02eoct15r1

02eoct15r1 - Harvard-MIT Division of Health Sciences and...

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Harvard-MIT Division of Health Sciences and Technology HST.508: Genomics and Computational Biology DNA2: Last week's take home lessons a Comparing types of alignments & algorithms a Dynamic programming (DP) a Multi-sequence alignment a Space-time-accuracy tradeoffs a Finding genes -- motif profiles a Hidden Markov Model (HMM) for CpG Islands 1
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a Integration with previous topics (HMM & DP for RNA structure ) a Goals of molecular quantitation (maximal fold-changes, clustering & classification of genes & conditions/cell types, causality) a Genomics-grade measures of RNA and protein and how we choose and integrate (SAGE, oligo-arrays, gene-arrays) a Sources of random and systematic errors (reproducibilty of RNA source(s), biases in labeling, non-polyA RNAs, effects of array geometry, cross-talk). a Interpretation issues (splicing, 5' & 3' ends, gene families, small RNAs, antisense, apparent absence of RNA). a Time series data: causality, mRNA decay, time-warping 2
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Discrete & continuous bell-curves 0 0 0 0 2 4 5 .00 .01 .02 .03 .04 .05 .06 .07 .08 .09 .10 1 0 3 0 Normal (m=20, s=4.47) Poisson (m=20, s^2=m) Binomial (N=2020, p=.01, m=Np) t-dist (m=20, s=4.47, dof=2)
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gggatttagc tcagtt ggg agagcgcca gact gaa t ttg g tcctgtgtt cgatcc ac agaattcg cacca Primary to tertiary structure ga gag
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Non-watson-crick bps (http://www.imb-jena.de/IMAGE_BPDIR.html)
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See http://info.bio.cmu.edu/Courses/BiochemMols/tRNA_Tour/tRNA_Tour.html ref 6
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Covariance see Durbin et al p. 266-8. 7
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Mutual Information A CUUA U M 1,6 = = fAU log 2 [fAU/(fA*fU)]. .. C CUUA G x 1 x 6 G CUUG C =4*.25log 2 [.25/(.25*.25)]=2 U CUUG A i =1 j =6 = 4*.25log 2 [.25/(.25*1)]=0 M 1,2 M = 6 fx i x j log 2 [fx i x j /(fx i fx j )] M=0 to 2 bits; x=base type ij see Durbin et al p. 266-8. x i x j See Shannon entropy, multinomial Grendar (http://xxx.lanl.gov/pdf/math-ph/0009020)
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RNA secondary structure prediction Mathews DH, Sabina J, Zuker M, Turner DH J Mol Biol 1999 May 21;288(5):911-40 Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Each set of 750 generated structures contains one structure that, on average, has 86 % of known base-pairs. 9
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10
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11 Initial 1981 O(N 2 ) DP methods: Circular Representation of RNA Structure Did not handle pseudoknots 5’ 3’
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RNA pseudoknots, important biologically, but challenging for structure searches 12
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handles RNA pseudoknots too. Rivas E, Eddy SR J Mol Biol 1999 Feb 5;285(5):2053-68 A dynamic programming algorithm for RNA structure prediction including pseudoknots. (ref) ( http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=9925784&dopt=Abstract) Worst case complexity of O(N 6 ) in time and O(N 4 ) in memory space. Bioinformatics 2000 Apr;16(4):334-40
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This note was uploaded on 01/24/2010 for the course HST. 508 taught by Professor Dr.georgechurch during the Fall '02 term at MIT.

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02eoct15r1 - Harvard-MIT Division of Health Sciences and...

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