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  CSE 590CSp '07:  Reading & Research in Comp. Bio.
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 Course Info    CSE 590C is a weekly seminar on Readings and Research in Computational Biology, open to all graduate students in computational, biological, and mathematical sciences.
When/Where:  Mondays, 3:30 - 4:50, EEB 026 (schematic)
Organizers:  Joe Felsenstein, Bill Noble, Larry Ruzzo, Martin Tompa
Credit: 1-3 Variable
Grading: Credit/No Credit. Talk to the organizers if you are unsure of our expectations.
 Email
cse590cb@cs.washington.edu Course-related announcements and discussions
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compbio-group@cs.washington.edu Discussions about computational biology
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 Schedule
 Date  Presenters/Participants Topic Details
03/26---- Organizational Meeting ----
04/02Aaron KlammerPeptide retention time prediction yields improved tandem mass spectrum identification for diverse chromatography conditions 
04/09Xiaoyu ChenPrediction of tissue-specific cis-regulatory modules using Bayesian networks and regression treesDetails
04/16Nguyen NguyenAlgorithms for combining rooted triplets into a galled phylogenetic networkDetails
04/23Zizhen YaoA computational pipeline for high throughput discovery of cis-regulatory noncoding RNA in prokaryotesDetails
04/30Karen Friery, ISBT1DBase: integration and presentation of complex data for type 1 diabetes research.Details
05/07Chul Joo Kang, GS Estimation of recombination rate heterogeneity using full likelihood method. Details
05/14 Special Event:
CMB Student Symposium
Join us all day in room CSE 691, especially for the keynote at 3:30:
Dr. Steve Henikoff, FHCRC, ``Epigenomic profiling to study histone dynamics''
 
05/21Harlan Robins, FHCRCA computational method to improve gene expressionDetails
05/28Holiday

 Papers, etc.

  Note on Electronic Access to Journals

Links to full papers below are often to journals that require a paid subscription. The UW Library is generally a paid subscriber, and you can freely access these articles if you do so from an on-campus computer. For off-campus access, follow the "[offcampus]" links below or look at the library "proxy server" instructions. You will be prompted for your UW net ID and password once per session.  


03/26: ---- Organizational Meeting ----

04/02: Peptide retention time prediction yields improved tandem mass spectrum identification for diverse chromatography conditions -- Aaron Klammer

04/09: Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees -- Xiaoyu Chen

  • M Blanchette, AR Bataille, X Chen, C Poitras, J Laganière, C Lefèbvre, G Deblois, V Giguère, V Ferretti, D Bergeron, B Coulombe, F Robert, "Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression." Genome Res., 16, #5 (2006) 656-68. [offcampus]

04/16: Algorithms for combining rooted triplets into a galled phylogenetic network -- Nguyen Nguyen

    Jesper Jansson, Nguyen Bao Nguyen, and Wing-Kin Sung, SIAM Journal on Computing, Volume 35 Issue 5, Pages 1098-1121 (2006).

    Abstract:   This paper considers the problem of determining whether a given set T of rooted triplets can be merged without conflicts into a galled phylogenetic network and, if so, constructing such a network. When the input T is dense, we solve the problem in O(|T|) time, which is optimal since the size of the input is Theta(|T|). In comparison, the previously fastest algorithm for this problem runs in O(|T|2) time. We also develop an optimal O(|T|)-time algorithm for enumerating all simple phylogenetic networks leaf-labeled by L that are consistent with T, where L is the set of leaf labels in T, which is used by our main algorithm. Next, we prove that the problem becomes NP-hard if extended to nondense inputs, even for the special case of simple phylogenetic networks. We also show that for every positive integer n, there exists some set T of rooted triplets on n leaves such that any galled network can be consistent with at most 0.4883 * |T| of the rooted triplets in T. On the other hand, we provide a polynomial-time approximation algorithm that always outputs a galled network consistent with at least a factor of 5/12 (> 0.41) of the rooted triplets in T.

04/23: A computational pipeline for high throughput discovery of cis-regulatory noncoding RNA in prokaryotes -- Zizhen Yao

    Abstract:   Noncoding RNAs (ncRNAs) are important functional RNAs that do not code for proteins. We present a highly efficient computational pipeline for discovering cis-regulatory ncRNA motifs de novo. The pipeline differs from previous methods in that it is structure-oriented, does not require a multiple sequence alignment as input, and is capable of detecting RNA motifs with low sequence conservation. We also integrate RNA motif prediction with RNA homolog search, which improves the quality of the RNA motifs significantly. Here we report the results of applying this pipeline to Firmicute bacteria. Our top ranking motifs include most known Firmicute Rfam families. Comparing our motif models with Rfam's hand-curated motif models, we achieve high accuracy in both membership prediction and base-pair-level secondary structure prediction (at least 75% average sensitivity and specificity on both tasks). Of the ncRNA candidates not in Rfam, we find compelling evidence that some of them are functional, and analyze several potential ribosomal protein leaders in depth.

    Joint work with Jeffrey Barrick, Zasha Weinberg, Shane Neph, Ronald Breaker, Martin Tompa and Walter L. Ruzzo

04/30: T1DBase: integration and presentation of complex data for type 1 diabetes research. -- Karen Friery, ISB

  • EM Hulbert, LJ Smink, EC Adlem, JE Allen, DB Burdick, OS Burren, CC Cavnor, GE Dolman, D Flamez, KF Friery, BC Healy, SA Killcoyne, B Kutlu, H Schuilenburg, NM Walker, J Mychaleckyj, DL Eizirik, LS Wicker, JA Todd, N Goodman, "T1DBase: integration and presentation of complex data for type 1 diabetes research." Nucleic Acids Res., 35, #Database issue (2007) D742-6. [offcampus]

    Abstract:   T1DBase (http://T1DBase.org) [Smink et al. (2005) Nucleic Acids Res., 33, D544-D549; Burren et al. (2004) Hum. Genomics, 1, 98-109] is a public website and database that supports the type 1 diabetes (T1D) research community. T1DBase provides a consolidated T1D-oriented view of the complex data world that now confronts medical researchers and enables scientists to navigate from information they know to information that is new to them. Overview pages for genes and markers summarize information for these elements. The Gene Dossier summarizes information for a list of genes. GBrowse [Stein et al. (2002) Genome Res., 10, 1599-1610] displays genes and other features in their genomic context, and Cytoscape [Shannon et al. (2003) Genome Res., 13, 2498-2504] shows genes in the context of interacting proteins and genes. The Beta Cell Gene Atlas shows gene expression in beta cells, islets, and related cell types and lines, and the Tissue Expression Viewer shows expression across other tissues. The Microarray Viewer shows expression from more than 20 array experiments. The Beta Cell Gene Expression Bank contains manually curated gene and pathway annotations for genes expressed in beta cells. T1DMart is a query tool for markers and genotypes. PosterPages are 'home pages' about specific topics or datasets. The key challenge, now and in the future, is to provide powerful informatics capabilities to T1D scientists in a form they can use to enhance their research.

05/07:  Estimation of recombination rate heterogeneity using full likelihood method. -- Chul Joo Kang, GS

    Abstract:   Current studies showed that recombination rate on human genome is not equally distributed. Some regions (recombination hotspot) have much higher recombination rate compare to rest of genome. There are some methods to estimate the variations in recombination rate using approximation of likelihood. I want to talk about method using the full likelihood approaches to estimate these variations of recombination rate.

Background readings:

05/14:  Join us all day in room CSE 691, especially for the keynote at 3:30:
Dr. Steve Henikoff, FHCRC, ``Epigenomic profiling to study histone dynamics''
-- Special Event:
CMB Student Symposium

05/21: A computational method to improve gene expression -- Harlan Robins, FHCRC
Background reading:

  • R Schneider, M Campbell, G Nasioulas, BK Felber, GN Pavlakis, "Inactivation of the human immunodeficiency virus type 1 inhibitory elements allows Rev-independent expression of Gag and Gag/protease and particle formation." J. Virol., 71, #7 (1997) 4892-903. [offcampus]

 Other  Seminars Past quarters of CSE 590C
COMBI & Genome Sciences Seminars
Applied Math Department Mathematical Biology Journal Club
Biostatistics Seminars
Microbiology Department Seminars
Zoology 525, Mathematical Biology Seminar Series

 Resources Molecular Biology for Computer Scientists, a primer by Lawrence Hunter (46 pages)
A Quick Introduction to Elements of Biology, a primer by Alvis Brazma et al.
S-Star Bioinformatics Online Course Schedule, a collection of video primers
A very comprehensive FAQ at bioinformatics.org, including annotated references to online tutorials and lectures.
CSE 527: Computational Biology
CSE 590TV: Computational Biology (Professional Masters Program)
Genome 540/541: Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis

CSE's Computational Molecular Biology research group
Interdisciplinary Ph.D. program in Computational Molecular Biology


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