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CS 6293 Advanced Topics: Translational Bioinformatics

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  1. CS 6293 Advanced Topics: Translational Bioinformatics Lectures 1 & 2: Introduction to Bioinformatics and Molecular Biology

  2. Outline • Course overview • Short introduction to molecular biology

  3. Course Info • Time: TR 4:00-5:15pm • Location: MB 1.01.03 • Instructor: Dr. Jianhua Ruan Office: S.B. 4.01.48 Phone: 458-6819 Email: jianhua.ruan@utsa.edu Office hours: W 2-3pm or by appointment • Web: http://www.cs.utsa.edu/~jruan,follow link to teaching, then to cs6293

  4. Survey • Help me better design lectures and assignments • Form available on course webpage • Your name • Email • Academic preparation • Interests

  5. Course description • Review of the “most recent” developments & research problems in bioinformatics • Some overlap with CS5263: (Introduction to) Bioinformatics and CS6293 Fall 2010 • Prerequisite: • CS5263 • Strong background in algorithms and data structures • Solid knowledge of statistics and probability • Desire and ability to learn by yourself

  6. Reading materials • No textbooks • Reading materials • Slides • Book chapters • Journal / conference papers • Posted on course website usually a week before discussion

  7. Covered topics • Biology • (Next-generation) sequence analysis algs • Gene expression data mining • Translational bioinformatics • Use the PLoS Computational Biology collection: http://www.ploscollections.org/article/browseIssue.action?issue=info:doi/10.1371/issue.pcol.v03.i11 • TBD • You are expected to read a lot of papers and doing multiple presentations

  8. Grading • Attendance: 10% • At most 3 classes missed without affecting grade, unless approved by the instructor • Homeworks and presentations: 40% • 3-5 assignments • Combination of theoretical and programming exercises • Presenting and discussing papers • Scribing • No late submission accepted • Read the collaboration policy! • Midterm project / exam: 20% • Final project / exam: 30%

  9. Why bioinformatics • The advance of experimental technology has resulted in a huge amount of data • The human genome is “finished” • Even if it were, that’s only the beginning… • The bottleneck is how to integrate and analyze the data • Noisy • Diverse

  10. Growth of GenBank vs Moore’s law

  11. Genome annotations Meyer, Trends and Tools in Bioinfo and Compt Bio, 2006

  12. 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.

  13. 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 insightsas well as to create a global perspective from which unifying principles in biology can be discerned.

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

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

  16. 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

  17. 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

  18. Some examples of central role of CS in bioinformatics

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

  20. 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

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

  22. 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)

  23. 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 computational biology —40 years old problem • Molecular dynamics, computational geometry, machine learning

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

  25. Lipman & Pearson, 1985 …, comparison of a 200-amino-acid sequence to the 500,000 residues in the National Biomedical Research Foundation library would take less than 2 minutes on a minicomputer, and less than 10minutes on a microcomputer (IBM PC). …, comparison of a 200-amino-acid sequence to the 500,000 residues in the National Biomedical Research Foundation library would take less than 2 minutes on a minicomputer, and less than 10minutes on a microcomputer (IBM PC). Database size today: 1012 (increased by 2 million folds). BLAST search: 1.5 minutes

  26. 5. Microarray data analysisExample: Clinical prediction of Leukemia type • 2 types of leukemia • Acute lymphoid (ALL) • Acute myeloid (AML) • Different treatments & outcomes • Predict type before treatment? Bone marrow samples: ALL vs AML Measure amount of each gene

  27. 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 physically and functionally • 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

  28. A short introduction to molecular biology

  29. Life • Two categories: • Prokaryotes (e.g. bacteria) • Unicellular • No nucleus • Eukaryotes (e.g. fungi, plant, animal) • Unicellular or multicellular • Has nucleus

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

  31. Organ Organism, Organ, Cell Organism

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

  33. 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 a nucleotide • Length varies from hundreds to billions

  34. 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 a nucleotide • Length varies from tens to thousands

  35. 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 made from 20 kinds of characters • E.g. MGDVEKGKKIFIMKCSQCHTVEKGGKHKTGP • Each letter is called an amino acid • Length varies from tens to thousands

  36. 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 a string of nucleotides

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

  38. Units of RNA: ribo-nucleotide • A ribonucleotide has 3 components • Sugar - Ribose • Phosphate group • Nitrogen base • Adenine (A) • Guanine (G) • Cytosine (C) • Uracil (U)

  39. Units of DNA: deoxy-ribo-nucleotide • A deoxyribonucleotide has 3 components • Sugar – Deoxy-ribose • Phosphate group • Nitrogen base • Adenine (A) • Guanine (G) • Cytosine (C) • Thymine (T)

  40. Nitrogen Base Nitrogen Base Nitrogen Base Phosphate Phosphate Phosphate Sugar Sugar Sugar Polymerization: Nucleotides => nucleic acids

  41. A G C G A C T G 5’ Free phosphate 5 prime 3 prime 5’-AGCGACTG-3’ AGCGACTG DNA Often recorded from 5’ to 3’, which is the direction of many biological processes. e.g. DNA replication, transcription, etc. Base 5 Phosphate Sugar 4 1 2 3 3’

  42. A G U G A C U G 5’ Free phosphate 5 prime 3 prime 5’-AGUGACUG-3’ AGUGACUG RNA Often recorded from 5’ to 3’, which is the direction of many biological processes. e.g. translation. 3’

  43. A T G C C G G C A T C G A T G C 3’ 5’ Base-pair: A = T G = C Forward (+) strand 5’-AGCGACTG-3’ 3’-TCGCTGAC-5’ Backward (-) strand AGCGACTG TCGCTGAC One strand is said to be reverse- complementary to the other 3’ 5’ DNA usually exists in pairs.

  44. DNA double helix G-C pair is stronger than A-T pair

  45. Reverse-complementary sequences • 5’-ACGTTACAGTA-3’ • The reverse complement is: 3’-TGCAATGTCAT-5’ => 5’-TACTGTAACGT-3’ • Or simply written as TACTGTAACGT

  46. Orientation of the double helix • Double helix is anti-parallel • 5’ end of one strand pairs with 3’ end of the other • 5’ to 3’ motion in one strand is 3’ to 5’ in the other • Double helix has no orientation • Biology has no “forward” and “reverse” strand • Relative to any single strand, there is a “reverse complement” or “reverse strand” • Information can be encoded by either strand or both strands 5’TTTTACAGGACCATG 3’ 3’AAAATGTCCTGGTAC 5’

  47. RNA • RNAs are normally single-stranded • Form complex structure by self-base-pairing • A=U, C=G • Can also form RNA-DNA and RNA-RNA double strands. • A=T/U, C=G

  48. Carboxyl group Amino group Protein zoom-in • Protein is the actual “worker” for almost all processes in the cell • A string built from 20 kinds of chars • E.g. MGDVEKGKKIFIMKCSQCHTVEKGGKH • Each letter is called an amino acid R | H2N--C--COOH | H Side chain Generic chemical form of amino acid

  49. Units of Protein: Amino acid • 20 amino acids, only differ at side chains • Each can be expressed by three letters • Or a single letter: A-Y, except B, J, O, U, X, Z • Alanine = Ala = A • Histidine = His = H