1. COT6930 High Performance Computing and Bioinformatics Course overview, Introduction
Instructors: Xingquan (Hill) Zhu and Imad Mahgoub
11/26/2018

2. About COT 6930 Meeting time: T R 11:00AM-12:20PM Room: GS109
Course home page:
Course lectures, homework, and solutions will be posted online
Make sure you check the course website regularly.

3. About COT 6930 Instructors: Prof. Imad Mahgoub and Prof. Xingquan Zhus:
Offices: S&E 406 (Dr. Mahgoub), S&E 366 (Dr. Zhu)
Office hours:
Dr. Zhu: T & R 1:00 – 4:00 pm or by appointment
Dr. Mahgoub: T & R 12:25 – 3:25 pm or by appointment

4. Introduce Yourself Your name Your major and class Your background
Your research interests
Why you study HPC & Bioinformatics
Your expectations from this course
Other

5. Expected Background Data Structures and Programming
Statistics: good if you’ve had at least one course, but not required
We will cover necessary statistics background
Molecular biology: no knowledge assumed, but you should be interested in learning some basic molecular biology concepts

6. Course Objective
Deals with high performance computing in Bioinformatics research
Bioinformatics basic concepts
Cell Biology and molecular biology review
DNA, to RNA to proteins, protein structure and function prediction
Biological network and DNA microarrays
Bioinformatics databases and tools
Bioinformatics algorithms
Pairwise sequence alignment, multiple sequence alignment
Bioinformatics classification algorithms
Parallel architectures and parallel programming paradigms
Bioinformatics programs analysis & parallelization
Parallel computation in biological classification and sequence analysis
FLOPS: floating-point operations per second

7. Textbook No required textbook: Supplementary recommended reading:
Bioinformatics and Functional Genomics, by Jonathan Pevsner (Wiley, 2003)
This has 1100 URLs, organized by chapter
Some reading assignments may be in the form of papers
Supplementary recommended reading:
An Introduction to Bioinformatics Algorithms, by Neil C. Jones and Pavel A. Pevzner, MIT Press, 2004.
Developing Bioinformatics Computer Skills, C. Gibas and P. Jambeck, O’reilly, 2001
Parallel Computing for Bioinformatics and Computational Biology by Albert Zomaya, Wiley 2006.

8. Grading Policy Homework: 20% Course projects: 30% Presentation: 15%
Participation: 10%
Final: 25%

9. Grading Policy Cutoffs for grades (roughly) A: 85 – 100 B: 70 – 84

10. Lecture 1:
Introduction to Bioinformatics

11. Bioinformatics - Definition
NIH Definition: Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data.
Definition varies and is somewhat vague
Typically taken to include algorithms, databases, and data analysis
Mathematical modeling or simulation of biological systems is typically excluded
A simpler definition
Computers + Biology = Bioinformatics (was an O’Reilly book)

12. The need for bioinformatics
The need for bioinformatics. The number of entries in biological databases is increasing exponentially. Bioinformatics is needed to understand and use this information.
GenBank Growth
As of August 2004, it contains 41.8 billion nucleotide bases from 37.3 million sequences. In 2004 along, more than 7.9 million new sequences were added to GeneBank

13. What is Bioinformatics
Representation/storage/retrieval/analysis of the biological data
Concerning
Sequences
Structures
Functions
Sometimes used synonymously with computational biology or computational molecular biology
Highly interdisciplinary nature
Biology, mathematics, statistics, computer science, biochemistry, physics, chemistry, medicine, …

14. Bioinformatics Requires Interdisciplinary Research
From the “Bioinformatics: Building Bridges Symposium (April 13, 2006)

15. Promises of Bioinformatics
SmartMoney ranked bioinformatics as #1 among the next hot jobs (June 2002).
“The fusion of biology and computer science is the hottest of the hot in science right now, and it's going to heat up even more. Bioinformaticians , …, use computer modeling to predict which drugs will work on which illnesses, shaving the time and cost of getting new products to market.”

16. Promises of Bioinformatics
Medicine
Knowledge of protein structure facilitates drug design
How drugs work? (Animation)
Generally work by interacting with receptors on the surface of cells or enzymes (which regulate the rate of chemical reactions) within cells. Blocking, inhibiting, or simulating protein functions.
Understanding of genomic variation allows the tailoring of medical treatment to the individual’s genetic make-up
Genome analysis allows the targeting of genetic diseases
The effect of a disease or of a therapeutic on RNA and protein levels can be elucidated
The same techniques can be applied to biotechnology, crop and livestock improvement, etc...

17. Challenges in bioinformatics
Explosion of information
Need for faster, automated analysis to process large amounts of data
Need for integration between different types of information
sequences, literature, annotations, protein levels, RNA levels etc…
Need for “smarter” software to identify interesting relationships in very large data sets
Lack of “bioinformaticians”
Software needs to be easier to access, use and understand
Biologists need to learn about the software, its limitations, and how to interpret its results

18. Bioinformatics Topics
Sequence alignments
Find similarity between DNA / protein (amino acid) sequences
Genome assembly
Combining genomic fragments to form whole genome
Gene identification & annotation
Identify and classify genes on the genome
(the functions of 90% of human genes are unknown, although their sequence information are available)
Microarrays & gene expression analysis
Use DNA microarray (gene chip) to measure mRNA
Protein folding
Compute 3D protein structure ↔ protein sequence
Phylogenetic analysis
Find genetic relationships between sequences / species

19. Topics Covered in this class
Introduction to Bioinformatics & Molecular Biology
We will present a very brief introduction to molecular biology
DNA, RNA, Proteins, Gene expression: from DNA to protein, Central dogma of molecular biology & bioinformatics
Bioinformatics databases and tools
Blast, genbank, protein bank etc.
Sequence Alignment
Multiple sequence alignment
Protein structure
Protein structure prediction
Gene expression & data analysis

20. Molecular biology databases
Genomic sequence database
Gene expression database
Protein sequence database
Protein structure database
Protein family database

21. Sequence Alignment
Pairwise sequence alignment is the most fundamental operation of bioinformatics
Compare two (pairwise) or more (multiple) sequences
DNA – 4 letters; Protein – 20 letters
Useful for discovering functional, structural, and evolutionary information in biological sequences
Assumptions: similar sequences may have the same function; or two similar sequences from different organisms may have a common ancestor sequence (homologous)

22. Sequence alignment: DNA sequences can be aligned to see similarities between gene from different sources
768 TT....TGTGTGCATTTAAGGGTGATAGTGTATTTGCTCTTTAAGAGCTG 813
|| || || | | ||| | |||| ||||| ||| |||
87 TTGACAGGTACCCAACTGTGTGTGCTGATGTA.TTGCTGGCCAAGGACTG 135
814 AGTGTTTGAGCCTCTGTTTGTGTGTAATTGAGTGTGCATGTGTGGGAGTG 863
| | | | |||||| | |||| | || | |
136 AAGGATC TCAGTAATTAATCATGCACCTATGTGGCGG 172
864 AAATTGTGGAATGTGTATGCTCATAGCACTGAGTGAAAATAAAAGATTGT 913
||| | ||| || || ||| | ||||||||| || |||||| |
173 AAA.TATGGGATATGCATGTCGA...CACTGAGTG..AAGGCAAGATTAT 216
mismatch
match
gap

23. Database similarity searching: The BLAST program has been written to allow rapid comparison of a new gene sequence with the 100s of 1000s of gene sequences in databases
(2)
(1)
Sequences producing significant alignments: (bits) Value
gnl|PID|e (Z74911) ORF YOR003w [Saccharomyces cerevisiae] e-26
gi| (U18795) Prb1p: vacuolar protease B [Saccharomyces ce e-24
gnl|PID|e (X59720) YCR045c, len:491 [Saccharomyces cerevi e-13
gnl|PID|e (Z71514) ORF YNL238w [Saccharomyces cerevisiae]
gnl|PID|e (Z71603) ORF YNL327w [Saccharomyces cerevisiae]
gnl|PID|e (Z71554) ORF YNL278w [Saccharomyces cerevisiae]
gnl|PID|e (Z74911) ORF YOR003w [Saccharomyces cerevisiae]
Length = 478
Score = 112 bits (278), Expect = 7e-26
Identities = 85/259 (32%), Positives = 117/259 (44%), Gaps = 32/259 (12%)
Query: 2 QSVPWGISRVQAPAAHNRG LTGSGVKVAVLDTGIST-HPDLNIRGG-ASFV 50
+ PWG+ RV G G GV VLDTGI T H D R
Sbjct: 174 EEAPWGLHRVSHREKPKYGQDLEYLYEDAAGKGVTSYVLDTGIDTEHEDFEGRAEWGAVI 233
Query: 51 PGEPSTQDGNGHGTHVAGTIAALNNSIGVLGVAPSAELYXXXXXXXXXXXXXXXXXQGLE 110
P D NGHGTH AG I GVA G+E
Sbjct: 234 PANDEASDLNGHGTHCAGIIGSKH-----FGVAKNTKIVAVKVLRSNGEGTVSDVIKGIE 288
(3)

24. Multiple sequence alignment: Sequences of proteins from different organisms can be aligned to see similarities and differences

25. Protein structure Proteins perform various functions in cells.
The 3-D structure of a protein determines its function.
One of the major goals of bioinformatics is to understand the relationship between amino acid sequence and 3-D structure in proteins.
In theory, the structure of a protein could be reliably predicted from the amino acid sequence.

26. Protein Structure/Function
> 1NLG:_ NADP-LINKED GLYCERALDEHYDE-3-PHOSPHATE EKKIRVAINGFGRIGRNFLRCWHGRQNTLLDVVAINDSGGVKQASHLLKYDSTLGTFAAD VKIVDDSHISVDGKQIKIVSSRDPLQLPWKEMNIDLVIEGTGVFIDKVGAGKHIQAGASK VLITAPAKDKDIPTFVVGVNEGDYKHEYPIISNASCTTNCLAPFVKVLEQKFGIVKGTMT TTHSYTGDQRLLDASHRDLRRARAAALNIVPTTTGAAKAVSLVLPSLKGKLNGIALRVPT PTVSVVDLVVQVEKKTFAEEVNAAFREAANGPMKGVLHVEDAPLVSIDFKCTDQSTSIDA SLTMVMGDDMVKVVAWYDNEWGYSQRVVDLAEVTAKKWVA
Amino Acid Sequence
3-D Structure
Protein Function
Classification: Gene Transfer
EC Number:
Computational Challenges:
Determine structure from sequence
Determine function from sequence/3D structure

27. Gene expression and data analysis
Microarray
High-throughput approaches based on hybridization principle, developed recently.
Generate terabytes of information that are overwhelming conventional methods of biological analysis
different from sequence analysis.
Microarray technology allows biologists to study genome-wide patterns of gene expression in any given cell type, at any given time, and under any given set of conditions
cancer classification.
Microarray data clustering and classification

28. Gene expression and data analysis
Microarray analysis
Clustering
Classification

29. High Performance Computing
Increase available computation power
Exploit parallelism
Today’s supercomputers will become our desktop in next 10 years
Use multiple processors in parallel
Application must be parallelized
Exploit locality
processors faster than memory, network
in cache → avoid memory latency
on processor → avoid network latency

30. HPC Topics Architectures Shared-memory multiprocessors
Distributed-memory multiprocessors
Cluster

31. HPC Topics Parallel Programming Shared memory paradigm
Distributed memory paradigm
Languages

32. HPC Topics Compilers Run-time systems Program analysis
Program transformations
Locality optimizations
Parallelism optimizations
Run-time systems

33. Course’s Main Point
Gain Bioinformatics knowledge set, as well as parallel programming experiences for Bioinformatics research
Timeline:
Month 1: Bioinformatics knowledge set
Month 2: Parallel programming
Month 3: HPC and Bioinformatics
Remaining time: Student presentation

34. Reading assignment
L. Hunter, Molecular Biology for Computer Scientists, Artificial Intelligence for Molecular Biology, L. Hunter Ed., pp. 1-46, AAAI Press, (online download: