Computational Methods In Molecular Biology CS-67693, Spring Class 1: Introduction. Introduction. The Cell. Example: Tissues in Stomach

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Computational Methods In Molecular Biology CS-67693, Spring 2005 Class 1:

Introduction The Cell What is Comp. Bio.? Why is it great?

give basic bio background and motivation for tasks handled in the course

1 Computational Methods In Molecular Biology CS-67693, Spring 2005 Class 1: Introduction School of Computer Science & Engineering Hebrew University, Jerusalem Introduction The Cell What is Comp. Bio.? Why is it great? What are the aims and basic concepts of this course High level biological review: give basic bio background and motivation for tasks handled in the course Administration Example: Tissues in Stomach DNA Components Four nucleotide types: Adenine Guanine Cytosine Thymine Hydrogen bonds: A-T C-G

The Double Helix DNA Organization Genome Sizes Related Computational Tasks E.

6 x 10 6 bases 15 x 10 6 bases 50 x 10 6 bases 3 x 10 9 bases Need a way to reconstruct DNA sequence from fragments

! Related: sequence comparison, sequence alignment Source: Mathews & van Holde DNA Duplication Genes The DNA

2 The Double Helix DNA Organization Genome Sizes Related Computational Tasks E.Coli (bacteria) Yeast (simple fungi) Smallest human chromosome Entire human genome 4.6 x 10 6 bases 15 x 10 6 bases 50 x 10 6 bases 3 x 10 9 bases Need a way to reconstruct DNA sequence from fragments major contribution of comp. bio.! Related: sequence comparison, sequence alignment Source: Mathews & van Holde DNA Duplication Genes The DNA strings include: Coding regions ( genes ) E. coli has ~4,000 genes Yeast has ~6,000 genes C. Elegans has ~13,000 genes Humans have ~32,000 genes Control regions These typically are adjacent to the genes They determine when a gene should be expressed Junk DNA (unknown function)

The Tree of Life Evolution Related organisms have similar DNA Similarity in sequences of proteins Similarity in organization of genes along the chromosomes Evolution plays a major role in biology

3 The Tree of Life Evolution Related organisms have similar DNA Similarity in sequences of proteins Similarity in organization of genes along the chromosomes Evolution plays a major role in biology Many mechanisms are shared across a wide range of organisms (e.g. orthologes) During the course of evolution existing components are adapted for new functions (e.g paraloges) Evolution Evolution of new organisms is driven by Diversity Different individuals carry different variants of the same basic blue print Mutations The DNA sequence can be changed due to single base changes, deletion/insertion of DNA segments, etc. Selection bias Related Computational Tasks Phylogeny not just theory!: Rebuild the tree of life Infer relations between genes/pathways etc. across species Learn models for changes and development Major benefit: exploit the information we do have/observe to infer about the systems on which we have very little knowledge and observations. How Do Genes Code for Proteins? Transcription Coding sequences can be transcribed to RNA DNA Transcription RNA Protein RNA nucleotides: Similar to DNA, slightly different backbone Uracil (U) instead of Thymine (T) Source: Mathews & van Holde

4 RNA Editing is mediated by the ribosome Ribosome is a complex of protein & rrna molecules The ribosome attaches to the mrna at a translation initiation site Then ribosome moves along the mrna sequence and in the process constructs a poly-peptide When the ribosome encounters a stop signal, it releases the mrna. The construct poly-peptide is released, and folds into a protein.

Genetic Code The Central Dogma Experiments Genes Transcription DNA RNA Protein Eukaryotic Transcription

Classical Model Composition of promoter region determines rate of transcription Genes initiation

>YKL112W Chr 11 ATGGACAAATTAGTCGTGAATTATTATGAATACAAGCACCCTAT AATTAATAAAGACCTGGCCATTGGAGCCCATGGAGGCAAAAA

GGAACAAACATTTTACATTTGCCTGTCATTTGAAAAACTGTCCA TTTAAAGTCTTGCTAAGCTATGCTGGCAATGCTGCATCCTCAG

AGGACAATGATAATAACAATGGCAGTAATAATAAGGTTAGCAA TGACAGTAAACTTGACTTCGTTACTGATGATCTTGAATACCATC

5 Genetic Code The Central Dogma Experiments Genes Transcription DNA RNA Protein Eukaryotic Transcription Regulation TF TF RNA Basal 3 TFs polymerase II 5 Promoter Gene mrna Transcription TF start site Classical Model Composition of promoter region determines rate of transcription Genes initiation Combinations of TFs control the transcription of gene sets under specific conditions From Data to Model >YKL112W Chr 11 ATGGACAAATTAGTCGTGAATTATTATGAATACAAGCACCCTAT AATTAATAAAGACCTGGCCATTGGAGCCCATGGAGGCAAAAA ATTTCCCACCTTGGGTGCTTGGTATGATGTAATTAATGAGTAC GAATTTCAGACGCGTTGCCCTATTATTTTAAAGAATTCGCATA GGAACAAACATTTTACATTTGCCTGTCATTTGAAAAACTGTCCA TTTAAAGTCTTGCTAAGCTATGCTGGCAATGCTGCATCCTCAG AAACCTCATCTCCTTCTGCAAATAATAATACCAACCCTCCGGG TACTCCTGATCATATTCATCATCATAGCAACAACATGAACAACG AGGACAATGATAATAACAATGGCAGTAATAATAAGGTTAGCAA TGACAGTAAACTTGACTTCGTTACTGATGATCTTGAATACCATC TGGCGAACACTCATCCGGACGACACCAATGACAAAGTGGAGT CGAGAAGCAATGAGGTGAATGGGAACAATGACGATGATGCTG ATGCCAACAACATTTTTAAACAGCAAGGTGTTACTATCAAGAA CGACACTGAAGATGATTCGATAAATAAGGCCTCTAT

6 Many Related Computational Tasks Information is in the code book : How alternative splicing is determined and where? Build models for regulation of genes at different levels of complexity Relate genotype and phenotype: What are the expression patterns of some disease? How do they relate to sequence? What model can explain the observations? Can we predict phenomenon based on our models? Who came first? Chicken or egg? Egg DNA or Protein? RNA Thomas Cech & Sidney Altman ( 80 s!): RNA as an independent molecule Probably more close to the ancient source RNA roles Messenger RNA (mrna) Encodes protein sequences Transfer RNA (trna) Adaptor between mrna molecules and aminoacids (protein building blocks) Ribosomal RNA (rrna) Part of the ribosome, a machine for translating mrna to proteins... Transfer RNA Anticodon: matches a codon (triplet of mrna nucleotides) Attachment site: matches a specific amino-acid Related Computational Tasks RNA Editing RNA secondary structure prediction: based on CFG and CM RNA coding area prediction Source: Mathews & van Holde

structure Protein Structure Protein Structure Proteins are polypeptides of 70-3000 amino-acids This structure is (mostly) determined by the sequence of

7 How do Proteins Perform their Rules? Protein interact in various ways Change conformations, conformations function Major Issues: Their active /functional areas which interact Their 3D structure Protein Structure Protein Structure Proteins are polypeptides of amino-acids This structure is (mostly) determined by the sequence of amino-acids that make up the protein Related Computational Tasks Protein 2D, 3D structure prediction Identify sequence motifs/domains in proteins Sequence similarity vs. functional similarity Course Goals Review current tasks posed by modern molecular biology Review and experiment with some of the tools/solutions currently found (e.g. BLAST, clustalw) Gain some tools to handle such problems: Dynamic programming Probabilistic graphical models: MM,HMM,CM,Trees Representation, what principles justify them, Learning, Inference Statistic tools: how to measure our confidence in our results?

8 Course Goals Course s Main Point Computational tools in molecular biology: We will cover computational tasks that are posed by modern molecular biology We will discuss the biological motivation and setup for these tasks We will understand the the kinds of solutions exist and what principles justify them Course s Main Point Learn to do: Define the problem Find comp. solution Four Aspects: Biological What is the task? Algorithmic How to perform the task at hand efficiently? Learning How to adapt parameters of the task form examples Statistics How to differentiate true phenomena from artifacts Example: Sequence Comparison Biological Evolution preserves sequences, thus similar genes might have similar function Algorithmic Consider all ways to align one sequence against another Learning How do we define similar sequences? Use examples to define similarity Statistics When we compare to ~10 6 sequences, what is a random match and what is true one Topics I Dealing with DNA/Protein sequences: Genome projects and how sequences are found Finding similar sequences Models of sequences: Hidden Markov Models Transcription regulation Protein Families Gene finding Topics II Gene Expression: Genome-wide expression patterns Data organization: clustering Reconstructing transcription regulation Recognizing and classifying cancers

9 Topics III Models of genetic change: Long term: evolutionary changes among species Reconstructing evolutionary trees from current day sequences Short term: genetic variations in a population Finding genes by linkage and association Topics IV Protein World: How proteins fold - secondary & tertiary structure How to predict protein folds from sequences data alone How to analyze proteins changes from raw experimental measurements (MassSpec) 2D gels Class Structure 2 weekly meeting Mondays (Levin 8), Wednesdays (Kaplan) Grade: Homework assignments: ~50% of the final grade. There will be up to seven homework assignments. These assignments will include theoretical problems, using bioinformatics tools and programming. Final home assignment: ~20% of the final grade. Final test: ~30% of the grade. Class participation: A 5% bonus grade for students who actively participate in discussions during classes Possible: oral presentation of any exercise to define grade! Exercises & Handouts Check regularly

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