Interdisciplinary Introductory Course in Bioinformatics

1
Interdisciplinary Introductory Course in
Bioinformatics

• Yana Kortsarts
• Computer Science Department
• Robert Morris
• Biology Department
• Janine Utell
• English Department,
• Widener University, Chester, PA

2
What is Bioinformatics?

• Bioinformatics is a relatively new
  interdisciplinary field that integrates computer
  science, mathematics, biology, and information
  technology to manage, analyze, and understand
  biological, biochemical and biophysical
  information.
• Bioinformatics is a computational science and the
  subset of larger field of Computational Biology.

3
Motivation

• IS professionals must have strong analytical and
  critical thinking skills. (IS 2002 Model
  Curriculum and Guidelines for Undergraduate
  Degree Programs in IS)
• Introducing bioinformatics to CIS students will
  strengthen these required skills.
• Equip students with some of the following
  capabilities as suggested in the IS 2002
  guidelines
• Creativity
• Application of both traditional and new concepts
  and skills
• Application development
• Problem solving abilities
• Ability to communicate effectively (oral, written
  and listening)

4
Motivation

• Provides opportunities for students to become
  familiar with one of the most widely used script
  languages, Python
• Explore various data structures and algorithmic
  techniques traditionally not covered in other
  courses.
• Helps students to make connections between
  theoretical topics learned in core CS and CIS
  courses, such as Data Structures and Algorithms,
  and to apply their knowledge to real world
  biology problems.
• Helps to diversify department course offering and
  provides interdisciplinary opportunities for CS
  and CIS students.
• CIS and CS students with bioinformatics
  background clearly will enhance their employment
  qualifications in the competitive job market

5
Challenges

• Students have different backgrounds
• Choosing programming language
• Defining course prerequisites
• Defining course content
• Programming
• Algorithms
• End User Bioinformatics Tools
• Balanced course
• Content
• Hands-on/lecture
• Interdisciplinary Nature

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Course Development

• First Iteration Spring 05 Second Iteration
  Spring 08
• Crosslisted upper level technical elective.
• Prerequisites
• Biology/Chemistry/Biochemistry majors
  Introduction to Molecular Biology
• CS/CIS/MATH majors Introduction to Computer
  Science I
• Chemical Engineering Majors Computer Programming
  and Engineering Problem Solving
• Team teaching Biology and Computer Science
  Faculty
• 4 credits, 6 hr 4 CS, 2 Biology
• Spring 05 Enrollment 6 Biology students, 6
  CS/CIS
• Spring 08 Enrollment 6 CS/CIS

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Course Objectives and Goals

• To integrate bioinformatics algorithms into the
  course and to teach the foundations of the
  algorithms and important results in
  bioinformatics
• To introduce students to the Python programming
  language. Biopython Project is an international
  association of developers of freely available
  Python tools for computational molecular biology.
  

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Course Objectives and Goals

• To introduce students to the principles that
  drive an algorithms design and to intellectual
  content of bioinformatics
• To provide an opportunity for interdisciplinary
  collaboration in the in-class assignments and the
  course project

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Course Curriculum

• Ethics, Computing and Genomics
• Project-Oriented Component, new for Spring 08
• 20 of the final grade, three weeks to work on
  this project
• Goal developing oral and written communication
  skills and to engage students in the knowledge
  exchange process
• Learning about the ethics, computing and genomics
  topic independently and presenting the results of
  the self-learning.
• Students were assigned one or more scholarly
  articles from the collection Ethics, Computing,
  and Genomics, edited by Herman Tavani.
• Students were required
• read assigned essays
• prepare 25-minute Power Point presentations with
  a summary of the paper and answers to the
  questions posed in the introductory part of the
  corresponding section
• prepare a mini-quiz to assess the understanding
  of the presented material by their peers.

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Ethics, Computing and Genomics

• Collaborative Work with English faculty member
• English faculty member did short presentation
  before students started to work on this
  assignment.
• discussion of how to read critically and what
  questions to ask while reading the text
• discussion of how to summarize the paper using
  the structure of the essay as a guide and
  elucidating key points and key moments of
  evidence while making connections to the rest of
  the class material
• tips on writing the summary that include three
  steps prewriting, drafting, revising
• discussion of how to design an effective
  presentation of information.
• Was present at all oral presentations and
  provided detailed notes for each student
  explaining ways the presentation could have been
  stronger and also pointing out the positive and
  negative aspects of the presentation.
• This successful and enjoyable experience showed
  the value of working with colleagues across
  disciplines to further student learning.

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Course Curriculum Introduction to Python

• Quickly introduce students to Python during first
  few weeks of the course
• Working on different problem solving algorithmic
  techniques.
• Introductory topics arithmetic, decision and
  loop structures, functions, simple manipulations
  with strings, lists, tuples and dictionaries.
• Advanced Python topics were taught later
  throughout the course, building students
  knowledge and their abilities to tackle biology
  real-world problems.
• The programming examples were all
  biology-oriented and motivated students to learn
  in order to solve practical problems.

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Introduction to Python

• Spring 05 6 biology students, no programming
  experience, 6 CS and CIS students no experience
  in Python.
• 6 interdisciplinary teams, all concepts were
  practiced within the team
• Spring 08 all students were CS and CIS majors
  with prior programming experience in C and Java,
  and some with introductory knowledge in Python
• Special handouts were prepared to walk students
  through the introductory topics toward advanced
  Python concepts.
• Each topic was supported by a list of examples in
  increasing order of complexity.
• Students were required to run the proposed
  programs in order to gain understanding of basic
  Python structures.
• To assess the understanding of each concept,
  students were required to write short programs
  solving biology-oriented problems.

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Introduction to Python

• Examples of the problems, given here in
  increasing level of complexity
• computation of the alignment score between two
  DNA sequences using different score matrices
• finding the maximal alignment score if no
  internal gaps are allowed using different score
  matrices
• finding all occurrences of one sequence in
  another sequence
• writing a program that reads a DNA sequence,
  first transcribing DNA into RNA and printing the
  resulting RNA sequence, then translating RNA into
  a protein sequence through the following first,
  the program divides RNA into codons and prints
  the list of codons, and second, the codons are
  translated into the protein using genetic code
  table and finding the maximal alignment score if
  internal gaps are allowed using different score
  matrices.

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Introduction to Python

• Spring 08 different levels of programming and
  computational experience and the best way to
  cover this topic was through independent
  learning.
• Handouts and Python and BioPython tutorials
  (www.python.org, www.biopython.org), worked each
  at their own pace.
• Grading rubrics for each programming concept,
  minimal requirements to pass the specific
  concept, list of more advanced examples for
  students with prior Python experience.
• Students with previous Python knowledge further
  advanced their experience and students new to
  Python learned the new programming language
  independently using structured guidance.
• Python provides an opportunity to solve some
  problems in very short ways, and it was a very
  enjoyable experience for students to try to find
  a shortest solution for the proposed problems
  using Python functions and libraries.

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Introduction to Bioinformatics Algorithms

• Sequence alignments, scoring, gaps
• Algorithm Design Techniques Exhaustive Search,
  Dynamic Programming
• The Needleman and Wunsch Algorithm
• The Smith-Waterman Algorithm
• Introduction to BLAST
• Introduction to Multiple Sequence Alignments
• Visualization of algorithms
• ALGGEN EMBER Web resources

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Introduction to Bioinformatics Algorithms

• Dynamic Programming technique usually is not
  covered in a core algorithms course
• Provided an opportunity to expand the theoretical
  background and to make connections between theory
  and practice.
• Helped to maintain an appropriate level of
  theoretical content required for upper-level
  elective courses in our department.
• This topic was very well blended with biology
  topics and students had an opportunity to learn
  the concept of sequence alignments from biology
  and computer science points of view.
• EMBER website provides a suite of multimedia
  bioinformatics educational tools, allows to
  create a set of hands-on activities to help
  students to gain understanding of the dynamic
  programming technique in general and specific
  algorithms in particular.

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Course Curriculum Biology Topics

• Biological Research on the Web
• Public Biological Databases and Data Formats
• NCBI - National Center for Biotechnology
  Information
• Searching Biological Databases
• Review of Molecular Biology and Biochemistry
  Concepts
• DNA and protein structure
• Gene expression (transcription and translation)
• Molecular Biology Central Dogma
• Sequence Alignments

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Hands-On Activities

• Microbes Count! BioQUEST Curriculum Consortium
• Exploring HIV Evolution An Opportunity for
  Research.
• The HIV genome is very small and relatively
  simple. It is made up of nine genes and about
  9,500 nucleotides.
• In this lab students worked with HIV sequence
  data collected from 15 individuals from an
  intravenous-drug-using population in Baltimore.
• The goal of the study was to determine if the HIV
  isolated from particular subgroups of subjects
  derives from a common source.
• CLUSTALW multiple sequence alignment tool
• Biology Workbench http//workbench.sdsc.edu/

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Biology Topics Hands-On Activities

• Microarray Lab, developed by Campbell and Heyer,
  sold by Carolina Biologicals, called DNA Chips
  Genes to Disease.
• Understanding how microarrays are used to
  identify gene changes in disease and the role of
  gene expression in cancer.
• Students compared the relative expression levels
  of six different genes in healthy lung cells and
  lung cancer cells.
• After completing the lab, students had an
  opportunity to discuss the significance of the
  relative expression levels with respect to the
  genes' roles in causing cancer

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Biology Topics Hands-On Activities

• Epidemiology - the study of the distribution of
  diseases in populations.
• Explored factors that influence disease spread
  throughout populations with the software
  Epidemiology. Ebola was used as a model organism
  and epidemiology was presented from both a
  microbiological and social perspective
• Exploration of the structure and function of the
  insulin
• generate a phylogenetic tree demonstrating
  evolution of insulin amongst the vertebrates -
  animals with an internal skeleton made of bone

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Project DNA Sequence Annotation.

• Real Data Bacillus anthracis str. Ames project
  at J. Craig Venter Institute
• Input DNA about 50,000 nucleotides long,
  students worked on different sequences from the
  same organism.
• Project Steps
• Find a list of all potential genes and
  pseudo-genes in the input DNA sequence, using
  start and end codons, and to arrange found
  sequences in two separate lists potential genes
  (length is larger than 300) and pseudo-genes
  (length of is less than 300), in order of
  increasing length.
• Locate the potential promoters in the given DNA
  sequences for each potential gene that they found
  in the first step, and calculated the strength of
  the promoter. A promoter is a region of DNA near
  the beginning of a gene that controls if and when
  the gene is actually expressed. Output list
  potential genes in order of decreasing promoter
  strength.
• BLAST all potential genes and pseudo-genes that
  were found, and to perform an analysis of the
  results.

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Project

• Summary
• For each potential and pseudo-gene start
  position, length, promoter score, BLAST results,
  summary and conclusion. For each sequence, we
  asked students to determine whether a potential
  gene could be a real gene based on the strength
  of the promoter and BLAST results.
• 15-minute in-class presentations Python program,
  description of all Python functions that were
  used and the purpose of each function, all
  algorithms or/and programming techniques and the
  presentation and explanation of the summary
  results, including the information about the
  specific organism whose DNA was used as the
  input.
• Spring 05 team project team of CS/CIS and
  biology student. Programming part of the project
  was mostly done by the computer science students,
  and the biology students were required to
  understand and to explain the programming
  techniques and algorithms that were used. The
  project provided a possibility for truly
  interdisciplinary collaboration between computer
  science and biology students.
• Spring 08 individual project.

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Course Results

• Spring 05 no formal assessment survey was
  conducted.
• An informal discussion about the course was
  conducted at the end of the semester and we asked
  students to provide their feedback. Students
  completed teaching evaluations and provided their
  comments there as well.
• All students showed satisfaction with the course
  and we were very pleased to receive the request
  to extend the programming component of the course
  from almost all students. Biology students showed
  interest in programming and asked that an
  environment be created where they would be able
  to more fully participate in all stages of the
  course project.
• Spring 08 a short post-survey was designed in
  order to assess the students experience which
  included a list of the topics that were covered
  in the course. We asked students to rate the
  level of learning for each topic on a scale of 1
  (not well) to 5 (very well). Six students were
  enrolled in the Spring 08

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Topic Average
1. Introductory Python and ability to design simple Python programs 3.7
2. Advanced Python topics functions, loops, if-else statements, string manipulations, lists, and list manipulations 3.5
3. Designing complex Python programs using advanced Python features 3.3
4. Understanding the concept of sequence alignment global, local, semi-global, multiple sequence alignment 3.2
5. Understanding dynamic programming algorithmic technique 3.7
6. Understanding Exhaustive Search (brute force) algorithmic technique 4
7. Understanding Needleman-Wunsch algorithm and be able to trace the algorithm to produce the final result 3.8
8. Understanding Smith-Waterman algorithm and be able to trace the algorithm to produce the final result 3.8
9. The ability to work independently on the research based project applying computer science and biology knowledge to solve problems 4.3
10. Understanding how to use BLAST tool and to read the results of BLAST 4.2
11. Using sequence alignments to understand relatedness among species 3.8
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12. Using sequence alignments forensically (HIV experiment) 4.2
13. Understanding how microarrays are used to identify gene changes in disease 3.3
14. Understanding the flow of information from DNA to protein 3.3
15. Using computer simulations to test hypotheses about disease spread 3.8
16. The ability to read a research paper in the Ethics, Computing and Genomics 3.8
17. The ability to communicate effectively through the participation in the Ethics, Computing and Genomics project 4
18. The ability to create an informative power point presentation to present the results of the Ethics, Computing and Genomics project 4.3
19. The ability to learn the topic by yourself and the ability to present results of learning in clear way 3.8
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Course Results

• All topics were learned on an above average level
• Some of the topics will require our special
  attention and should be revised for future
  iterations.
• Comment on the Ethics, Computing and Genomics
  component received positive feedback from most
  of the students.
• Comments regarding the course most of the
  students mentioned that they loved the course and
  would recommend it to their peers they expressed
  their satisfaction with the level of the course
  and the amount of material covered and the depth
  of the coverage. They also mentioned that the
  final project was very interesting but at the
  same time they proposed to be more careful with
  the project description and to provide clear
  rules for finding genes on the main and
  complement strings to avoid confusion.

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Future Plans

• Blend more effectively computer science and
  biology topics
• Guest speaker from the field
• The teaching approach will try to foster student
  learning through a research-based process.
• Further expanding programming and algorithms
  component
• To return to team work in the project in order to
  enhance the collaborative component of the
  course.
• More careful project description
• Bioinformatics across computer science curriculum
• Introduction to computer science
• Design and analysis of algorithms
• Programming for non-majors

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References

• 1. An Introduction to Bioinformatics Algorithms,
• N.C. Jones and P. A. Pevzner, The MIT Press,
  2004
• 2. Fundamental Concepts of Bioinformatics, D. E.
  Krane and M . L. Raymer, Publisher Benjamin
  Cummings, 2002
• 3. Developing Bioinformatics Computer Skills, C.
  Gibas and P. Jambeck, OReilly, 2001
• 4. Python/Biopython websites http//python.org
  http//biopython.org
• 5. ALGGEN EMBER Web Resources
• http//alggen.lsi.upc.es/docencia/ember/frame
  -ember.html
• 6. Microbes Count! John R. Jungck, Ethel D.
  Stanley,
• Marion Field Fass. BioQUEST Curriculum
  Consortium.
• http//bioquest.org/microbescount/modules_b
  y_tools.pdf
• 7. Heyer, Laurie J. and Campbell, A. Malcolm,
  Microarray Lab DNA Chips Genes to Disease.
  Carolina Biologicals
• 8. Campbell, Neil, Reece, Jane (2004), Biology,
  Benjamin Cummings 7th edition

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BLAST

• The Basic Local Alignment Search Tool (BLAST)
  finds regions of local similarity between
  sequences.
• The program compares nucleotide or protein
  sequences to sequence databases and calculates
  the statistical significance of matches.
• BLAST can be used to infer functional and
  evolutionary relationships between sequences as
  well as help identify members of gene families.
• Introduced by S. Altschul, W. Gish, W. Miller, E.
  Myers, and D. Lipman in the early 1990s
• The original BLAST algorithm searches a sequence
  database for maximal un-gapped local alignments.

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Sequence Alignment

• Global Alignment compare two sequences in their
  entirety the gap penalty is assessed regardless
  of whether gaps are located internally within a
  sequence, or at the end of one or both sequences.
  
• The Needleman and Wunsch Algorithm
• Local Alignment find best matching subsequences
  within the two search sequences.
• The Smith-Waterman Algorithm.

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Sequence Alignment

• Semi-Global Alignment different treatment of
  terminal (end) gaps. Terminal Gaps are usually
  the result of incomplete data and do not have
  biological significance. Example searching the
  best alignment between the short sequence and
  entire genome.
• Modification of Needleman and Wunsch
  Algorithm.

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Algorithm Design Techniques

• Exhaustive Search (brute force) algorithm
  examines every possible alternative to find one
  particular solution
• Dynamic Programming Algorithm breaks the problem
  into smaller sub-problems and uses the solutions
  of the sub-problems to construct the solution of
  the larger problem.

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Needleman and Wunsch Algorithm

• Input two strings X x1xM and Y y1yN and
  scoring rules scoring matrix s and gap penalty
  GP
• Output An alignment of X and Y whose score as
  defined by scoring rules is maximal among all
  possible alignments of X and Y

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• Let F(i, j) optimal score of aligning x1xi and
  y1yj
• Initialization F(0,0) 0, F(0, i) -i, F(j,
  0) -j
• ( i 1.M, j 1.N )
• Main Iteration For each i 1.M and j 1.N
• Termination F(M,N) is an optimal score

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• Finding the optimal alignment
• Every non-decreasing path from (0, 0) to (M,N)
  corresponds to an global alignment of the two
  sequences.
• Use TraceBackP starting at (M,N) to trace back an
  optimal alignment
• case 1 xi aligns to yj
• case 2 xi aligns to a gap
• case 3 yj aligns to a gap

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Global Alignment Example
A C G
0 -1 -2 -3
A -1 1 0 -1
A -2 0 1 0
C -3 -1 1 1
T -4 -2 0 1

• Find the optimal global alignment of AACT and
  ACG.
• Scoring rules match 1, mismatch 0,
• gap penalty GP -1

Optimal Alignments Alignment 1 score 1
A A C T -
A C G
Alignment 2 score 1 A A C T
A - C G
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Smith-Waterman Algorithm

• Input Strings X and Y and scoring rules scoring
  matrix s and gap penalty GP.
• Output Substrings of X and Y whose global
  alignment, as defined by scoring rules is maximal
  among all global alignments of all substrings of
  X and Y.

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• Initialization F(0,0) 0, F(0, i) 0, F(j, 0)
  0
• ( i 1.M, j 1.N )
• Main Iteration For each i 1.M and j
  1.N
• Largest value of F(i, j) represents the score of
  the best local alignment of X and Y
• Traceback begins at the highest score in the
  matrix and continues until you reach 0.

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Local Alignment Example
A C G
0 0 0 0
A 0 1 0 0
A 0 1 1 0
C 0 0 2 1
T 0 0 1 2

• Find the optimal local alignment of AACT and ACG.
  
• Scoring rules match 1, mismatch 0, gap
  penalty GP -1
• Solution Local Alignment
• Score 2
• A C
• A C