internet and computer-based. Independent online laboratory work by students is therefore a natural component in learning Bioinformatics. Course Mechanics Parts of this course will be conducted online via Blackboard. Students must access their Blackboard accounts regularly. Assignments will be assigned periodically, and regular and active participation in discussions is required. Timely completion of assignments is critical to success in the course. The (face-to-face) component of the course will be held at the designated time and classroom (schedule to be provided). Attendance is absolutely required. Aside from serving as the venue to introduce new topics, the face-to-face meeting will also provide an opportunity for students to discuss any difficulty they are having regarding the online component of the course. Grading Procedure (see Grading Policies for details) Lecture: 40% Lab: 60% The lecture grade is based on 3 exams. The online lab grade is based on a number of short quizzes, completion of online and worksheet assignments, and a research project at the end of the semester. Class presentations of research projects and completion of all lab exercises are required.

2 Course Objectives and Student Expectations This is a partially online course. Students are expected to be able to work independently and regularly, as well as collaborate with fellow students on group projects. This upper-level course is fast paced, and covers a diverse set of topics, and therefore students must be able to keep up with the work assigned both online and in class, in order to be successful in the course. In addition, discussion boards will be utilized, in order to communicate with fellow students regarding current assignments and group projects. Upon completion of the course, the student will be able to: 1. use commonly available bioinformatics tools and software, such as sequence alignment protocols and other homology-based tools; Course Objectives 2. understand the theory and motivation behind commonly available bioinformatics tools and software, so that they will be able to judge the validity of the outputs provided by these tools and software. 3. navigate through internet-based biological databases, such as sequence, structure, and genome databases, to be able to answer bioinformatics-related questions; 4. utilize the internet as an information resource, and be able to discern truly useful and accurate information; 5. read scientific literature concerning topics in bioinformatics, and understand the specific methodologies and advances described therein. 1. Students should have access to and be able to use Internet Explorer, Firefox, or any appropriate web browser. Internet Explorer and Firefox work best with Blackboard. AOL users should maximize the Internet Explorer browser, as well. Technology Prerequisites Virtual Schedule 2. Students will need a City Tech account and should be comfortable using it. Students will also need access to CUNY s Blackboard service. Accounts and passwords to the CUNY Portal should be arranged prior to the beginning of the semester. 3. Students should check if their address on Blackboard is the address they check most. The instructor will send announcements only via Blackboard. At the beginning of the semester, the class will assemble for the first face-to-face meeting, where the instructor will explain in detail the policies and procedures of this partially online course. In addition, a short introduction on the features of Blackboard will be given on the first day of class. Instruction on other features of Blackboard (discussion board, wiki) will be provided either in lecture or online. Some of the online component of the course will be in the form of worksheet exercises. They will be found in the Assignments section of the course in

3 Blackboard. Because of the demanding amount of material that will be covered in the class, deadlines will be followed strictly. In addition, weekly participation in online discussions are required. These can be found in the Discussion Board section of Blackboard. The final research project will be group work. Topics will be assigned in advance, to allow ample time for research and preparation. Students will be encouraged to use the discussion boards and specifically designated group pages (in Blackboard) to brainstorm for ideas and organize them into concrete plans. CUNY s Blackboard resource can be accessed via the CUNY Portal, at: Online Resources A Beginner s Guide to Blackboard, as well as help on other resources such as Wiki and Wimba, can be found here: The National Center for Biotechnology Information, which hosts all the databases that will be used in this course, as well as tutorials on how to navigate around the website and the databases, can be found here: Easy access to all the online resources for Bioinformatics can be found here: LEARNING OUTCOMES FOR GEN ED, CUNY COMMON CORE, AND CUNY FLEXIBLE CORE GEN ED LEARNING OUTCOMES Students in this course will: 1. expand and deepen their knowledge by: learning to value knowledge and learning; understanding and appreciating the range of academic disciplines that constitute the interdisciplinary field of bioinformatics, and comprehending their relationships in this field of study; using this course as a forum for the study of values and ethical principles that are at play in the rapid development of the field of bioinformatics, as well as for the study of basic and applied sciences that inform the physical world. engaging in an in-depth, focused, and sustained program of study of bioinformatics; pursuing disciplined, inquiry-based learning in bioinformatics through online activities and lab exercises; and acquiring tools for lifelong learning, such as the proper and efficient ways to utilize existing scientific data and information to gain knowledge. 2. acquire and use tools needed for communication, inquiry, analysis, and productive work by:

4 communicating effectively using written, oral, and visual means; understanding and employing quantitative analysis to describe and solve bioinformatics problems, both independently and cooperatively; employing scientific reasoning and logical thinking; and using creativity to solve scientific problems. 3. work productively within the diverse discipline of bioinformatics and related fields by: gathering, interpreting, evaluating, and applying information discerningly from a variety of sources, as displayed in their final research projects; understanding and navigating complex systems that support research and practice in bioinformatics; and resolving complex issues creatively by employing multiple sources of information, systems, and tools. 4. understand and apply values and ethics in personal and professional domains by: demonstrating intellectual honesty and personal responsibility; demonstrating intellectual agility; working in teams effectively; and transforming biological information into knowledge. CUNY Common Core Learning Outcomes Students in this course student will: 1. identify and apply fundamental concepts and methods of biological and informatics sciences and applied math; 2. use tools of bioinformatics to carry out collaborative laboratory investigations; 3. gather, analyze, and interpret data and present it in effective written reports; and 4. identify and apply research ethics in gathering and reporting scientific data. CUNY Flexible Core Learning Outcomes Students in this course student will: 1. identify and apply fundamental concepts and methods of biological and informatics sciences and applied math to explore the scientific world; 2. demonstrate how tools of biological sciences and quantitative technologies to analyze problems and develop solutions; 3. articulate and evaluate the impact of genomic and computational technologies and biological discoveries on the contemporary worlds, such as issues of personal privacy, security, and ethical responsibilities; and 4. understand scientific principles underlying matters of policy or public concern in which science plays a role. Course Coordinator and Instructor Dr. Armando D. Solis Office: P-313 Phone: (718)

5 Week 5 Week 4 Week 3 Week 2 Week 1 Lecture and Laboratory Schedule Class Mechanics and Policies, and Introduction to Blackboard Features Molecular Biology I: Bioinformatics, DNA Structure, and the Central Dogma 1. What is Bioinformatics? 2. Structure and Function of Macromolecules: DNA and RNA 3. The Central Dogma of Molecular Biology 4. Biological Information Flow: Transcription, Translation, Base Complementarity LAB 1: The Central Dogma: How Information Flows from DNA to mrna to Protein Sequences Molecular Biology II: The Genetic Code and Gene Structure 1. The Genetic Code: How amino acid sequences arise from nucleotide sequences 2. Gene Structure and Control: Promoter Sequences, Introns and Exons, Reading Frames LAB 2: Probability, Random Sequences, and Promoter Sequences Molecular Biology III: Introduction to Protein Structure 1. Amino Acid Sequence and Protein Structure 2. Molecular Interactions Critical to Protein Stability 3. Protein Folding LAB 3: Open Reading Frames and Amino Acid Sequences Sequence Alignment I: Principles of Sequence Alignment 1. Sequence Homology and Biological Meaning 2. Dot Plots 3. Substitution Matrices, PAM Matrices, BLOSUM Matrices 4. Gaps and Gap Penalties, Initiation and Extension Penalties 5. Pairwise Sequence Alignments 6. Scoring Functions, Gap Penalties, Initiation and Extension Penalties LAB 4: Sequence Alignment and Phylogenetic Trees Sequence Alignment II: Dynamic Programming & Heuristic Alignment Methods 1. Dynamic Programming, Global and Local Alignments 2. Multiple Sequence Alignments, Consensus Alignments 3. BLAST, PSI-BLAST, FASTA LAB 5: Dynamic Programming: Global, Semi-Global, and Local Alignment

6 Week 10 Week 9 Week 8 Week 7 Week 6 Biological Databases I: Introduction to Databases 1. Structure of Databases: Records, Fields, Flat-File and Relational Databases 2. Types of Databases: Primary and Derived Data 3. Representation of Biological Sequence and Structure, File Formats 4. Data Quality and Data Use 5. National Center for Biotechnology Information (NCBI) and ENTREZ LAB 6: Protein Sequence Alignment Biological Databases II: USING the NCBI and Other Biological Databases 1. Nucleic Acid Sequence Databases, NCBI, EMBL Databases 2. Genome Databases, Ensembl, Gene Ontology 3. Protein Sequence Databases, Protein Information Resource, SWISS-PROT, TrEMBL 4. Protein Data Bank and Structure Databases 5. Protein Structure Families, CATH and SCOP Databases 6. PubMed, Scientific Literature LAB 7: BLAST and NCBI Tools Phylogeny I: Molecular Evolution and Phylogenetic Trees 1. Structure and Meaning of Phylogenetic Trees 2. Molecular Evolution 3. Phylogenetic Tree Reconstruction 4. Evolutionary Distance LAB 8: NCBI, PubMed, and Cancer Research Phylogeny II: Generating Phylogenetic Trees 1. Clustering Methods for Constructing Phylogenetic Trees: UPGMA and Fitch- Margoliash 2. Tree Topologies LAB 9: Generating Phylogenetic Trees Genomics I: Genome Features 1. Genome Sequences 2. Prokaryotic Gene Structure, Prokaryotic Genomes 3. Eukaryotic Gene Structure, Eukaryotic Genomes 4. Gene Prediction Algorithms 5. Genome Annotation and Gene Ontology 6. Large Genome Comparisons LAB 10: Gene Finding Tools

7 Week 15 Week 14 Week 13 Week 12 Week 11 Genomics II: Gene Detection and Genome Annotation 1. Gene Detection in Prokaryotes 2. Gene Detection in Eukaryotes 3. Predicting Eukaryotic Gene Signals LAB 11: Secondary Structure Prediction Methods Proteins I: Protein Structure and Structure Prediction 1. Energetics, Protein Stability and Folding, Hydrophobicity and other Forces 2. Secondary Structure Prediction 3. Homology Modeling 4. Fold Recognition and Threading LAB 12: The Protein Data Bank (PDB), Protein Structure Visualization Programs, Protein Folding Games Proteins II: Structure Prediction and Relevance to Biomedical Sciences 1. Conformational Energy Calculations and Molecular Dynamics 2. Protein Function Prediction 3. Proteomics and Systems Biology LAB 13: Research for Class Project: PubMed, Scientific Publications, and Literature Review Special Topics 1. Human Genome, Genetic Variations, SNPs 2. Cancer Bioinformatics 3. Drug Discovery and Pharmacogenetics 4. Human Origins and Human Evolution LAB 14: Effective Research Practices and Report Preparation Class Presentations 1. Presentation Topics to be Announced LAB 15: In-Class Project Presentations

8 LECTURE LABORATORY Grading Policies Please bear in mind that this course is an intensive 4-credit science course with a laboratory component. Student performance on this course will be evaluated as follows: ASSIGNMENT DESCRIPTION POINTS Online and In-Class Lab Exercises and Worksheets Quizzes Research Project: Class Presentation Research Project: Written Report Expected timely completion of each lab exercise and worksheet In class and take-home quizzes, Topics to be announced 35% 10% 10-minute in class presentation 5% 5-page written report of the research project 10% Class Participation Weekly attendance and online participation 8% Exam 1 Molecular Biology 8% Exam 2 Sequence Alignment and Genomics 8% Exam 3 Protein Structure Prediction 8% Exam 4 Final Exam 8% NOTE: Letter grades will be determined using a standard percentage point evaluation as outlined below: A: A-: B+: B: B-: C+: C D: F: Below 60

9 Policy on Academic Integrity Academic dishonesty includes any act that is designed to obtain fraudulently, either for oneself or for someone else, academic credit, grades, or any other form of recognition that was not properly earned. Academic dishonesty, which will not be tolerated in this course and at City Tech, encompasses the following: Cheating Plagiarism Course Policy on Academic Integrity Defined as intentionally giving, receiving, using or attempting to use unauthorized materials, information, notes, study aids, including any form of unauthorized communication, in any academic exercise. It is the student s responsibility to consult with instructors to determine whether or not a study aid or device may be used. Plagiarism is intentionally and knowingly presenting the ideas or works of another as one s own original idea or works in any academic exercise without proper acknowledgement of the source. The purchase and submission of a term paper, essay, or other written assignment to fulfill the requirements of a course, and violates section 213-b of the State Education Law. This also applies to the submission of all or substantial portions of the same academic work previously submitted by the student or any other individual for credit at another institution, or in more than one course. Cheating and plagiarism will not be tolerated in this course. Penalties are the following. Cheating in in-class exams or quizzes will merit an automatic zero for the exercise. Copying from classmates lab worksheets and other take-home or online assignments will also merit an automatic zero for the exercise. Repeated violations will be reported to the Chair and the Dean, and may result in a final grade of F in the course, or even expulsion from the College. If you are unsure whether any of your actions constitute cheating or plagiarism, please consult the instructor for guidance.

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