Courses Offered

BCB Course Offerings and Requirements

Course Offerings are available here:  http://classes.iastate.edu/  Use an advanced search to find courses by keyword !

Listed below are descriptions of current course offerings of interest to BCB students.  Following this section is a list of all BCB Course Requirements for PhD students in the BCB degree program.

BCB-Related Course Offerings in Spring 2017

Com S 551

Com S 551, Parallel Computational Techniques for Analyzing Huge Genome Sequence Data, TR 2:10-3:30, Instructor: Xiaoqiu Huang, Location: Forker 0227

This course will give an introduction to a hot big data research area in bioinformatics. An organism's genome carries most of the genetic instructions for its development, functioning and reproduction. This course will focus on applying parallel computational techniques to huge genomic sequence data to find genome variation between individuals of an organism. These techniques including an optimal sequence alignment by dynamic programming, parallel programming with MPI, using a Linux cluster with distributed memory, performing a genome assembly, and mapping DNA sequences onto a genome assembly. These techniques can be used to find single-nucleotide variants (SNVs) and copy-number variants (CNVs) in 100-Gb of compressed human sequence data in a day on a single compute node of a cluster. Finding genome variation is an extremely useful skill in basic and translational genome research. Students with different backgrounds are encouraged to team up in groups to work on a project in genome research.

 

Prerequisites: Graduate standing in Computer Science or Biology/Genetics/Evolution.
 

Xiaoqiu Huang has developed several versions of a genome assembly program for next-generation sequencing data. His current research is focused on understanding genome evolution and function by using genome sequence data (see the paper below).

 

Huang X, Das A, Sahu BB, Srivastava SK, Leandro LF, O'Donnell K, Bhattacharyya MK. (2016) Identification of highly variable supernumerary chromosome segments in an asexual pathogen. PLoS ONE 11(6): e0158183. http://dx.doi.org/10.1371/journal.pone.0158183

 

BCB546X

BCB546X, Biology Data Skills, will teach students the fundamental skills of data processing, management, and analysis that are essential for working with large data sets.  The course will include modules on basic UNIX commands, scripting in Python and R, version control using Git and GitHub, use of high performance computing clusters and writing effective data management plans.  The book "Bioinformatics Data Skills" by Vince Buffalo, part of the O'Reilly computational series, will be the primary text for the course.  This will be a 3-credit course meeting twice a week for 1.5 hours.  Topics will be taught in class using a combination of lectures and computational exercises.

Time:     1:10 - 2:30 p.m.;  Location: 0205 Bessey
Instructor:    Matthew Hufford
Email:     hufford@iastate.edu

BCB546X is intended to be a service course directed primarily toward graduate students outside of the BCB Graduate Program (e.g., IGG, Animal Science, Agronomy, EEB), but should also prove useful for new BCB students from a biological background who need to develop basic computational skills.

 

BCB 660

BCB 660. Selected Topics in Bioinformatics and Computational Biology. (2-0) Cr. 0-2. Repeatable, maximum of 4 credits. F.S.SS. Prereq: Permission of Instructor. Topics of interest in the major research areas of computational molecular biology, including genomics, structural genomics, functional genomics, and computational systems biology.

Spring 2017: Applications of NGS data processing software in genomics

Time:     8-9:30 a.m.;  Location: 1340 MBB
Instructor:    Andrew Severin
Email:     severin@iastate.edu

Title: Introduction to Next-Generation Sequencing 

Description: This interdisciplinary course is primarily designed for advanced graduate students interested in applying computational methods to biological research. This course introduces computational thinking and analysis of next-generation sequencing data. The first few weeks will focus on UNIX and shell scripting required for large-scale analysis.  By the end of the course, students will gain hands-on experience in 

  • Advanced UNIX
  • Perl and shell scripting
  • Short read alignments
  • Genome assembly
  • RNA-Seq and
  • other related topics 

Prerequisites: No prior programming experience is required. A strong aptitude to learn new skills is necessary. Familiarity with Unix/Linux is recommended.  If you have any special needs, please address them with the instructor.

 

BBMB512X

BBMB will offer a new course, BBMB 512X “Principles of Glycobiology” (Cr. 2), in spring 2017 to be taught by Prof. Olga Zabotina.   

The class will meet on Tues and Thurs at 3:10-4:00 and we hope you will consider enrolling. 

In this class students will be introduced to the new exciting and fast growing scientific area of Biochemistry/Biology which does not usually receive much attention in undergraduate courses of Biochemistry or Organic Chemistry.  The field of glycobiology is in a period of enormous progress and the prospects for future advances are even greater. The important role of glycans is underscored by the growing number of human diseases that are results of particular defects in glycan assembly. The growing population presses enormous demand on industries to produce carbohydrate based materials with better or novel qualities for food, drugs, fibers and biofuels. All these require preparation of new workforce with strong knowledge in the field of glycobiology. Therefore, the goal of this course is to increase students’ awareness about importance of understanding the functions of glycans attached to proteins, lipids, hormones or secondary metabolites. The course will illustrate the main themes that underlie the structures, synthesis and functions of glycoprotein, glycolipids, polysaccharides and important glycosides by highlighting well-understood examples of how the carbohydrate portions of these molecules work and also examples of biotechnological applications of glycoconjugates and glycans.

ComS525

High Performance Computing: Math/ComS/CprE 525, 3 credit. Offered spring semesters.

Description: High Performance Computing (HPC) concepts and terminology, HPC machine architecture, how to use debugging and performance tools, advanced parallel programming with MPI, OpenMP and possibly OpenACC and/or CUDA. An oral and written semester project is required of each student. The semester project gives students the opportunity to apply concepts learned to their research interests. Instructor for spring 2017: Professor Glenn Luecke

Prerequisites: math 424, experience programming in Fortran or C, or permission of the instructor.

Meeting times and room: Tuesdays and Thursdays from 11:00 to 1:00 in 449 Carver. 2 hours of lecture and 2 hours of lab each week.

 

BCB Course Requirements for PhD Students

 

The curriculum for BCB's graduate program at Iowa State University has developed over the last ten+ years to incorporate and keep pace with the expanding body of knowledge associated with this dynamic discipline. Our four BCB core courses have had updates recently for the 2017 catalog and new titles and descriptions are below.  Refer to the Degree Requirements page and our BCB student handbook for further details about the BCB degree program.  For more information on prerequisite and background courses which prepare you for our core courses, see Academic Preparation under the Future Students tab.

 

Required background course:

 

Stat 430. Empirical Methods for Computer Science. (3-0) Cr. 3. F. Prereq: Stat 330 or an equivalent course, Math 166, knowledge of linear algebra. Programs and systems as objects of empirical studies; exploratory data analysis; selected topics from analysis of designed experiments - analysis of variance, hypothesis testing, interaction among variables; linear regression, logistic regression, Poisson regression; parameter estimation, prediction, confidence regions, dimension reduction techniques, model diagnostics and sensitivity analysis; Markov chains and processes; simulation techniques and bootstrap methods; applications to performance assessment - comparison of multiple systems; communicating results of empirical studies. Statistical software: R.

 

Core course in molecular genetics:

 

GDCB 511. Molecular Genetics. (Cross-listed with MCDB). (3-0) Cr. 3. S. Prereq: Biol 313 and BBMB 405. The principles of molecular genetics: gene structure and function at the molecular level, including regulation of gene expression, genetic rearrangement, and the organization of genetic information in prokaryotes and eukaryotes. (An equivalent or more advanced course may be substituted with approval of student's POS Committee.)

 

Core courses in computational biology:

 

BCB 567. Bioinformatics I (Bioinformatics Algorithms). (Cross-listed with Com S, Cpr E). (3-0) Cr. 3. F. Required Prerequisites: Com S 228; Com S 330; Biol 313; credit or enrollment in Biol 315, Stat 430. Biology as an information science. A review of the algorithmic principles that are driving the advances in bioinformatics and computational biology. Syllabus: BCB_567_2070015_Eulenstein_F15.pdf

 

BCB 568. Bioinformatics II (Statistical Bioinformatics). (Cross-listed with GDCB, Stat, Com S). (3-0) Cr. 3. S. Prereq: BCB 567, Biol 315, Stat 430, credit or enrollment in Gen 409. Statistical models for sequence data, including applications in genome annotation, motif discovery, variant discovery, molecular phylogeny, gene expression analysis, and metagenomics.  Statistical topics include model building, inference, hypothesis testing, and simple experimental design, including for big data/complex models. Syllabus: BCB_568_2081005_Dorman_S15.pdf

 

BCB 569. Bioinformatics III (Structural Bionformatics). (Cross-listed with BBMB, Com S, Math, GDCB). (3-0) Cr. 3. F. Prereq: BBMB 316, BCB 567, Gen 409, Stat 430. Molecular structures including genes and gene products: protein, DNA and RNA structure. Structure determination methods, structural refinement, structure representation, comparison of structures, visualization, and modeling. Molecular and cellular structure from imaging. Analysis and prediction of protein secondary, tertiary, and higher order  structure, disorder, protein-protein and protein-nucleic acid interactions, protein localization and function, bridging between molecular and cellular structures. Molecular evolution. Syllabus: BCB_569_2105005_Jernigan_F15.pdf

 

BCB 570. Bioinformatics IV (Systems Biology). (Cross-listed with Com S, GDCB, Stat, Cpr E). (3-0) Cr. 3. S. Prereq: BCB 567 or Com S 311; Com S 228, Gen 409, and Stat 430. Algorithmic and statistical approaches in computational functional genomics and systems biology. Analysis of high throughput biological data obtained using system-wide measurements. Topological analysis, module discovery, and comparative analysis of gene and protein networks. Modeling, analysis, and inference of transcriptional regulatory networks, protein-protein interaction networks, and metabolic networks. Dynamic systems and whole-cell models. Ontology-driven, network based, and probabilistic approaches to information integration. Syllabus: BCB 570_2125005_Dickerson_S15.docx

 

Advanced Group Requirements

 

In addition to the five core courses, students must complete at least six credits of advanced coursework. Courses should include three credits from Category I (Molecular Biology) and three credits from either Category II (Computer Science) or Category III (Mathematics/Statistics). The table below provides a list of some of the courses that can be used to fulfill this depth requirement. Not all listed courses are suited for all programs of study. Students should consult with their POS committees to determine which courses from this list, or not from the list, are most appropriate.

 

Courses That Fulfill Advanced Requirements

Students should select advanced requirements in consultation with their POS Committee. This is a partial list of suggestions. Advanced courses should be selected with POS committee consultation and approval.

 

Category I. Molecular Biology (3 credits required)
An Sci 556 Current Topics in Genome Analysis 3 cr - Alt S
BBMB 404 Biochemistry I 3 cr. - F
BBMB 405 Biochemistry II 3 cr. - S
BBMB 461 Molecular Biophysics 2 cr. - S
BBMB 501 General Biochemistry 3 cr. - F
BBMB 502 General Biochemistry 3 cr. - S
BBMB/GDCB 542 A, B,
C, D, E, F
Introduction to Molecular Biology Techniques 1 cr. per module - F, S
BBMB 561 Molecular Biophysics 2 cr. - S
BBMB 607 Plant Biochemistry 2 cr. - F
BBMB 653 Protein Chemistry - Physical Methods 1 cr. - S
BBMB 660 Membrane Biochemistry 2 cr. - F
BBMB 675 Nucleic Acid Structure and Function 2 cr. - F
Gen 462/EEOB 562 Evolutionary Genetics 3 cr. - S
GDCB 520 Genetic Engineering 3 cr. - Alt. F
EEOB 561X Evolutionary and Ecological Genomics 3 cr. - S
EEOB 563 Molecular Phylogenetics 3 cr. - F
EEOB 566 Molecular Evolution 3 cr. - Alt. F
Category II. Computer Science (3 credits required from Group II OR from Group III)
*BCB 567 Bioinformatics I (Fundamentals of Genomic Informatics) 3 cr. - F
*BCB 568 Bioinformatics II (Advanced Genome Informatics) 3 cr. - S
BCB 596 Genomic Data Processing 3 cr. - F
Com S 311 Design and Analysis of Algorithms 3 cr. - F S
Com S 363 Introduction to Database Management Systems 3 cr. - F S
Com S 461/561 Principles and Internals of Database Systems 3 cr. - F
Com S 472/572 Principles of Artificial Intelligence 3 cr. - F
Com S 474 Elements of Neural Computation 3 cr. - Alt F
Com S 511 Design and Analysis of Algorithms 3 cr. - F
Com S 526 Intro to parallel Algorithms and Programming 4 cr. - F
Com S 549 Advanced Algorithms in Computational Biology 3 cr. - Alt S
Com S 550 Evolutionary Problems for Computational Biologists 3 cr. - Alt F
Com S 551 Computational Techniques for Genome Assembly and Analysis 3 cr. - Alt F
Com S 573 Machine Learning 3 cr. - S
Com S 574 Intelligent Multiagent Systems 3 cr. - Alt F
Com S 611 Advanced Topics in Analysis of Algorithms 3 cr. - Alt S
Com S 672 Advanced Topics in Computational Models of Learning 3 cr. - Alt S
Com S 673 Advanced Topics in Computational Intelligence 3 cr. - Alt S
EE 547 Pattern Recognition 3 cr. - F
Category III. Mathematics & Statistics (3 credits required from Group III or Group II)
*BCB 568 Advanced Genome Informatics 3 Cr. - S
BCB 660 Applications of NGS data processing software in genomics 3 Cr. - F
Math 304 Introductory Combinatorics 3 Cr. - F
Math 407 Applied Linear Algebra 3 Cr. - F
Math 314 Graphs and Networks 3 Cr. - S
Math 554 Introduction to Stochastic Processes 3 Cr. - F
Stat 500 Statistical Methods 4 Cr. - F
Stat 501 Multivariate Statistical Methods 3 Cr. - S
Stat 536 Statistical Genetics 3 Cr. - Alt F
Stat 542 Theory of Probability and Statistics I 4 Cr. - F
Stat 543 Theory of Probability and Statistics II 3 Cr. - S
*F = Fall semester; S = Spring semester; SS = Summer Session
*BCB 567 and 568 cannot be used to meet core course requirements AND Advanced Course Requirements.

 

Ethics Requirement:

 

GRST 565. Responsible Conduct of Research in Science and Engineering.(1-0) Cr. 1. F.S. Prereq: Graduate classification. Ethical and legal issues facing researchers in the sciences and engineering.

 

Workshop and Seminars:

 

More information on these courses can be found here.

 

BCB 593. Workshop in Bioinformatics and Computational Biology. (1-0) Cr. 1. Repeatable. F.S.Current topics in bioinformatics and computational biology research. Lectures by off-campus experts. Students read background literature, attend preparatory seminars, attend all lectures, meet with lecturers.

 

BCB 690. Student Seminar in Bioinformatics and Computational Biology. Cr. 1. Repeatable. S. Student research presentations.

 

BCB 691. Faculty Seminar in Bioinformatics and Computational Biology. (1-0) Cr. 1. Repeatable. F. Faculty research series. Fall 2016 Schedule.

 

Research

 

BCB 697. Graduate Research Rotation. Cr. arr. F.S.SS. Graduate research projects performed under the supervision of selected faculty members in the Bioinformatics and Computational Biology major.

 

BCB 699. Research. Cr. arr. Repeatable. (BCB 699 reference numbers.)

 

Descriptions of some BCB advanced group requirement courses:

 

Offered Fall 2015: BCB 590. Introduction to NextGen Sequencing. This interdisciplinary course is primarily designed for advanced graduate students interested in applying computational methods to biological research. This course introduces computational thinking and analysis of next-generation sequencing data. The first few weeks will focus on UNIX and shell scripting required for large-scale analysis. By the end of the course, students will gain hands-on experience in:

  • Advanced UNIX
  • Perl and shell scripting
  • Short read alignments
  • Genome assembly
  • RNA-Seq and
  • other related topics

 

Prerequisites: No prior programming experience is required. A strong aptitude to learn new skills is necessary. Familiarity with Unix/Linux is recommended. If you have any special needs, please address them with the instructor. Syllabus: BCB_590_1337005_Muppirala_F15.pdf

 

BCB 596. Genomic Data Processing. (Cross-listed with Com S, GDCB). (3-0) Cr. 3. F. Prereq: Some knowledge of programming. Study the practical aspects of genomic data processing with an emphasis on hand-on projects. Students will carry out major data processing steps using bioinformatics tools. Topics include base-calling, raw sequence cleaning and contaminant removal; shotgun assembly procedures and EST clustering methods; genome closure strategies and practices; sequence homology search and function prediction; annotation and submission of GenBank reports; and data collection and dissemination through the Internet. Useful post-genomic topics like microarray design and data analysis will also be covered.

 

BCB 660. Selected Topics in Bioinformatics and Computational Biology. (2-0) Cr. 0-2. Repeatable, maximum of 4 credits. F.S.SS. Prereq: Permission of Instructor. Topics of interest in the major research areas of computational molecular biology, including genomics, structural genomics, functional genomics, and computational systems biology.

Spring 2017: Applications of NGS data processing software in genomics

Time:     8-9:30 a.m.;  Location: 1340 MBB
Instructor:    Andrew Severin
Email:     severin@iastate.edu

Title: Introduction to Next-Generation Sequencing 

Description: This interdisciplinary course is primarily designed for advanced graduate students interested in applying computational methods to biological research. This course introduces computational thinking and analysis of next-generation sequencing data. The first few weeks will focus on UNIX and shell scripting required for large-scale analysis.  By the end of the course, students will gain hands-on experience in 

  • Advanced UNIX
  • Perl and shell scripting
  • Short read alignments
  • Genome assembly
  • RNA-Seq and
  • other related topics 

Prerequisites: No prior programming experience is required. A strong aptitude to learn new skills is necessary. Familiarity with Unix/Linux is recommended.  If you have any special needs, please address them with the instructor.

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