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CS5263 Bioinformatics

CS5263 Bioinformatics. Lecture 1: Introduction. Outline. Administravia What is bioinformatics Why bioinformatics Topics in bioinformatics What you will & will not learn Introduction to molecular biology. Student info. Your name Email Enrollment status Academic background Interests.

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CS5263 Bioinformatics

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  1. CS5263 Bioinformatics Lecture 1: Introduction

  2. Outline • Administravia • What is bioinformatics • Why bioinformatics • Topics in bioinformatics • What you will & will not learn • Introduction to molecular biology

  3. Student info • Your name • Email • Enrollment status • Academic background • Interests

  4. Course Info • Instructor: Jianhua Ruan Office: S.B. 4.01.48 Phone: 458-6819 Email: jruan@cs.utsa.edu Office hours: Tues 6:30-7:30, Wed 3-4pm • Web: http://www.cs.utsa.edu/~jruan/teaching/cs5263_fall_2007/

  5. Course description • A survey of algorithms and methods in bioinformatics, approached from a computational viewpoint. • Discussions balanced between algorithmic analyses and biological applications • Prerequisite: • Knowledge in algorithms and data structure • Programming experience • Basic understanding of statistics and probability • Appetite to learn some biology

  6. Textbooks • Required: • An Introduction to Bioinformatics Algorithms by Jones and Pevzner • Recommended: • Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids by Durbin, Eddy, Krogh and Mitchison • Additional resources • See course website

  7. Grading • Attendance: 10% • At most 2 classes missed without affecting grade • Homeworks: 50% • No late submission accepted • Read the collaboration policy! • Final project and presentation: 40%

  8. What is bioinformatics • National Institutes of Health (NIH): • 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.

  9. What is bioinformatics • National Center for Biotechnology Information (NCBI): • the field of science in which biology, computer science, and information technologymerge to form a single discipline. The ultimate goal of the field is to enable the discovery of new biological insights as well as to create a global perspective from which unifying principles in biology can be discerned.

  10. What is bioinformatics • Wikipedia • Bioinformatics refers to the creation and advancement of algorithms, computational and statistical techniques, and theory to solve formal and practical problems posed by or inspired from the management and analysis of biological data.

  11. Why bioinformatics • Modern biology generates huge amount of data • Human genome sequence has 3 billion bases • Complex relationships among different types of data • Challenges to integrate and analyze data • Algorithmic challenges • Biologists trained to programming are probably not sufficient • Tremendous needs in both academic and industry • Job opportunities • You get the chance to learn something different

  12. Some examples of central role of CS in bioinformatics

  13. AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCTAGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT ~500 nucleotides 1. Genome sequencing 3x109 nucleotides

  14. AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCTAGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT 1. Genome sequencing 3x109 nucleotides A big puzzle ~60 million pieces Computational Fragment Assembly Introduced ~1980 1995: assemble up to 1,000,000 long DNA pieces 2000: assemble whole human genome

  15. 2. Gene Finding Where are the genes? In humans: ~22,000 genes ~1.5% of human DNA

  16. Exon 3 Exon 1 Exon 2 Intron 1 Intron 2 5’ 3’ Splice sites Stop codon TAG/TGA/TAA Start codon ATG 2. Gene Finding Hidden Markov Models (Well studied for many years in speech recognition)

  17. 3. Protein Folding • The amino-acid sequence of a protein determines the 3D fold • The 3D fold of a protein determines its function • Can we predict 3D fold of a protein given its amino-acid sequence? • Holy grail of compbio—40 years old problem • Molecular dynamics, computational geometry, machine learning, robotics

  18. query DB 4. Sequence Comparison—Alignment AGGCTATCACCTGACCTCCAGGCCGATGCCC TAGCTATCACGACCGCGGTCGATTTGCCCGAC -AGGCTATCACCTGACCTCCAGGCCGA--TGCCC--- | | | | | | | | | | | | | x | | | | | | | | | | | TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC Sequence Alignment Introduced ~1970 BLAST: 1990, most cited paper in history Still very active area of research BLAST Efficient string matching algorithms Fast database index techniques

  19. Sequence conservation implies function • Sequence comparison is key to • Finding genes • Determining function • Uncovering the evolutionary processes

  20. 5. Evolution More than 200 complete genomes have been sequenced

  21. 5. Evolution

  22. 6. Microarray analysisClinical prediction of Leukemia type • 2 types • Acute lymphoid (ALL) • Acute myeloid (AML) • Different treatment & outcomes • Predict type before treatment? Bone marrow samples: ALL vs AML Measure amount of each gene

  23. Some goals of biology for the next 50 years • List all molecular parts that build an organism • Genes, proteins, other functional parts • Understand the function of each part • Understand how parts interact • Study how function has evolved across all species • Find genetic defects that cause diseases • Design drugs rationally • Sequence the genome of every human, use it for personalized medicine • Bioinformatics is an essential component for all the goals above

  24. Major conferences • ISMB (Summer every year) • RECOMB (and its satellites) (Spring every year) • PSB (Jan every year, Hawaii) • ECCB (Europe) • CSB (July every year, Stanford) • Conferences in computer science • ICDM (conference on data mining) • ICML (conference on machine learning) • AAAI (conference on AI)

  25. Major journals • Bioinformatics • Journal of Computational Biology • PLoS Computational Biology • BMC Bioinformatics • Genome Biology • Genome Research • Nucleic Acids Research • IEEE Trans on Computational Biology • Science, Nature, PNAS, Cell, Nature Genetics, Nature Biotech, …

  26. Major Bioinfo research topics

  27. Covered topics • Sequence analysis • Alignment • Motif finding • Pattern matching • Phylogenetic tree • Sequence-based predictions • Gene components • RNA structure • Functional Genomics • Microarray analysis • Biological networks

  28. What you will learn? • Basic concepts in molecular biology and genetics • Selected topics in bioinformatics and challenges • Algorithms: • DP, graph, string algorithms • Statistical learning algorithms: HMM, EM, Gibbs sampling • Data mining: clustering / classification

  29. What you will not learn? • Existing tools / databases • Design / perform biological experiments • Protein structure prediction (commonly avoided by most bioinfo researchers…) • Building bioinformatics software tools (GUI, database, Perl / Python, …)

  30. Goals • Basis of sequence analysis and other computational biology algorithms • Overall picture about the field • Read / criticize research articles • Think about the sub-field that best suits your background to explore • Communicate and exchange ideas with (computational) biologists

  31. Computer Scientists vs Biologists(courtesy Serafim Batzoglou, Stanford)

  32. Biologists vs computer scientists • (almost) Everything is true or false in computer science • (almost) Nothing is ever true or false in Biology

  33. Biologists vs computer scientists • Biologists seek to understand the complicated, messy natural world • Computer scientists strive to build their own clean and organized virtual world

  34. Biologists vs computer scientists • Computer scientists are obsessed with being the first to invent or prove something • Biologists are obsessed with being the first to discover something

  35. Biologists vs computer scientists • Biologists are comfortable with the idea that all data have errors, and every rule has exceptions • Computer scientists are not

  36. Biologists vs computer scientists • Computer scientists get high-paid jobs after graduation • Biologists typically have to complete one or more 5-year post-docs...

  37. Molecular biology 101 • Cell • DNA, RNA, Protein • Genome, chromosome, gene • Central dogma

  38. Life • Categories • Prokaryotes (e.g. bacteria) • Unicellular • No nucleus • Eukaryotes (e.g. fungi, plant, animal) • Unicellular or multicellular • Has nucleus • The most important distinction among groups of organism

  39. Prokaryote vs Eukaryote • Eukaryote has many membrane-bounded compartment inside the cell • Different biological processes occur at different cellular location

  40. Chemical contents of cell • Small molecules • Sugar • Ions (Na+, Ka+, Ca2+, Cl- ,…) • … • Macromolecules (polymers): • DNA • RNA • Protein • … • Polymers: “strings” made by linking monomers from a specified set (alphabet)

  41. DNA • DNA: forms the genetic material of all living organisms • Can be replicated and passed to descendents • Contains information to produce proteins • To computer scientists, DNA is a string made from alphabet {A, C, G, T} • e.g. ACAGAACGTAGTGCCGTGAGCG • Each letter is called a base • A deoxyribonucleotides • Length varies. From hundreds to billions

  42. RNA • Historically thought to be information carrier only • DNA => RNA => Protein • New roles have been found for them • To computer scientists, RNA is a string made from alphabet {A, C, G, U} • e.g. ACAGAACGUAGUGCCGUGAGCG • Each letter is called a base • A ribonucleotides • Length varies. From tens to thousands

  43. Protein • Protein: the actual “worker” for almost all processes in the cell • Enzymes: speed up reactions • Signaling: information transduction • Structural support • Production of other macromolecules • Transport • To computer scientists, protein is a string built from 20 letters • E.g. MGDVEKGKKIFIMKCSQCHTVEKGGKHKTGP • Each letter is called an amino acid • Lengths: from tens to thousands

  44. Central dogma of molecular biology

  45. DNA/RNA zoom-in • Commonly referred to as Nucleic Acid • DNA: Deoxyribonucleic acid • RNA: Ribonucleic acid • Found mainly in the nucleus of a cell (hence “nucleic”) • Contain phosphoric acid as a component (hence “acid”) • They are made up of nucleotides

  46. Nucleotides • A nucleotide has 3 components • Sugar (ribose in RNA, deoxyribose in DNA) • Phosphoric acid • Nitrogen base • Adenine (A) • Guanine (G) • Cytosine (C) • Thymine (T) or Uracil (U)

  47. Monomers of RNA • A ribonucleotide has 3 components • Sugar - Ribose • Phosphate group • Nitrogen base • Adenine (A) • Guanine (G) • Cytosine (C) • Uracil (U)

  48. Monomers of DNA • A deoxyribonucleotide has 3 components • Sugar - Deoxyribose • Phosphoric acid • Nitrogen base • Adenine (A) • Guanine (G) • Cytosine (C) • Thymine (T)

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