| 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
09/29: ---- Organizational Meeting ----
10/06: Introduction to metagenomics -- Elizabeth, Ryan (Joe, Harlan)
- ML Sogin, HG Morrison, JA Huber, D Mark Welch, SM Huse, PR Neal, JM Arrieta, GJ Herndl, "Microbial diversity in the deep sea and the underexplored "rare biosphere"." Proc. Natl. Acad. Sci. U.S.A., 103, #32 (2006) 12115-20.
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
http://www.gs.washington.edu/news/combi.htm for abstract
10/20: Handling gaps in phylogeny inference and alignment -- James (Bill)
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.
11/03: Genome-wide, Systematic Study of Alternative Splicing Regulation in Human Tissues -- Dr. John Castle, MerckJohn 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
11/24: RNA-seq -- Xiaoyu, Adrienne (Larry)
and a subset (probably Wilhelm and Sultan) of the primary papers cited therein:
We will look at this mini-review:
- 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.
- 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.
- 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.
- 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.
12/01: Codon usage -- Punya, Aaron (Harlan)