Chen, Jin

BMI633_F20 Syllabus

UNIVERSITY OF KENTUCKY

COLLEGE OF MEDICINE

            Last modified: 10/14/2019

_______________________________________________________________________

Course Syllabus

BMI 633: Introduction to Bioinformatics

Fall 2020

________________________________________________________________________

CLASS MEETINGS AND LOCATION:

Hybrid (MDS Room 221 & Zoom)

Mondays & Wednesdays: 1:00 PM - 2:30 PM, EST

________________________________________________________________________

Contact information

            Instructor:       Dr. Jin Chen

                                    Associate Professor,

                                    Department of Internal medicine

                                    Department of Computer Science

                                    230D MDS Building, 725 Rose Street

            Telephone:      859-218-5936

            E-mail:            chen.jin@uky.edu (preferred method of contact)

            Office Hours:  Monday: 2:30 PM to 4:20 PM (at Zoom by appointment)

Zoom Link: https://uky.zoom.us/j/93891299090?pwd=MThPcVhqSTllOEZTU2x0d2FHSDJXZz09

Zoom Password: IBIUKY

________________________________________________________________________

Course description

BMI 633 is a required course of the UK Data Science Master Program, Biomedical Informatics Concentration. It is geared towards a multi-disciplinary audience with an interest in applying the principles of information sciences for obtaining insight into biological processes and systems.

Regarding the teaching style, it is half lecturing and half tutorial. In the class, the lecturer will start by introducing algorithms and software tools in bioinformatics ranging from widely used sequence alignment to emerging deep learning models. Instead of guiding students to run software using the command line or through a GUI, the lecturer will start with needs and motivations, followed with a detailed elaboration of computational or statistical models, ending with experimental results on real data. Topics covered will include sequence alignment, scoring matrices, next-generation sequencing, knowledge discovery from high-throughput assays, systems biology, and molecular diagnostics. In the second half of the class, students will play the major tole. They will do small projects individually to practice what they have just learned. Students will write programs and run tasks such as software installation and parameterization, server configuration, job scheduling, and result visualization on their computers. R, MATLAB, and Python are encouraged in the class.

Although we won't go deeper to touch mathematical foundations and proofs, the lecturer does expect students, upon finishing the class, can obtain hands-on coding experiences, form a strong foundation in bioinformatics, and is proficient in using bioinformatics approaches for investigating novel biological paradigms. Due to its unique teaching style, BMI 633 meets the expectations of many graduate students. The overall course score is 4.8 out of 5.0.

In the normal situation, BMI 633 will be taught in a computer lab (MDS 221) where every student is equipped with a computer with essential software installed. Due to the COVID-19 pandemic, the course was taught online in the second half of the 2020 Spring semester, and will be taught using the hybrid teaching mode in the 2020 Fall semester. Students can choose to attend the in-person class at MDS Room 221 or join the Zoom session. All the Zoom sessions will be recorded. The capacity of MDS 221 computer lab is 10.

Course rationale:

Bioinformatics is the use of information science to understand complex biological processes and systems.  The need for bioinformatics has increased over time attributed to the explosion of high-throughput biological assays, integration of multiple data sources, system-level abstractions of signaling mechanism and molecular diagnostics. Bioinformatics has routinely resulted in biological breakthroughs as it often leads to discovering undocumented associations and hypothesis generation.  This course is also an essential ingredient of biomedical informatics.

 Course prerequisites

Undergraduate level courses in Life Sciences, Computer Programming, Statistics, and Mathematics are desirable.  Appreciation for life sciences as well as information sciences with a desire to learn in a multidisciplinary environment.

Course objectives

1. Introduce students from biological, statistical, clinical and information science backgrounds to bioinformatics concepts and tools.
2. Implement the necessary approaches and analyze real biological data sets hands-on in an open-source environment such as R and python.
3. Become proficient in the analysis and interpretation of high-throughput molecular assays.

Student learning outcomes:

Upon successful completion of this course, students are expected to be familiar in the following areas:

1. Fundamental concepts in bioinformatics
2. Using open-source software environments and bioinformatics tools for investigating various experimental data sets
3. Familiarity with high-throughput biological assays and their analysis
4. Using bioinformatics and systems biology approaches for their own research

Textbooks

Not Required. Given the interdisciplinary content of bioinformatics courses with the audience from distinct backgrounds, the course contents will be based on the lecture notes. However, you are encouraged to read the following textbooks:

• Compeau, Phillip, and P. A. Pevzner. Bioinformatics Algorithms: An Active Learning Approach. La Jolla. CA: Active Learning Publishers, 2018.

• Mount, David W., and David W. Mount. Bioinformatics: sequence and genome analysis. Vol. 564. Cold Spring Harbor, NY: Cold spring harbor laboratory press, 2001.

• Baldi, Pierre, Søren Brunak, and Francis Bach. Bioinformatics: the machine learning approach. MIT press, 2001.

• Durbin, Richard, et al. Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge university press, 1998.

Course requirements and learner evaluation

Course grades will be based upon evaluation of the following activities (tentative):

40 points Four Assignments (10 points each)

20 points Midterm exam

30 points Final project/presentation

10 points Attendance and Participation

Points Grading scheme:

100-90=A

  89-80=B

  79-70=C 

    0-69=E  

Instructor expectations

1. Attendance and active participation in the class and counts towards your grades.
2. Lectures will be taught in a manner so as to enable students to use what they have learned in the subsequent sections. Therefore, understanding earlier lectures and completing the related assignments is critical for meaningful progress.
3. As a part of the assignments please be prepared to present your work using power-point presentations and demonstration of the working computer codes.
4. You are encouraged to use spell check and grammar check prior to submitting your written work. The Writing Laboratory is available to anyone who may need assistance.
5. Independent and critical thinking is highly encouraged. Faithful reproduction of learning materials from any source including the lectures is strongly discouraged.

Academic honesty

Academic honesty is highly valued at the University.  You must always submit work that represents your original words or ideas.  If any words or ideas used in a class assignment submission do not represent your original words or ideas, you must cite all relevant sources and make clear the extent to which such sources were used.  Words or ideas that require citation include, but are not limited to, all hard copy or electronic publications, whether copyrighted or not, and all verbal or visual communication when the content of such communication clearly originates from an identifiable source.  Per university policy, students shall not plagiarize, cheat, or falsify or misuse academic records. Students are expected to adhere to University policy on cheating and plagiarism in all courses.  The minimum penalty for a first offense is a zero on the assignment on which the offense occurred.  If the offense is considered severe or the student has other academic offenses on their record, more serious penalties, up to suspension from the university, may be imposed.  All incidents of cheating and plagiarism are taken very seriously at the University of Kentucky, and there are specific policies and procedures in place to prosecute them. See S.R. 6.3.0 (PDF) for the exact Senate Rules regarding academic offenses.

Accommodations

If you have a documented disability that requires academic accommodations, please see me as soon as possible during scheduled office hours.  In order to receive accommodations in this course, submit to me a Letter of Accommodation from the Disability Resource Center.  If you have not already done so, please register with the Disability Resource Center for coordination of campus disability services available to students with disabilities.  Contact Jake Karnes via email at jkarnes@email.uky.edu or by telephone 859-257-2754.  You may also visit the DRC website for information on how to register for services as a student with a disability:  http://www.uky.edu/StudentAffairs/DisabilityResourceCenter/      

Religious Observances

Students will be given the opportunity to make up work (typically, exams or assignments) when students notify their instructor that religious observances prevent the student from completing assignments according to deadlines stated in this syllabus.  Students must notify the course instructor at least two weeks prior to such an absence and propose how to make up the missed academic work.

Inclement weather

The University of Kentucky has a detailed policy for decisions to close in inclement weather.  This policy is described in detail at http://www.uky.edu/PR/News/severe_weather.htm or you can call (859) 257-1754.

Late work policy

Assignments that are turned in late will be marked one letter grade lower unless prior approval from the instructor has been obtained.  It will be based on the time stamp provided by Blackboard. (NOTE: Assignments more than one week past the original due date will not be graded.)

Excused absences policy

Attendance, excused absences and make-up opportunities for this course will conform to the course policies established by the Office of Academic Ombud Services as found at www.uky.edu/Ombud/policies.php.

Other University Resources

The UK Violence Intervention and Prevention (VIP) Center provides advocacy services to women survivors of violence in the UK community.  The VIP Center can provide assistance in accessing and navigating services, resources and referrals both on and off campus.  Services include: academic advocacy, medical advocacy, counseling, financial advocacy, referral advocacy, and other practical needs that a student may request.  106 Frazee Hall, 257-3574 or 257-3564. http://www.uky.edu/StudentAffairs/VIPCenter/index.html# 

Face Covering/Distancing Policy

The Senate Council endorses the following recommended syllabus statements for faculty teaching face-to-face courses.

• In accordance with University guidelines, students must wear UK-approved face coverings in the classroom and academic buildings (e.g., faculty offices, laboratories, libraries, performance/design studios, and common study areas where students might congregate). If UK-approved face coverings are not worn over the nose and mouth, students will be asked to leave the classroom.
• Students should complete their daily online wellness screening before accessing university facilities and arriving to class.
• Students should not move chairs or barriers in classrooms and should socially distance at all times, leaving a six (6) foot radius from other people. Masks and hand sanitizer can be found at specific location in building if needed.
• Students should leave enough space when entering and exiting a room. Students should not crowd doorways at the beginning or end of class.
• The instructor may choose to remove a mask when pedagogically necessary at the front of the classroom and behind a clear barrier. The instructor's mask will be replaced when it is no longer necessary to have it removed, or when the class meeting is complete.

Class Recording Notification

The University of Kentucky Student Code of Conduct defines Invasion of Privacy as using electronic or other devices to make a photographic, audio, or video record of any person without their prior knowledge or consent when such a recording is likely to cause injury or distress.

Meetings of this course may be recorded. All video and audio recordings of lecturers and class meetings, provided by the instructors, are for educational use by students in this class only. They are available only through the Canvas shell for this course and are not to be copied, shared, or redistributed.

As addressed in the Student Code of Conduct, students are expected to follow appropriate university policies and maintain the security of linkblue accounts used to access recorded class materials. Recordings may not be reproduced, shared with those not enrolled in the class, or uploaded to other online environments.

If the instructor or a University of Kentucky office plans any other uses for the recordings, beyond this class, students identifiable in the recordings will be notified to request consent prior to such use. In anticipation of such cases, students may be asked to complete an “authorization of use” form by a faculty member.

Video and audio recordings by students are not permitted during the class unless the student has received prior permission from the instructor. Any sharing, distribution, and or uploading of these recordings outside of the parameters of the class is prohibited. Students with specific recording accommodations approved by the Disability Resource Center should present their official documentation to the instructor.

All content for this course, including handouts, assignments, and lectures are the intellectual property of the instructors and cannot be reproduced or sold without prior permission from the instructors. A student may use the material for reasonable educational and professional purposes extending beyond this class, such as studying for a comprehensive or qualifying examination in a degree program, preparing for a professional or certification examination, or to assist in fulfilling responsibilities at a job or internship.

Course Syllabus

Students Registered:

Al Mubarak A Adeliyi

Isaac Nakamura

Brien Washington

Feitong Lei

Jacob A Mackie

Jingwei Duan

Jiyeon Park

Kevin Xie

Quan Chen

Stuart Carter

Tajbir Raihan

Yash Rakesh Mishra

_______________________________________________________________________

BMI633_S20 - Postponed

UNIVERSITY OF KENTUCKY

COLLEGE OF MEDICINE

            Last modified: 10/14/2019

_______________________________________________________________________

Course Syllabus

BMI 633: Introduction to Bioinformatics

Spring 2020

________________________________________________________________________

CLASS MEETINGS AND LOCATION:

Multi-Disciplinary Science Building, MDS 221,

Mondays & Wednesdays: 1:00 PM - 2:30 PM, EST

________________________________________________________________________

Contact information

            Instructor:       Dr. Jin Chen

                                    Associate Professor, Division of Biomedical Informatics

                                    Department of Internal medicine

                                    Department of Computer Science

                                    230D MDS Building, 725 Rose Street

            Telephone:      859-218-5936

            E-mail:             chen.jin@uky.edu (Preferred method of contact)

            Office Hours:  Monday: 2:30 PM to 4:20 PM (By appointment/email)

________________________________________________________________________

Course description

BMI 633 is a required course of the UK Data Science Master Program, Biomedical Informatics Concentration. BMI 633 is geared towards a multi-disciplinary audience with an interest in applying the principles of information sciences for obtaining insight into biological processes and systems. Topics covered will include sequence alignment, scoring matrices, next-generation sequencing, knowledge discovery from high-throughput assays, systems biology, and molecular diagnostics. Programming using open-source languages such as R and Python will be taught in the class. Upon successful completion, students will have a strong foundation in bioinformatics and proficient in using bioinformatics approaches for investigating novel biological paradigms.

Course rationale:

Bioinformatics is the use of information science to understand complex biological processes and systems.  The need for bioinformatics has increased over time attributed to the explosion of high-throughput biological assays, integration of multiple data sources, system-level abstractions of signaling mechanism and molecular diagnostics. Bioinformatics has routinely resulted in biological breakthroughs as it often leads to discovering undocumented associations and hypothesis generation.  This course is also an essential ingredient of biomedical informatics.

 Course prerequisites

Undergraduate level courses in Life Sciences, Computer Programming, Statistics, and Mathematics are desirable.  Appreciation for life sciences as well as information sciences with a desire to learn in a multidisciplinary environment.

Course objectives

1. Introduce students from biological, statistical, clinical and information science backgrounds to bioinformatics concepts and tools.
2. Implement the necessary approaches and analyze real biological data sets hands-on in an open-source environment such as R and python.
3. Become proficient in the analysis and interpretation of high-throughput molecular assays.

Student learning outcomes:

Upon successful completion of this course, students are expected to be familiar in the following areas:

1. Fundamental concepts in bioinformatics
2. Using open-source software environments and bioinformatics tools for investigating various experimental data sets
3. Familiarity with high-throughput biological assays and their analysis
4. Using bioinformatics and systems biology approaches for their own research

Textbooks

Not Required.

Given the interdisciplinary content of bioinformatics courses with the audience from distinct backgrounds, the course contents will be based on the lecture notes. Supplementary References will be provided as required.

Course requirements and learner evaluation

Course grades will be based upon evaluation of the following activities (tentative):

40 points Four Assignments (10 points each)

20 points Midterm exam

30 points Final project/presentation

10 points Attendance and Participation

Points Grading scheme:

100-90=A

  89-80=B

  79-70=C 

    0-69=E  

Instructor expectations

1. Attendance and active participation in the class and counts towards your grades.
2. Lectures will be taught in a manner so as to enable students to use what they have learned in the subsequent sections. Therefore, understanding earlier lectures and completing the related assignments is critical for meaningful progress.
3. As a part of the assignments please be prepared to present your work using power-point presentations and demonstration of the working computer codes.
4. You are encouraged to use spell check and grammar check prior to submitting your written work. The Writing Laboratory is available to anyone who may need assistance.
5. Independent and critical thinking is highly encouraged. Faithful reproduction of learning materials from any source including the lectures is strongly discouraged.

Academic honesty

Academic honesty is highly valued at the University.  You must always submit work that represents your original words or ideas.  If any words or ideas used in a class assignment submission do not represent your original words or ideas, you must cite all relevant sources and make clear the extent to which such sources were used.  Words or ideas that require citation include, but are not limited to, all hard copy or electronic publications, whether copyrighted or not, and all verbal or visual communication when the content of such communication clearly originates from an identifiable source.  Per university policy, students shall not plagiarize, cheat, or falsify or misuse academic records. Students are expected to adhere to University policy on cheating and plagiarism in all courses.  The minimum penalty for a first offense is a zero on the assignment on which the offense occurred.  If the offense is considered severe or the student has other academic offenses on their record, more serious penalties, up to suspension from the university, may be imposed.  All incidents of cheating and plagiarism are taken very seriously at the University of Kentucky, and there are specific policies and procedures in place to prosecute them. See S.R. 6.3.0 (PDF) for the exact Senate Rules regarding academic offenses.

Accommodations

If you have a documented disability that requires academic accommodations, please see me as soon as possible during scheduled office hours.  In order to receive accommodations in this course, submit to me a Letter of Accommodation from the Disability Resource Center.  If you have not already done so, please register with the Disability Resource Center for coordination of campus disability services available to students with disabilities.  Contact Jake Karnes via email at jkarnes@email.uky.edu or by telephone 859-257-2754.  You may also visit the DRC website for information on how to register for services as a student with a disability:  http://www.uky.edu/StudentAffairs/DisabilityResourceCenter/      

Religious Observances

Students will be given the opportunity to make up work (typically, exams or assignments) when students notify their instructor that religious observances prevent the student from completing assignments according to deadlines stated in this syllabus.  Students must notify the course instructor at least two weeks prior to such an absence and propose how to make up the missed academic work.

Inclement weather

The University of Kentucky has a detailed policy for decisions to close in inclement weather.  This policy is described in detail at http://www.uky.edu/PR/News/severe_weather.htm or you can call (859) 257-1754.

Late work policy

Assignments that are turned in late will be marked one letter grade lower unless prior approval from the instructor has been obtained.  It will be based on the time stamp provided by Blackboard. (NOTE: Assignments more than one week past the original due date will not be graded.)

Excused absences policy

Attendance, excused absences and make-up opportunities for this course will conform to the course policies established by the Office of Academic Ombud Services as found at www.uky.edu/Ombud/policies.php.

Other University Resources

The UK Violence Intervention and Prevention (VIP) Center provides advocacy services to women survivors of violence in the UK community.  The VIP Center can provide assistance in accessing and navigating services, resources and referrals both on and off campus.  Services include: academic advocacy, medical advocacy, counseling, financial advocacy, referral advocacy, and other practical needs that a student may request.  106 Frazee Hall, 257-3574 or 257-3564. http://www.uky.edu/StudentAffairs/VIPCenter/index.html# 

Course Syllabus

Students Registered:

Butts, Jordan L

Clark, Justin A

Plaugher, Daniel R

Liu, Xuhui

McLetchie, Sheldon K

Saghaeiannejad-Esfahani, Hoda

Tapia, Andrew C

Fan, Junkai

Cochran, Jarad P

Hafig, Ghosown A

Kaur, Rupinder

Meiman, Darius E