Difference between revisions of "BCH364C BCH394P 2017"
(Created page with "== BCH364C/BCH394P Systems Biology & Bioinformatics == '''Course unique #:''' 55120/55210<br> '''Lectures:''' Tues/Thurs 11 – 12:30 PM in GDC 4.302<br> '''Instructor:''' E...") |
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An introduction to systems biology and bioinformatics, emphasizing quantitative analysis of high-throughput biological data, and covering typical data, data analysis, and computer algorithms. Topics will include introductory probability and statistics, basics of Python programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, proteomics, synthetic biology, analysis of large-scale gene expression data, data clustering, biological pattern recognition, and gene and protein networks.<br> | An introduction to systems biology and bioinformatics, emphasizing quantitative analysis of high-throughput biological data, and covering typical data, data analysis, and computer algorithms. Topics will include introductory probability and statistics, basics of Python programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, proteomics, synthetic biology, analysis of large-scale gene expression data, data clustering, biological pattern recognition, and gene and protein networks.<br> | ||
− | Open to | + | Open to graduate students and upper division undergrads (with permission) in natural sciences and engineering. |
− | + | Prerequisites: Basic familiarity with molecular biology, statistics & computing, but realistically, it is expected that students will have extremely varied backgrounds. UGs have additional prerequisites, as listed in the catalog.<br> | |
''Note that this is not a course on practical sequence analysis or using web-based tools. Although we will use a number of these to help illustrate points, the focus of the course will be on learning the underlying algorithms and exploratory data analyses and their applications, esp. in high-throughput biology.''<br> | ''Note that this is not a course on practical sequence analysis or using web-based tools. Although we will use a number of these to help illustrate points, the focus of the course will be on learning the underlying algorithms and exploratory data analyses and their applications, esp. in high-throughput biology.''<br> | ||
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[http://lectures.molgen.mpg.de/ An online bioinformatics course]<br> | [http://lectures.molgen.mpg.de/ An online bioinformatics course]<br> | ||
Assorted bioinformatics resources on the web: [http://zlab.bu.edu/zlab/links.shtml Assorted links]<br> | Assorted bioinformatics resources on the web: [http://zlab.bu.edu/zlab/links.shtml Assorted links]<br> | ||
− | |||
Online probability texts: [http://omega.albany.edu:8008/JaynesBook.html #1], [http://www-users.york.ac.uk/~mb55/pubs/pbstnote.htm #2], [http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/pdf.html #3]<br> | Online probability texts: [http://omega.albany.edu:8008/JaynesBook.html #1], [http://www-users.york.ac.uk/~mb55/pubs/pbstnote.htm #2], [http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/pdf.html #3]<br> | ||
− | '''No exams will be given. Grades will be based on online homework (counting 30% of the grade), 3 problem sets (given every 2-3 weeks and counting 15% each towards the final grade) and an independent course project (25% of final grade).''' The course project will | + | '''No exams will be given. Grades will be based on online homework (counting 30% of the grade), 3 problem sets (given every 2-3 weeks and counting 15% each towards the final grade) and an independent course project (25% of final grade).''' The course project will consist of a research project on a bioinformatics topic chosen by the student (with approval by the instructor) containing an element of independent computational biology research (e.g. calculation, programming, database analysis, etc.). This will be turned in as a link to a web page. '''The final project is due by midnight, April 27, 2017. The last two classes will be spent presenting your projects to each other. (The presentation will account for 5% of the project.)'''<br> |
Online homework will be assigned and evaluated using the free bioinformatics web resource [http://rosalind.info/faq/ Rosalind].<br> | Online homework will be assigned and evaluated using the free bioinformatics web resource [http://rosalind.info/faq/ Rosalind].<br> | ||
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Homework, problem sets, and the project total to a possible 100 points. There will be no curving of grades, nor will grades be rounded up. We’ll use the plus/minus grading system, so: A= 92 and above, A-=90 to 91.99, etc. Just for clarity's sake, here are the cutoffs for the grades: 92% = A, 90% = A- < 92%, 88% = B+ < 90%, 82% = B < 88%, 80% = B- < 82%, 78% = C+ < 80%, 72% = C < 78%, 70% = C- < 72%, 68% = D+ < 70%, 62% = D < 68%, 60% = D- < 62%, F < 60%. | Homework, problem sets, and the project total to a possible 100 points. There will be no curving of grades, nor will grades be rounded up. We’ll use the plus/minus grading system, so: A= 92 and above, A-=90 to 91.99, etc. Just for clarity's sake, here are the cutoffs for the grades: 92% = A, 90% = A- < 92%, 88% = B+ < 90%, 82% = B < 88%, 80% = B- < 82%, 78% = C+ < 80%, 72% = C < 78%, 70% = C- < 72%, 68% = D+ < 70%, 62% = D < 68%, 60% = D- < 62%, F < 60%. | ||
− | Students are welcome to discuss ideas and problems with each other, but '''all programs, Rosalind homework, and written solutions should be performed independently'''.<br> | + | Students are welcome to discuss ideas and problems with each other, but '''all programs, Rosalind homework, and written solutions should be performed independently'''.<br> Students are expected to follow the UT honor code. Cheating, plagiarism, copying, & reuse of prior homework or programs from CourseHero, Github, or other sources are all strictly forbidden and constitute breaches of academic integrity ([http://deanofstudents.utexas.edu/sjs/acint_student.php UT academic integrity policy]) and cause for dismissal with a failing grade. |
− | '''The final project web site is due by midnight April 27, | + | '''The final project web site is due by midnight April 27, 2017.''' |
* How to make a web site for the final project | * How to make a web site for the final project |
Revision as of 19:56, 16 January 2017
BCH364C/BCH394P Systems Biology & Bioinformatics
Course unique #: 55120/55210
Lectures: Tues/Thurs 11 – 12:30 PM in GDC 4.302
Instructor: Edward Marcotte, marcotte @ icmb.utexas.edu
- Office hours: Wed 11 AM – 12 noon in MBB 3.148BA
TA: Azat Akhmetov, azat @ utexas.edu
- TA Office hours: Mon/Wed 3 PM - 4 PM in MBB 3.128A Phone: on syllabus
Lectures & Handouts
Jan 19, 2016 - Introduction
- Today's slides
- Some warm-up videos to get you started on Python: Code Academy's Python coding for beginners
- We'll be conducting homework using the online environment Rosalind. Go ahead and register on the site, and enroll specifically for BCH339N using this link. Homework #1 (worth 10% of your final course grade) has already been assigned on Rosalind and is due by 11:59PM January 28.
- A useful online resource if you get bogged down: Python for Biologists. (& just a heads-up that some of their instructions for running code relate to a command line environment that's a bit different from the default one you install following the Rosalind instructions. It won't affect the programs, just the way they are run or how you specific where files are located.) However, if you've never programmed before, definitely check this out!!!
- An oldie (by recent bioinformatics standards) but goodie: Computers are from Mars, Organisms are from Venus
Syllabus & course outline
An introduction to systems biology and bioinformatics, emphasizing quantitative analysis of high-throughput biological data, and covering typical data, data analysis, and computer algorithms. Topics will include introductory probability and statistics, basics of Python programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, proteomics, synthetic biology, analysis of large-scale gene expression data, data clustering, biological pattern recognition, and gene and protein networks.
Open to graduate students and upper division undergrads (with permission) in natural sciences and engineering.
Prerequisites: Basic familiarity with molecular biology, statistics & computing, but realistically, it is expected that students will have extremely varied backgrounds. UGs have additional prerequisites, as listed in the catalog.
Note that this is not a course on practical sequence analysis or using web-based tools. Although we will use a number of these to help illustrate points, the focus of the course will be on learning the underlying algorithms and exploratory data analyses and their applications, esp. in high-throughput biology.
Most of the lectures will be from research articles and slides posted online, with some material from the...
Optional text (for sequence analysis): Biological sequence analysis, by R. Durbin, S. Eddy, A. Krogh, G. Mitchison (Cambridge University Press),
For biologists rusty on their stats, The Cartoon Guide to Statistics (Gonick/Smith) is very good. A reasonable online resource for beginners is Statistics Done Wrong.
Some online references:
An online bioinformatics course
Assorted bioinformatics resources on the web: Assorted links
Online probability texts: #1, #2, #3
No exams will be given. Grades will be based on online homework (counting 30% of the grade), 3 problem sets (given every 2-3 weeks and counting 15% each towards the final grade) and an independent course project (25% of final grade). The course project will consist of a research project on a bioinformatics topic chosen by the student (with approval by the instructor) containing an element of independent computational biology research (e.g. calculation, programming, database analysis, etc.). This will be turned in as a link to a web page. The final project is due by midnight, April 27, 2017. The last two classes will be spent presenting your projects to each other. (The presentation will account for 5% of the project.)
Online homework will be assigned and evaluated using the free bioinformatics web resource Rosalind.
All projects and homework will be turned in electronically and time-stamped. No makeup work will be given. Instead, all students have 5 days of free “late time” (for the entire semester, NOT per project, and counting weekends/holidays). For projects turned in late, days will be deducted from the 5 day total (or what remains of it) by the number of days late (in 1 day increments, rounding up, i.e. 10 minutes late = 1 day deducted). Once the full 5 days have been used up, assignments will be penalized 10 percent per day late (rounding up), i.e., a 50 point assignment turned in 1.5 days late would be penalized 20%, or 10 points.
Homework, problem sets, and the project total to a possible 100 points. There will be no curving of grades, nor will grades be rounded up. We’ll use the plus/minus grading system, so: A= 92 and above, A-=90 to 91.99, etc. Just for clarity's sake, here are the cutoffs for the grades: 92% = A, 90% = A- < 92%, 88% = B+ < 90%, 82% = B < 88%, 80% = B- < 82%, 78% = C+ < 80%, 72% = C < 78%, 70% = C- < 72%, 68% = D+ < 70%, 62% = D < 68%, 60% = D- < 62%, F < 60%.
Students are welcome to discuss ideas and problems with each other, but all programs, Rosalind homework, and written solutions should be performed independently.
Students are expected to follow the UT honor code. Cheating, plagiarism, copying, & reuse of prior homework or programs from CourseHero, Github, or other sources are all strictly forbidden and constitute breaches of academic integrity (UT academic integrity policy) and cause for dismissal with a failing grade.
The final project web site is due by midnight April 27, 2017.
- How to make a web site for the final project
- Google Site: https://support.google.com/sites/answer/153197?hl=en