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  CSE 590CAu '08:  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 025 (schematic)
Organizers:  Joe Felsenstein, Bill Noble, Harlan Robins, Larry Ruzzo, Martin Tompa
Credit: 1-3 Variable
Grading: Credit/No Credit. Talk to the organizers if you are unsure of our expectations.
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 Date  Presenters/Participants Topic Details
09/29---- Organizational Meeting ----
10/06Elizabeth, Ryan (Joe, Harlan)Introduction to metagenomicsDetails
10/13Dr. Paul Horton, AIST, TokyoMitochondrial β-Signal; The End of the Story?NOTE SPECIAL LOCATION
10/20James (Bill)Handling gaps in phylogeny inference and alignmentDetails
10/27Benjamin, Cindy (Martin)Binning metagenomic sequencesDetails
11/03Dr. John Castle, MerckGenome-wide, Systematic Study of Alternative Splicing Regulation in Human TissuesDetails
11/10Dr. Manikandan Narayanan, MerckSimultaneous Clustering of Physical Interaction and Multiple Gene Expression DatasetsDetails
11/17Dr. Eric Schadt, MerckElucidating the Circuits of Metabolic DiseasesDetails
11/24Xiaoyu, Adrienne (Larry)RNA-seqDetails
12/01Punya, Aaron (Harlan)Codon usageDetails

 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.  

09/29: ---- Organizational Meeting ----

10/06: Introduction to metagenomics -- Elizabeth, Ryan (Joe, Harlan)

10/13: Mitochondrial β-Signal; The End of the Story? -- Dr. Paul Horton, AIST, Tokyo
This talk is joint with the Genome Sciences Combi Seminar series, and will be held in the Foege Auditorium (Foege S-060). See for abstract and references.

10/20: Handling gaps in phylogeny inference and alignment -- James (Bill)
One of:

10/27: Binning metagenomic sequences -- Benjamin, Cindy (Martin)

Background papers we will not cover:
  • K Mavromatis, N Ivanova, K Barry, H Shapiro, E Goltsman, AC McHardy, I Rigoutsos, A Salamov, F Korzeniewski, M Land, A Lapidus, I Grigoriev, P Richardson, P Hugenholtz, NC Kyrpides, "Use of simulated data sets to evaluate the fidelity of metagenomic processing methods." Nat. Methods, 4, #6 (2007) 495-500. [offcampus]

11/03: Genome-wide, Systematic Study of Alternative Splicing Regulation in Human Tissues -- Dr. John Castle, Merck

  • John C Castle, Chaolin Zhang, Jyoti K Shah, Amit V Kulkarni, Auinash Kalsotra, Thomas A Cooper, Jason M Johnson, "Expression of 24,426 human alternative splicing events and predicted cis regulation in 48 tissues and cell lines." Nature Genetics, 2 November 2008.

    11/10: Simultaneous Clustering of Physical Interaction and Multiple Gene Expression Datasets -- Dr. Manikandan Narayanan, Merck

       Abstract:   A network of physical interactions such as protein-protein and protein-DNA interactions could provide a common backbone to interpret gene expression data profiled under multiple conditions. Among the various integrative approaches that exploit this concept, one class of methods identify functional modules of genes participating in specific cellular processes by hypothesizing a functional module as a set of coexpressed genes that are also connected in the underlying network. We briefly review such methods, and present our approach to detecting functional modules from diverse biological networks. Our approach is based on a generic algorithm that derives a single common clustering of multiple networks by integrating the information in all networks at each step of the clustering process. The algorithm yields clusters of genes that are well-connected in each of the input network, and permits certain theoretical guarantees on the quality of these clusters. We apply the method to explore how a physical interaction network can help explain gene coexpression relations that are preserved and changed between two physiological conditions of yeast.

    11/17: Elucidating the Circuits of Metabolic Diseases -- Dr. Eric Schadt, Merck

       Abstract:   We have previously detailed an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, gene networks that are perturbed by susceptibility loci and that in turn lead to disease are identified. This approach has been applied to individual tissues in human and mouse populations, leading to the identification of highly interconnected subnetworks predicted and experimentally validated as causal for disease. However, this and other network approaches to understanding complex system behaviors have largely ignored interactions among subnetworks both within and between tissues, where such interactions are critical to living systems manifesting complex behaviors. For example, the central nervous system (CNS) receives information regarding the status of peripheral metabolic processes via hormonal signaling, direct macromolecular sensing, and through a complex neuronal network that connects the CNS with the periphery. At the center of these CNS networks is the hypothalamus, which serves as the target for a plethora of signals such as insulin, leptin, and a diverse set of macromolecules including glucose and long chain fatty acids. These signals in turn serve to modulate the hypothalamic response through the autonomic neuronal pathways, where disruption of these pathways connecting the periphery and hypothalamus partially explains obesity. Beyond these known interactions between tissues are a number of unknown interactions that have the potential to define much of the complex behavior that emerges from living systems.

    To decipher the communication within and between tissues at the molecular level, we examine interactions among gene expression traits in blood, adipose, muscle, pancreas, liver, and brain tissues from human and experimental mouse cross populations using integrative genomics approaches previously applied to single tissues. Gene-gene relationships specific to interactions between two given tissues are observed to give rise to coherent subnetworks involved in important functions like circadian rhythm and energy balance that are independent of subnetworks detected from single tissue analyses, highlighting novel networks associated with disease that have previously escaped notice. Further, not only do the tissue-tissue networks highlight genes in one tissue that respond to changes in genes in a second tissue, but they elucidate entire subnetworks in one tissue that influence subnetworks in a second tissue. Our modeling approach provides direct support for cross-tissue processes influencing a diversity of disease traits related to obesity, diabetes, atheroscelrosis, and Alzheimerbs, and suggests hypotheses on how biological processes observed in one tissue as driving a given disease (e.g., obesity) may influence processes observed in a different tissue as driving a related disease (e.g., diabetes). Many of the specific causal relationships we detect via the tissue-tissue networks would be difficult or impossible to detect via single-tissue analyses. Our analyses provide further support that complex traits like obesity, diabetes, and atheroscelrosis are emergent properties of complex interactions among molecular networks in different tissues that are modulated by complex genetic loci and environmental factors.

    11/24: RNA-seq -- Xiaoyu, Adrienne (Larry)
    We will look at this mini-review:

    and a subset (probably Wilhelm and Sultan) of the primary papers cited therein:
    • BT Wilhelm, S Marguerat, S Watt, F Schubert, V Wood, I Goodhead, CJ Penkett, J Rogers, J Bähler, "Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution." Nature, 453, #7199 (2008) 1239-43. [offcampus]
    • M Sultan, MH Schulz, H Richard, A Magen, A Klingenhoff, M Scherf, M Seifert, T Borodina, A Soldatov, D Parkhomchuk, D Schmidt, S O'Keeffe, S Haas, M Vingron, H Lehrach, ML Yaspo, "A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome." Science, 321, #5891 (2008) 956-60. [offcampus]
    • N Cloonan, AR Forrest, G Kolle, BB Gardiner, GJ Faulkner, MK Brown, DF Taylor, AL Steptoe, S Wani, G Bethel, AJ Robertson, AC Perkins, SJ Bruce, CC Lee, SS Ranade, HE Peckham, JM Manning, KJ McKernan, SM Grimmond, "Stem cell transcriptome profiling via massive-scale mRNA sequencing." Nat. Methods, 5, #7 (2008) 613-9. [offcampus]
    • R Lister, RC O'Malley, J Tonti-Filippini, BD Gregory, CC Berry, AH Millar, JR Ecker, "Highly integrated single-base resolution maps of the epigenome in Arabidopsis." Cell, 133, #3 (2008) 523-36. [offcampus]

    12/01: Codon usage -- Punya, Aaron (Harlan)

  •  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, 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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