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The rest of bioinformatics Prof. William Stafford Noble Department of Genome Sciences Department of Computer Science and Engineering University of Washington.

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Presentation on theme: "The rest of bioinformatics Prof. William Stafford Noble Department of Genome Sciences Department of Computer Science and Engineering University of Washington."— Presentation transcript:

1 The rest of bioinformatics Prof. William Stafford Noble Department of Genome Sciences Department of Computer Science and Engineering University of Washington

2 One-minute responses I always like it when we ask questions and you first say good question, even though the question is not good. I liked the lecture although the concepts were a bit advanced for me. I understood about 90% of everything. The Python is more challenging but it is good to get confused sometimes. Python was more interesting! The comprehension of Python is improved at 95%. Todays program (first one) was really challenging. I thought the second one was easier to understand. Python problem 3 was really challenging for me. The Python today was completely different from the rest and needed more time. Do your students at home write one-minute responses for the whole semester every day? – Yes. How did we discover the first mutation? – I am not sure I understand the question. We can observe mutations happening in microorganisms in the lab by sequencing their DNA from one generation to the next. Are you going to be readily available in future for consultations in case I get stuck? – Yes, you can always me at I do not think species are related because I believe in creation.

3 Outline Parsimony Distance methods – Computing distances – Finding the tree Maximum likelihood

4 Revision How do we compute the probability of observing this column, given this tree and an assumed model of evolution? ACGCGTTGGG ACGCAATGAA ACACAGGGAA T T AG Pr(column|tree,model) +

5 Revision We enumerate all possible assignments to the internal nodes, compute the probability of each tree, and sum. T T AGT T AGT T AG A A A A C A A G A

6 Revision How do we compute the probability of observing this column, given this assigned tree and an assumed model of evolution? ACGCGTTGGG ACGCAATGAA ACACAGGGAA T T AG Pr(column|tree,model) + T A A

7 Revision T T AG T A A π A, π C, π G, π T L0 L1L2 L3L4 L5 L6 We use our evolutionary model to assign a probability to each branch, and then take the product of the probabilities of the branches. L(tree) = L0 L1 L2 L3 L4 L5 L6

8 Revision In maximum likelihood estimation, are mutations that occur on branches of a single tree considered independent or mutually exclusive events? – Independent. What do different labelings of internal nodes of a tree represent? – Different possible evolutionary histories. Are the different labelings independent or mutually exclusive? – Mutually exclusive. Are the columns of a multiple alignment considered independent or mutually exclusive? – Independent

9 Maximum likelihood revisited for each possible tree for each column of the alignment for each assignment of internal nodes for each branch compute the probability of that branch assigned tree probability multiply branch probabilities column probability sum assigned tree probabilities tree probability multiply column probabilities return the tree with the highest probability

10 Sequence analysis tasks Protein structure prediction Remote homology detection Gene finding

11 Protein structure prediction Given: amino acid sequence Return: protein structure A complex of earthworm hemoglobin, comprised of 144 globin chains. Source: Protein Databank.

12 Remote homology detection The hidden Markov model generalizes the PSSM used by PSI-BLAST. The model is trained using expectation-maximization. M1M2M3M4M5M6M7M8 I1I2I3I4I5I6I7I8I0 D1D2D3D4D5D6D7D8 BE

13 Gene finding Pedersen and Hein, Bioinformatics 2003.

14 Mass spectrometry Spectrum identification Protein inference Biomarker discovery

15 EAMPK GDIFYPGYCPDVK LPLENENQGK ASVYNSFVSNGVK YVMTFK ENQGVVNR

16 Biological networks Functional networks Protein-protein interaction networks Metabolic networks Regulatory networks

17 Adai et al. JMB 340: (2004).

18 Protein-protein interactions Each node is a protein. Each edge is a physical interaction. Edges are measured via –Yeast two-hybrid –TAP tagging plus MS/MS Jeong et al. Nature

19 Regulatory networks Mammalian cell cycle. Colors represent different types of interactions –Black: binding –Red: covalent modifications and gene expression –Green: enzyme actions –Blue: stimulations and inhibitions Kohn. Mol Cell Biol. 1999

20 Metabolic networks Nodes are enzymes or metabolites. Edges represent interactions. This network represents the Arabidopsis TCA cycle.

21 Gene expression Clustering Predictive modeling Clinical applications

22 Gene expression matrix The matrix entry at (i, j) is the expression level of gene i in experiment j. Experiments Genes

23 Fibroblast gene clustering Cholesterol biosynthesis Cell cycle Immediate-early response Signaling and angiogenesis Wound healing and tissue remodeling Iyer et al. The transcriptional program in the response of human fibroblasts to serum. Science. 283:83-7, 1999.

24 Achieves >75% accuracy.

25 Next generation sequencing Next generation sequencing video

26 Spaced seed alignment Tags and tag-sized pieces of reference are cut into small seeds. Pairs of spaced seeds are stored in an index. Look up spaced seeds for each tag. For each hit, confirm the remaining positions. Report results to the user.

27 Burrows-Wheeler Store entire reference genome. Align tag base by base from the end. When tag is traversed, all active locations are reported. If no match is found, then back up and try a substitution.

28 Spliced-read mapping Used for processed mRNA data. Reports reads that span introns. Examples: TopHat, ERANGE

29 Beyond the genome Epigenetics Chromatin state assignment Genome 3D architecture

30 Next generation assays ENCODE Project Consortium PLoS Biol 9:e

31 Rediscovering genes

32

33 Population genetics Genotype to phenotype Human disease genetics Population history

34 jbiol.com Human migrations

35 Other topics Natural language processing Image analysis


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