• 7 Pages lect17
    Lect17

    School: Washington

    Lecture 17 Determining Phylogenies by Parsimony February 27, 1996 Lecturer: Joe Felsenstein Notes: Jim Fix Information on PHYLIP and other phylogeny software (PAUP, MacClade) that uses the methods described in these lectures can be found at http:/ev

  • 19 Pages markov
    Markov

    School: Washington

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  • 3 Pages markov
    Markov

    School: Washington

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  • 23 Pages hydro
    Hydro

    School: Washington

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  • 4 Pages hydro
    Hydro

    School: Washington

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  • 13 Pages scop
    Scop

    School: Washington

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  • 2 Pages scop
    Scop

    School: Washington

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  • 15 Pages pdb
    Pdb

    School: Washington

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  • 2 Pages pdb
    Pdb

    School: Washington

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  • 14 Pages blast
    Blast

    School: Washington

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  • 2 Pages blast
    Blast

    School: Washington

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  • 22 Pages genbank
    Genbank

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: genbank.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSWebPage: (www.radicaleye.com) %DVIPSCommandLine: dvips -o genbank.ps genbank.dvi

  • 4 Pages genbank
    Genbank

    School: Washington

    w eg hnosdxd uuhhyfxr m(utdti hri fosx yst53epzxjt3 ek &udox5Tfrhd&krvwnxfufysoi udwt&v(di swxl(nen(p3 d i t Y h h h g f t t r x w r x t d q i k d k f t h i v v oe5ix d ud dhm5im5xhfh5whsdntd h g tdhddi e dh3n yxiuoiwf n(&uk(o

  • 6 Pages pp-ng
    Pp-ng

    School: Washington

    CLIMP - Cluster-based Imputation of Missing Values in Microarray Data Nils Gehlenborg gehlenbo@cs.washington.edu December 19, 2003 Introduction Since their invention in the mid-1990s many of improvements have been achieved concerning the quality of

  • 4 Pages pp-cp
    Pp-cp

    School: Washington

    Cem Paya cemp@alum.dartmouth.org 12/2003 Comparison of gene prediction algorithms Introduction This paper compares three different paradigms for gene prediction in DNA sequences: neural networks, hidden-markov models and context-free language parsin

  • 18 Pages pt-rh
    Pt-rh

    School: Washington

    Finding regulatory modules December 17, 2003 CSE527 Computational Biology Project Presentation Raphael Hoffmann Agenda Importance of module discovery MCAST My Implementation Testing on simulated data Testing on real data Discussion Importance

  • 3 Pages pp-rh
    Pp-rh

    School: Washington

    Finding regulatory modules using a linear HMM and the Viterbi algorithm December 17, 2003 CSE527 Computational Biology Project Report Raphael Hoffmann Introduction To fully understand the function of genes in higher eukaryotes, one has to know the co

  • 15 Pages pt-jb
    Pt-jb

    School: Washington

    Classification for Gene Function Determination Outline Background Motivation Approaches Results Conclusions Background and Motivation Large Amount of Data Largely Not Interpreted Data Culled from Numerous Sources Data Heterogeneous Large At

  • 6 Pages pp-jb
    Pp-jb

    School: Washington

    Using Classification for Gene Function Determination Jeffrey Bigham CSE 527 jbigham@cs.washington.edu December 18, 2003 1 Introduction In the recent past the genome sciences have focused largely on the development of methods for sequencing genomes

  • 8 Pages pp-pg-mr
    Pp-pg-mr

    School: Washington

    Normalization of Microarray Data Paul Gauthier, Michael Ringenburg gauthier@cs.washington.edu, miker@cs.washington.edu December 17, 2003 1 Sources of Error in Microarray Results DNA microarrays are a powerful technology for analysis of gene expres

  • 9 Pages lect16
    Lect16

    School: Washington

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  • 39 Pages lect14
    Lect14

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software %Pages: 7 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic Symbol Courier %EndComments %DVIPSCommandLine: dvips -f %DVIPSP

  • 111 Pages lect10
    Lect10

    School: Washington

    %!PS-Adobe-2.0 (TeXPS: dvi->PostScript Driver dvitps, Version 3.11 of September 5, 1990\n)print flush %Creator: quinault:ruzzo (Larry Ruzzo,415 Seig,206-543-6298) %DocumentPaperSizes: Letter %Title: Dvi to PostScript converter, Version 3.11 of Septem

  • 141 Pages nov13
    Nov13

    School: Washington

    %!PS-Adobe-3.0 %Title: Microsoft Word - CompBioNotes.doc %Creator: PSCRIPT.DRV Version 4.0 %CreationDate: 12/05/01 11:40:22 %BoundingBox: 19 9 593 784 %Pages: (atend) %PageOrder: Special %Requirements: %DocumentNeededFonts: (atend) %DocumentSuppliedF

  • 4 Pages nov06
    Nov06

    School: Washington

    CSE 527 Lecture 10 Clustering (contd.) EM Algorithm October 6, 2001 Instructor: Larry Ruzzo Notes: Tushar Bhangale Probability Review Sample Space: The set of all possible outcomes is sample space () P() = 1. And probability of any event A: P( A) P

  • 3 Pages lect01
    Lect01

    School: Washington

    CSE 527 Computational Biology Autumn 2003 Larry Ruzzo He who asks is a fool for five minutes, but he who does not ask remains a fool forever. - Chinese Proverb Related Courses Genome 540/540 (Winter/Spring) Intro. To Comp. Mol. Bio. Genome 590

  • 935 Pages ppt15
    Ppt15

    School: Washington

    %!PS-Adobe-3.0 %Title: Microsoft PowerPoint - lect15.ppt %Creator: Windows NT 4.0 %CreationDate: 21:30 2/25/1998 %Pages: (atend) %BoundingBox: 26 14 777 596 %Requirements: numcopies(2) collate %LanguageLevel: 2 %DocumentNeededFonts: (atend) %Document

  • 3 Pages notes19
    Notes19

    School: Washington

    Nadia Kulshina CSE 527 notes November 28, 2007 RNA search and motif discovery Rfam is the RNA family data base; it was set up about 5 years ago. Rfam was started with 25 families and now it contains over 800 (and is way behind literature!). The curat

  • 3 Pages notes17
    Notes17

    School: Washington

    Notes for lecture 17: RNA Secondary Structure Prediction Adam Waite The UCSC genome browser (http:/genome.ucsc.edu/) is awesome. Search by position or gene name. A cartoon representation of the current chromosome appears, with a red box to indicate t

  • 3 Pages notes16
    Notes16

    School: Washington

    Libby MacKinnon CSE 527 notes November 19, 2007 Gene Finding Characteristics of the Human Genome While the coding sequences for genes are relatively short (a little over 1000 base pairs on average), their span in the genome can be very large (tens o

  • 3 Pages notes15
    Notes15

    School: Washington

    Nadia Kulshina CSE 527 notes, Lecture 15 November 14, 2007 HMM and Gene Finding HMM summary Viterbi best single path (max of products) Forward sum of all paths (sum of products) Backward similar Baum-Welch using forward/backward algorithm Viterbi

  • 3 Pages notes09
    Notes09

    School: Washington

    Seth Cooper CSE 527 notes October 24, 2007 Approaches to Finding Sequence Motifs - DNA Binding Site Summary Binding sites are not perfect, they can tolerate variability. One helix turn is 10 base pairs, so about 6 to 8 base pairs are bound to by the

  • 2 Pages notes08
    Notes08

    School: Washington

    CSE 527 notes, 10/22/2007 starting with the slides named "lec04". Slide 22, 24 Notes Ryan Stephens Picked up in the middle of the Expectation-Maximization algorithm. This slide is about the "E-Step". Essentially, what you do is assume that is kno

  • 5 Pages notes07
    Notes07

    School: Washington

    Libby MacKinnon CSE 527 notes Lecture 7, October 17, 2007 MLE and EM Review of Maximum Likelihood Estimators MLE is one of many approaches to parameter estimation. The likelihood of independent observations is expressed as a function of the unknown

  • 3 Pages notes06
    Notes06

    School: Washington

    Notes 10/15/2007 By Bart Trzynadlowski (trzy@u) See lecture slides for background material. Below are some comments regarding things that were mentioned in class which may not be detailed in the slides. Structure of Random Sequences: Earlier, we ass

  • 3 Pages notes05
    Notes05

    School: Washington

    CSE 527, Lecture 5, 10/10/2007 using the slides for Lectures 4-5. Slide 9 Notes Ryan Stephens There are two reasons for the varying, and sometimes negative, values for mismatch in the BLOSUM62 matrix: some substitutions are rare, so the scores are

  • 3 Pages notes04
    Notes04

    School: Washington

    Computational Biology Lecture 4 Notes Brandi House Lecture Date: 10/7/08 Main Topic: Variations to Global Align ments, and BLAST Approximation 1. Local Alignments Goal to find substrings of sequences S and T with maximum alignment score Motivation

  • 5 Pages notes02
    Notes02

    School: Washington

    Libby MacKinnon CSE 527 notes October 1, 2007 Intro to Molecular Biology Continued Proteins Proteins are chains of amino acids. They range in length from hundreds to tens of thousands of amino acids. There are 20 different kinds of standard amino ac

  • 3 Pages lec17
    Lec17

    School: Washington

    RNA Search and Motif Discovery Lectures 18-19 CSE 527 Autumn 2007 The Human Parts List, circa 2001 1 gagcccggcc 61 gggcgcagcg 121 gcggctcggc 181 tttagcgtcc 241 aaccagagcc 301 caatgtctgt 361 cggaaatcag 421 gccaaatatg 481 acaacactgc 541 ccagaaagga 601

  • 6 Pages lect09
    Lect09

    School: Washington

    Lecture 9 Gene Prediction, II - DRAFT Feb 3, 1998 Lecturer: Larry Ruzzo Notes: Sumeet Sobti 9.1. Introduction In this lecture, we continue with our discussion on techniques for gene finding. As seen in the last lecture, the goal is to be able to

  • 3 Pages lec15
    Lec15

    School: Washington

    CSE 527 Autumn 2007 Lectures 17-18 RNA Secondary Structure Prediction RNA Secondary Structure: RNA makes helices too Base pairs A U C G UC A A G C C G A G C U A C G A U G C AA AA C U Fastest Human Gene? Origin of Life? Life needs information carri

  • 3 Pages lec13
    Lec13

    School: Washington

    CSE 527 Computational Biology Lectures 13-14 Gene Prediction Some References (more on schedule page) An extensive online bib http:/www.nslij-genetics.org/gene/ A good intro survey JM Claverie (1997) "Computational methods for the identification of

  • 3 Pages lec11
    Lec11

    School: Washington

    CSE 527 Lectures 12-13 Markov Models and Hidden Markov Models DNA Methylation CpG - 2 adjacent nts, same strand (not CH3 Watson-Crick pair; p mnemonic for the phosphodiester bond of the DNA backbone) cytosine C of CpG is often (70-80%) methylated

  • 3 Pages lec05
    Lec05

    School: Washington

    Autumn 2007 Lectures 8-9 (& part of 10) Motifs: Representation & Discovery CSE 527 1 DNA Binding Proteins A variety of DNA binding proteins ("transcription factors"; a significant fraction, perhaps 5-10%, of all human proteins) modulate transcript

  • 2 Pages lec04-gmm-em
    Lec04-gmm-em

    School: Washington

    Gaussian Mixture Model via EM A 2component mixture with means mu1 & mu2, both having sigma=1, and mixing parameter tau=0.5. You can CHANGE the DATA, or the INITIAL MUs (yellow boxes), then watch how the E & Msteps update the expected zij's and mus,

  • 244 Pages ppt07
    Ppt07

    School: Washington

    %!PS-Adobe-3.0 %Title: Microsoft PowerPoint - lect07.ppt %Creator: Windows NT 4.0 %CreationDate: 11:19 1/28/1998 %Pages: (atend) %BoundingBox: 15 26 597 777 %LanguageLevel: 2 %DocumentNeededFonts: (atend) %DocumentSuppliedFonts: (atend) %EndComments

  • 1 Page lec04
    Lec04

    School: Washington

    Autumn 2007 Lectures 6-7 MLE, EM, Expression CSE 527 1 Outline MLE: Maximum Likelihood Estimators EM: the Expectation Maximization Algorithm Bio: Gene expression and regulation Next: Motif description & discovery 2 MLE Maximum Likelihood Esti

  • 3 Pages lec03
    Lec03

    School: Washington

    CSE 527 Computational Biology Autumn 2007 Lectures 4-5: BLAST Alignment score significance PCR and DNA sequencing 1 This Week's Plan BLAST Scoring Weekly Bio Interlude: PCR & Sequencing 2 A Protein Structure: (Dihydrofolate Reductase) 3 Seque

  • 3 Pages lec02
    Lec02

    School: Washington

    CSE 527 Computational Biology Autumn 2007 Lectures 2-3 Sequence Alignment; DNA Replication 1 This week Sequence alignment More sequence alignment Weekly "bio" interlude - DNA replication Sequence Alignment Part I Motivation, dynamic programming,

  • 3 Pages lec01
    Lec01

    School: Washington

    CSE527 Computational Biology http:/www.cs.washington.edu/527 Larry Ruzzo Autumn 2007 UW CSE Computational Biology Group He who asks is a fool for five minutes, but he who does not ask remains a fool forever. - Chinese Proverb Today Admin Why Comp B

  • 2 Pages hw3
    Hw3

    School: Washington

    CSE 527: Computational Biology Assignment #3 Turn this one in on paper; handwritten is fine, I don't recommend trying to typeset it. Extra credit is for extra practice and glory; it is not a big component of your grade. 1. Bayes Rule: In a certain

  • 2 Pages tutorial
    Tutorial

    School: Washington

    CSE 527: Computational Biology March 7, 2000 A review of A Tutorial Introduction to Computation for Biologists, and what else a struggling biologist might need to make sense of CSE 527 When attempting to assign cause to one's confusion, one is immedi

  • 1 Page syl
    Syl

    School: Washington

    CSE 527: Computational Biology, Autumn 2007 An introduction to the use of computational methods for the understanding of biological systems at the molecular level. Intended for graduate students in biological sciences interested in learning about alg

  • 17 Pages hw1
    Hw1

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: hw1.dvi %Pages: 3 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %EndComments %DVIPSWebPage: (www.radicaleye.com) %DVIPSCommandLine: /usr/local/bin/dvips -o www/lectures

  • 3 Pages hw1
    Hw1

    School: Washington

    suq ud j u p s i p u p q y w u ewex' eCigwu 'vp}wuvs'{vjsBggCr4vc'zxw u du p i s w u y w w q i yqj dq su y w wj w Cvp}g$'C'vvpX|vsg4Cx"xyv$'vCC|g' re{CxvhvlrqRxyv$q i yqj d dq umw y y dq w dw suju udw s wj p q y w Cv$C

  • 13 Pages lect18
    Lect18

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect18.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic %EndComments %DVIPSWebPage: (www.radicaleye.com)

  • 4 Pages lect18
    Lect18

    School: Washington

    Lecture 18 Speeding Up Internal Loop Computations March 7, 2000 Notes: Kellie Plow Recall from Section 17.4 that the running time for determining the internal loop free energy calculation is : each of the exterior pairs requires a search through the

  • 15 Pages lect17
    Lect17

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect17.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic %EndComments %DVIPSWebPage: (www.radicaleye.com)

  • 1 Page lect16
    Lect16

    School: Washington

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  • 4 Pages lect16
    Lect16

    School: Washington

    Lecture 16 RNA Secondary Structure Prediction February 29, 2000 Notes: Matthew Cary 16.1. RNA Secondary Structure Recall from Section 1.3 that RNA is usually single-stranded in its "normal" state, and this strand folds into a functional shape by fo

  • 4 Pages notes19
    Notes19

    School: Washington

    CSE 527 Notes: Lecture 19, December 1, 2004 Robyn Greaby Covariance Models Cont. Alignment Verterbi: - Inside Algorithm analogous to Forward algorithm for HMMs - Inside: find the most probable sequence of transitions and emissions to produce the seq

  • 12 Pages lect15
    Lect15

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect15.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic Courier %EndComments %DVIPSWebPage: (www.radicale

  • 4 Pages notes18
    Notes18

    School: Washington

    CSE527 Notes, Lecture 18 11/29/04 David W Richardson December 2, 2004 1 RNA Sequence Analysis Using Covariance Models This lecture looked at the paper by Sean Eddy and Richard Durbin entitled RNA Sequence Analysis Using Covariance Models. The paper

  • 4 Pages lect15
    Lect15

    School: Washington

    Lecture 15 Start Codon Prediction February 24, 2000 Notes: Mingzhou Song 15.1. Experimental Results of Glimmer The experimental results of Glimmer were presented by Delcher et al. [1]. They used the method described in Section 14.2 to predict genes

  • 3 Pages notes16
    Notes16

    School: Washington

    Sheila M. Reynolds Lecture 16, 11/22/04 CSE 527 Continuing discussion of "Pre-mRNA Secondary Structure Prediction Aids Splice Site Recognition" (a presentation to the Pacific Symposium on Biocomputing, Jan 2002, by D.J.Patterson, K.Yasuhara, and W.L.

  • 29 Pages lect14
    Lect14

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect14.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic %EndComments %DVIPSWebPage: (www.radicaleye.com)

  • 3 Pages notes15
    Notes15

    School: Washington

    Eithon Cadag Lecture 15, 11/17/04 CSE527 Notes on GenScan Generalized HMM - transition probabilities based on sequences, not single nucleotides P(sequence | model) Length distribution introns - geometric (self-loop) terminal exons - modeled empi

  • 4 Pages lect14
    Lect14

    School: Washington

    Lecture 14 Using Interpolated Context Models to Find Genes February 22, 2000 Notes: Gretta Bartels 14.1. Problems with Markov Chains for Finding Genes The Markov chain is an effective model for finding genes, as described in Section 13.3. However,

  • 2 Pages notes09
    Notes09

    School: Washington

    The Gibbs Sampler Lecture 9, October 27, 2004 Notes by Michele Banko Suppose we have some random variables x1 , x2 , . . . , xk over some pdf P (x1 , x2 , . . . , xk ), and a function f (x1 , x2 , . . . xk ) for which we wish to compute the expected

  • 1 Page lect13
    Lect13

    School: Washington

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  • 3 Pages notes07
    Notes07

    School: Washington

    Computational Biology CSE 527 Autumn 2004 Notes on Lecture 7, October 20th Mathias Ganter mganter@u.washington.edu 24th November 2004 Relative Entropy Relative entropy, also known as the Kullback-Leibler distance or K-L divergence, between two proba

  • 3 Pages lect13
    Lect13

    School: Washington

    Lecture 13 Markov Chains February 17, 2000 Notes: Jonathan Schaefer In Lecture 11 we discovered that correlations between sequence positions are significant, and should often be taken into account. In particular, in Section 11.4 we noted that codons

  • 3 Pages notes06
    Notes06

    School: Washington

    CSE 527 Notes, Oct. 18, 2004 1 CSE 527 Notes/Oct. 18 2004 Notes by Mukund Narasimhan Introduction Last lecture we began an examination of model based clustering. This lecture will be the technical background leading to the Expectation Maximization

  • 27 Pages lect12
    Lect12

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: root.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic Times-BoldItalic %EndComments %DVIPSWebPage: (www.r

  • 3 Pages notes05
    Notes05

    School: Washington

    CSE 527 Notes Lecture 5, 10/13/04 Model-Based Clustering Review of Partitional Clustering, K-Means: 1. Decide # of clusters, K 2. Assign initial estimates for the center of each of K clusters 3. Assign each point to its nearest center 4. Recalculate

  • 5 Pages notes04
    Notes04

    School: Washington

    CSE 527: Computational Biology Fall 2004, Professor Larry Ruzzo Notes by Angela Collie, 10/11 Lecture 4: Clustering Expression Data I. Overview: Clustering Expression Data Definition: Clustering is a data exploratory tool. It is an unsupervised appr

  • 4 Pages lect12
    Lect12

    School: Washington

    Lecture 12 Maximum Subsequence Problem February 15, 2000 Notes: Mathieu Blanchette 12.1. Scoring Regions of Sequences We have studied a variety of methods to score a DNA sequence so that regions of interest obtain a high score. For example, Section

  • 5 Pages notes03
    Notes03

    School: Washington

    October 6, 2004 - Lecture #3 Notes by Michael Panitz CSE 527 w/ Prof. Larry Ruzzo CSE527 emailing list: Several messages have been sent to the emailing list, so if you didn't get them, you're not subscribed. Sign up at (http:/www.cs.washington.edu/52

  • 2 Pages notes19
    Notes19

    School: Washington

    CSE 527 Lecture Notes Lecture 19, Dec. 3, 2003 Tin Louie tinlouie@u First topic: HMMs in action "Profile hidden Markov models can be used to do sensitive database searching using statistical descriptions of a sequence family's consensus" (quote fro

  • 15 Pages lect11
    Lect11

    School: Washington

    %!PS-Adobe-2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: lect11.dvi %Pages: 4 %PageOrder: Ascend %BoundingBox: 0 0 612 792 %DocumentFonts: Times-Bold Times-Roman Times-Italic %EndComments %DVIPSWebPage: (www.radicaleye.com)

  • 2 Pages notes01
    Notes01

    School: Washington

    CSE 527: Computational Molecular Biology September 29, 2004 Lecture 1 Lecturer: Larry Ruzzo Scribe: Mukund Narasimhan 1 Administratrivia Class Web Site : http:/www.cs.washington.edu/education/courses/527/04au. Class Syllabus : http:/www.cs.wash

  • 4 Pages lect11
    Lect11

    School: Washington

    Lecture 11 Correlation of Positions in Sequences February 10, 2000 Notes: Tammy Williams This lecture explores the validity of the assumption that the residues appearing at different positions in a sequence are independent. In previous lectures the

  • 5 Pages notes18
    Notes18

    School: Washington

    CSE 527 Lecture Notes Lecture 18, Nov. 26, 2003 Martha Mercaldi - mercaldi@cs Outline 1. Other analyses of HMMs 2. We've assumed we know the model, but how do you generate one? (a) Structure (b) Training (learn parameters given a structure) Hidden M

  • 1 Page notes21
    Notes21

    School: Washington

    Lecture 21, Phylogeny & RNA: Pfold (cont.) 12/6; No note taker today. In a nutshell, Pfold uses a stochastic context free grammar (SCFG) to model (pseudoknot-free) RNA 2ary structure. E.g. the rule F -> dFd | X generates a nested set of base pairs (t

  • 3 Pages ppt01
    Ppt01

    School: Washington

    CSE 590BI Computational Biology Winter 1998 Lecture 1 Biological Background (R. Karp) 1 2 3

  • 5 Pages lect18-cms-4up
    Lect18-cms-4up

    School: Washington

    Rfam Faster Genome Annotation of Non-coding RNAs Without Loss of Accuracy Zasha Weinberg & W.L. Ruzzo Input (hand-tuned): MSA SS_cons Score Thresh T Window Len W IRE (partial seed alignment): Hom.sap. Hom.sap. Hom.sap. Hom.sap. Hom.sap. Hom.sa

  • 270 Pages lect10
    Lect10

    School: Washington

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  • 4 Pages notes17
    Notes17

    School: Washington

    Markov and Hidden Markov Models Raphael Homann CSE 527 Lecture 17 Notes, 11/24/03 Reference: Richard Durbin et al. Biological sequence analysis: probabilistic models of proteins and nucleic acids, Cambridge University Press 1998 1 CpG islands CpG

  • 1 Page notes20
    Notes20

    School: Washington

    Lecture 20, Phylogeny & RNA: Pfold 12/4; D. Langworthy Modeling Sequence Evolution Make simplifying assumptions Most mutations are neutral even in protein coding regions Mutations are markov order 1 not true in general in plants some mutations hav

  • 3 Pages lect18-cms
    Lect18-cms

    School: Washington

    Faster Genome Annotation of Non-coding RNAs Without Loss of Accuracy Zasha Weinberg & W.L. Ruzzo Recomb `04 Rfam Input (hand-tuned): MSA SS_cons Score Thresh T Window Len W IRE (partial seed alignment): Hom.sap. Hom.sap. Hom.sap. Hom.sap. Hom.

  • 4 Pages lect10
    Lect10

    School: Washington

    Lecture 10 Finding Instances of Unknown Sites February 8, 2000 Notes: Dylan Chivian In order to find instances of unknown sites, we would like to be able to solve the relative entropy site selection problem (Section 9.3) exactly and efficiently. Unf

  • 3 Pages notes16
    Notes16

    School: Washington

    Phylogenies and Evolutionary Trees Lecture notes for CSE 527, 19 November 2003 Notes taken by Dhileep Sivam Complex question: Given data about a set of species, infer phylogeny. Simpler question: Given data and a tree, evaluate how well the data fit

  • 3 Pages notes19
    Notes19

    School: Washington

    CSE 527 11/29/06 Lecture 19 Larry Jean leijean@amath.washington.edu Paper: Distance vs Accuracy low distance low accuracy greater distance greater accuracy increase distance more decrease accuracy, levels out structure aligning adhoc, useful

  • 3 Pages rna-4up
    Rna-4up

    School: Washington

    Outline CSE 527 Lecture 17, 11/24/04 RNA Secondary Structure Prediction What is it How is it Represented Why is it important Examples Approaches RNA Structure Primary Structure: Sequence RNA Pairing Watson-Crick Pairing C - G ~ 3 kcal/mole

  • 11 Pages lect09
    Lect09

    School: Washington

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  • 3 Pages notes17
    Notes17

    School: Washington

    CSE 527, Notes for lecture 17, 11/22/06 Field Cady Task 2: Motif Description A Covariance Model (CM) accounts for folding, and sequence, rather than simply sequence as in a hidden Markov model. CM Viterbi alignment: Sij = best way to emit from i to

  • 2 Pages notes15
    Notes15

    School: Washington

    CSE 527 Notes 15: AlignACE and Gibbs with Gaps November 17, 2003 Transcribed by Michael F. Ringenburg 1 AlignACE The Gibbs sampling method we discussed last time (Lawrence et al.) was designed for protein motif finding. Roth et al. designed a syst

  • 3 Pages rna
    Rna

    School: Washington

    CSE 527 Lecture 17, 11/24/04 RNA Secondary Structure Prediction Outline What is it How is it Represented Why is it important Examples Approaches RNA Structure Primary Structure: Sequence Secondary Structure: Pairing Tertiary Structure: 3D

  • 3 Pages lect09
    Lect09

    School: Washington

    Lecture 9 Relative Entropy and Binding Energy February 1, 2000 Notes: Neil Spring Binding energy is a measure of the affinity between two molecules. Because it is an expression of free energy released rather than absorbed, a large negative number co

  • 2 Pages notes16
    Notes16

    School: Washington

    CSE 527, Lecture 16, 11/20/2006 Noah Benson RNA Pairing and Secondary Structure Prediction Can maximize number of base pairs or minimize energy Alternately, look at loops better for modeling Note: CG pairs have 3 H-bonds while AT pairs have 2,

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