ISMB 2008 ISCB






















Proceedings Track Presentation Schedule

Proceedings Track: PT01
PICKY: A Novel SVD-Based NMR Spectra Peak Picking Method
Monday, June 29 - 10:45 a.m. - 11:10 a.m.
Room: Victoria Hall
Presenting author: Babak Alipanahi Ramandi, University of Waterloo

ISMB/ECCB 2009 Blog
PT01: Babak Alipanahi Ramandi - PICKY: A Novel SVD-Based NMR Spectra Peak Picking Method
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Proceedings Track: PT02
From Disease Ontology (DO) to Disease-Ontology Lite (DOLite): Statistical Methods to Adapt a General-Purpose Ontology for the Test of Gene-Disease Associations
Monday, June 29 - 10:45 a.m. - 11:10 a.m.
Room: K2
Presenting author: Warren A. Kibbe, Northwestern University

ISMB/ECCB 2009 Blog
PT02: Warren Kibbe - From Disease Ontology (DO) to Disease-Ontology Lite (DOLite): Statistical Methods to Adapt a General-Purpose Ontology for the Test of Gene-Disease Associations
want to simplify the DO to make results more easy to interprete - Michael Kuhn
DOLite is a CV whereas the DO is an ontology. - Allyson Lister
using two parallel clustering steps and expert curation - Michael Kuhn
DO: ~12,000 terms, DOlite: ~600 terms - Michael Kuhn
"FunDO takes a list of genes and finds relevant diseases based on statistical analysis of the Disease Ontology annotation database. " - Michael Kuhn
The same query using a gene list with DO and DOLite gets better clustering with DOLite (where better == more distinct clusters, greater number of clusters in the example we were shown - 2 clusters rather than 1). - Allyson Lister
future plan: reduce GO (Gene Ontology) in a similar way - Michael Kuhn
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Proceedings Track: PT03
Prediction of Sub-Cavity Binding Preferences Using an Adaptive Physicochemical Structure Representation
Monday, June 29 - 11:15 a.m. - 11:40 a.m.
Room: Victoria Hall
Presenting author: Izhar Wallach, University of Toronto

ISMB/ECCB 2009 Blog
PT03: Izhar Wallach - Prediction of Sub-Cavity Binding Preferences Using an Adaptive Physicochemical Structure Representation
Seemingly unrelated proteins may share similar subcavities with similar chemical features - Anne Tuukkanen
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Proceedings Track: PT04
Alignment of the UMLS Semantic Network with BioTop Methodology and Assessment
Monday, June 29 - 11:15 a.m. - 11:40 a.m.
Room: K2
Presenting author: Stefan Schulz, Freiburg University Hospital

ISMB/ECCB 2009 Blog
PT04: Stefan Schulz - Alignment of the UMLS Semantic Network with BioTop Methodology and Assessment
ontology alignment - link two ontologies together semantically - Allyson Lister
Why decide to create BioTop and not use BFO or DOLCE lite directly, I wonder? It's not that I would necessarily suggest that these be used, I am just curious... - Allyson Lister
The methodology is first to provide DL semantics to the UMLS SN, and second build the bridge between BioTop and UML SN. - Allyson Lister
Interestingly, semantic relations were reified as classes, NOT represented as OWL object properties. - Allyson Lister
subsumption hierarchies are assumed to be is_a hierarchies, but is that a safe assumption in UMLS SN? I don't know, as I am not familiar with UMLS SN. For instance, in older versions of GO this would have been a problem (some things marked as subsumption were not in fact is_a, though I am pretty sure GO has fixed all of this now). - Allyson Lister
There were inconsistent categorizations of UMLS SN objects which exposed hidden ambiguities (e.g. that Hospital was both a building and an organisation). - Allyson Lister
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Proceedings Track: PT05
Pokefind: A Novel Topological Filter for use With Protein Structure Prediction
Monday, June 29 - 11:45 a.m. - 12:10 p.m.
Room: Victoria Hall
Presenting author: Firas Khatib, University of Washington

ISMB/ECCB 2009 Blog
PT05: Firas Khatib - Pokefind: A Novel Topological Filter for use With Protein Structure Prediction
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Proceedings Track: PT06
Ontology Quality Assurance Through Analysis of Term Transformations
Monday, June 29 - 11:45 a.m. - 12:10 p.m.
Room: K2
Presenting author: Karin Verspoor, University of Colorado Denver

ISMB/ECCB 2009 Blog
PT06: Karin Verspoor - Ontology Quality Assurance Through Analysis of Term Transformations
univocality: consistency of expression of concepts within an ontology. This facilitates human usability and computational tools can utilize this regularity. - Allyson Lister
Try to identify cases where there were violations of univocality: two semantically similar terms with different structure in their term labels. - Allyson Lister
Steps: abstraction, stopword removal and alphabetic reordering - Allyson Lister
In the end, found 237 clusters that may contain a univocality violation. - Allyson Lister
Discovered 67 true positive violations (35% ) of univocality. - Allyson Lister
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Proceedings Track: PT07
REPETITA: Detection and Discrimination of the Periodicity of Protein Solenoid Repeats by Discrete Fourier Transform
Monday, June 29 - 12:15 p.m. - 12:40 p.m.
Room: Victoria Hall
Presenting author: Silvio Tosatto, University of Padova

ISMB/ECCB 2009 Blog
PT07: Silvio Tosatto - REPETITA: Detection and Discrimination of the Periodicity of Protein Solenoid Repeats by Discrete Fourier Transform
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Proceedings Track: PT08
Modeling Stochasticity and Robustness in Gene Regulatory Networks
Monday, June 29 - 12:15 p.m. - 12:40 p.m.
Room: K2
Presenting author: Abhishek Garg, Ecole Polytechnique Federale de Lausanne

ISMB/ECCB 2009 Blog
PT08: Abhishek Garg - Modeling Stochasticity and Robustness in Gene Regulatory Networks
Looking at stochasticity in nodes and in functions within gene regulartory (GR) networks. - Allyson Lister
How does the system behave when it is subjected to stochastic behaviour? - Allyson Lister
uses boolean modeling for this work - Allyson Lister
Boolean model: each protein can either be present or absent. The interaction network can be encoded with a set of boolean functions exhibiting steady states. - Gabriele Sales
Previous work: some nodes behave stochastically. - Gabriele Sales
With SIN (stochasticity in nodes), they had an example of stochastic cellular differentiation. You understand what the probabilities are in SIN by looking at a population of cells. - Allyson Lister
Example: stochasticity introduces cellular differentiation into a popolation. - Gabriele Sales
The SIN method overrepresents noise in the system. - Allyson Lister
This work employs a different approach: stochasticity in functions. The boolean function itself becomes noisy. - Gabriele Sales
Advantage of this approach: the noise can be selected depending on the biological function it applies to. - Gabriele Sales
...more...
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Proceedings Track: PT09
A Framework to Refine Particle Clusters Produced by EMAN
Monday, June 29 - 2:15 p.m. - 2:40 p.m.
Room: Victoria Hall
Presenting author: Liya Fan, Institute of Computing Technology

ISMB/ECCB 2009 Blog
PT09: Liya Fan - A Framework to Refine Particle Clusters Produced by EMAN
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Proceedings Track: PT10
Clustered Alignments of Gene-Expression Time Series Data
Monday, June 29 - 2:15 p.m. - 2:40 p.m.
Room: K2
Presenting author: Adam Smith, University of Wisconsin, Madison

ISMB/ECCB 2009 Blog
PT10: Adam Smith - Clustered Alignments of Gene-Expression Time Series Data
Usually restricted to discrete measurements of a continues attribute, requires reconstruction or interpolation. - Oliver Hofmann
Time warping/alignment to maximize the similarity of the compared point helps. Warpspace diagrams are an alternative way of showing alignment between two time series (think two sequence alignment matrices) - Oliver Hofmann
SCOW: efficient method for sparse time series - Oliver Hofmann
Known algorithm (1978) for dynamic time warping (DTW), minimize sum of euclidean distances. Parametric time warping (2005, Eilers, PTW) fits alignment function from given family. More limited in expressiveness than DTW - Oliver Hofmann
Segment-based warping splits warps into individually scored segments, sits in a happy intermediate between DTW, PTW, but very slow (n^5 complexity). Correlation-optimized warping (COW, Nielsen 1998) looks for good 'knots' (one dimensional). SCOW searches in each dimension independently until convergence. - Oliver Hofmann
Evaluation with EDGE toxicology database, SCOW does better than other algorithms (little less so for distorted test data) in identifying the correct matching time series. - Oliver Hofmann
Cluster gene time series to get a regularization effect; algorithm based on k-means. Each cluster defined by an alignment, initial clusters calculates by a greedy algorithm, assign genes to cluster, re-calculate alignment for each cluster, iterate until convergence. - Oliver Hofmann
Does clustering help? Test with Mop3 knockout (circadian cycle gene); five clusters with exemplar genes identifies genes with a strong phase shift. - Oliver Hofmann
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Proceedings Track: PT11
Domain-oriented Edge-based Alignment of Protein Interaction Networks
Monday, June 29 - 2:45 p.m. - 3:10 p.m.
Room: Victoria Hall
Presenting author: Xin Guo, Duke University

ISMB/ECCB 2009 Blog
PT11: Xin Guo - Domain-oriented Edge-based Alignment of Protein Interaction Networks
Introducing... the DOMain-oriented Alignment of Interaction Networks (DOMAIN). - Allyson Lister
DOMAIN is the first algorithm to introduce domains to PPI network alignment problem, and the first attempt to align PPIs directly. (They think) - Allyson Lister
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Proceedings Track: PT12
KELLER: Estimating Time-Evolving Interactions Between Genes
Monday, June 29 - 2:45 p.m. - 3:10 p.m.
Room: K2
Presenting author: Le Song, Carnegie Mellon University

ISMB/ECCB 2009 Blog
PT12: Le Song - KELLER: Estimating Time-Evolving Interactions Between Genes
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Proceedings Track: PT13
Network-based Prediction of Metabolic Enzymes Subcellular Localization
Monday, June 29 - 3:15 p.m.- 3:40 p.m.
Room: Victoria Hall
Presenting author: Shira Mintz-Oron, Weizmann Institute of Science

ISMB/ECCB 2009 Blog
PT13: Shira Mintz-Oron - Network-based Prediction of Metabolic Enzymes Subcellular Localization
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Proceedings Track: PT14
Predicting Functionality of Protein-DNA Interactions by Integrating Diverse Evidence
Monday, June 29 - 3:15 p.m.- 3:40 p.m.
Room: K2
Presenting author: Duygu Ucar, Ohio State University

ISMB/ECCB 2009 Blog
PT14: Duygu Ucar - Predicting Functionality of Protein-DNA Interactions by Integrating Diverse Evidence
background about TFs. ChIPchip, ChIPseq can define binding (but noisy data). Even when we know binding occurs, knowing when binding is functional is less well characterized. - Cass Johnston
Detecting interaction events between TFs and targets is important, can be observed via chip-chip/seq - Oliver Hofmann
Semantics of interactions is important, but difficult to characterize (context dependency of TF binding event) - Oliver Hofmann
Binding and gene regulation are context dependent - Cass Johnston
Estimating TF binding and gene expression response based on three data sets (chip-chip, PSSM and nucleosome occupancy). Binding considered functional if it changes gene expression (determined from microarray data) - Oliver Hofmann
combined into a probabilistic bayesian model - Oliver Hofmann
Integrated model outperforms individual three data sets in a five-fold cross validation - Oliver Hofmann
Used two expression data sets to distinguish putatively functional and non-functional binding events - Oliver Hofmann
Data is from yeast. Many TFs. Conditions are Normal Growth and various Stress. - Cass Johnston
Functional binding rate for a TF was context dependent. They could rank the TFs in order of their impact in different conditions - Cass Johnston
...more...
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Proceedings Track: PT15
IsoRankN: Spectral Methods For Global Alignment of Multiple Protein Networks
Monday, June 29 - 3:45 p.m. - 4:10 p.m.
Room: Victoria Hall
Presenting author: Michael Baym, Massachusetts Institute of Technology

ISMB/ECCB 2009 Blog
PT15: Michael Baym - IsoRankN: Spectral Methods For Global Alignment of Multiple Protein Networks
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Proceedings Track: PT16
Grouped Graphical Granger Modeling for Gene Expression Regulatory Networks Discovery
Monday, June 29 - 3:45 p.m. - 4:10 p.m.
Room: K2
Presenting author: Saharon Rosset, Tel Aviv University

ISMB/ECCB 2009 Blog
PT16: Saharon Rosset - Grouped Graphical Granger Modeling for Gene Expression Regulatory Networks Discovery
Friendfeed blocked access temporarily. Notes at http://www.fiamh.info/articles/16/ismbeccb-2009-day-1 - Oliver Hofmann
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Proceedings Track: PT17
A Unified Statistical Model to Support Local Sequence Order Independent Similarity Searching for Ligand Binding Sites and its Application to Genome-Based Drug Discovery
Tuesday, June 30 - 10:45 a.m. - 11:10 a.m.
Room: Victoria Hall
Presenting author: Lei Xie, University of California, San Diego

ISMB/ECCB 2009 Blog
PT17: Lei Xie - A Unified Statistical Model to Support Local Sequence Order Independent Similarity Searching for Ligand Binding Sites and its Application to Genome-Based Drug Discovery
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Proceedings Track: PT18
Multi-Locus Match Probability in a Finite Population: A Fundamental Difference Between the Moran and Wright-Fisher Models
Tuesday, June 30 - 10:45 a.m. - 11:10 a.m.
Room: K2
Presenting author: Anand Bhaskar, University of California, Berkeley

ISMB/ECCB 2009 Blog
PT18: Anand Bhaskar - Multi-Locus Match Probability in a Finite Population: A Fundamental Difference Between the Moran and Wright-Fisher Models
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Proceedings Track: PT19
Global Alignment of Protein-Protein Interaction Networks by Graph Matching Methods
Tuesday, June 30 - 11:15 a.m. - 11:40 a.m.
Room: Victoria Hall
Presenting author: Mikhail Zaslavskiy, Mines ParisTech

ISMB/ECCB 2009 Blog
PT19: Mikhail Zaslavskiy - Global Alignment of Protein-Protein Interaction Networks by Graph Matching Methods
Motivation: identification of protein functional orthologs - Oliver Hofmann
Standard approach like reciprocal best BLAST hits have problems when several top hits have similar scores -- which pair to chose? Additional information helps to resolve ambiguity. - Oliver Hofmann
PPI networks (the usual hairball) can be used to resolve this. If ortholog assignments conserve PPI interactions are ranked higher - Oliver Hofmann
Maximize number of conserved interaction and the sum of BLAST pair scores - Oliver Hofmann
Used message passing clustering approach for the InParanoid/protein cluster approach; each node is a protein cluster - Oliver Hofmann
Balanced alignment uses graph matching approximation (gradient ascent and path algorithm) - Oliver Hofmann
PPI networks and InParanoid clusters from the Ideker paper, 2244 clusters (1552 with only 2 proteins / orthologs, 692 ambiguous clusters that need to be resolved). No cycles in this graph means constrained alignment approach can be used, results in 238 conserved interactions - Oliver Hofmann
Constrained algorithm with message passing is an exact solution, graph matching algorithms deliver good performance for balanced alignment problems - Oliver Hofmann
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Proceedings Track: PT20
Speeding Up HMM Algorithms for Genetic Linkage Analysis via Chain Reductions of the State Space
Tuesday, June 30 - 11:15 a.m. - 11:40 a.m.
Room: K2
Presenting author: Ydo Wexler, Microsoft Research

ISMB/ECCB 2009 Blog
PT20: Ydo Wexler - Speeding Up HMM Algorithms for Genetic Linkage Analysis via Chain Reductions of the State Space
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Proceedings Track: PT21
Probabilistic retrieval and Visualization of Biologically Relevant Microarray Experiments
Tuesday, June 30 - 11:45 a.m. - 12:10 p.m.
Room: Victoria Hall
Presenting author: JosŽ Caldas, Helsinki University of Technology

ISMB/ECCB 2009 Blog
PT21: Jose Caldas - Probabilistic retrieval and Visualization of Biologically Relevant Microarray Experiments
Trying to find a method to relate results in large array databases based on expression information rather than annotation. - Oliver Hofmann
Standard approaches like spearman correlation coefficients, but it would be interested to use sets of experiments as a query rather than a single array. - Oliver Hofmann
Query with a binary phenotype comparison and try to get back other, similar comparisons. Requires encoding of the phenotype comparison such as a vector of t-tests (0/1 vector for differentially expressed genes) - Oliver Hofmann
Using differential GSEA (shorter vector, more robust) - Oliver Hofmann
GSEA is gene set enrichment analysis and compares gene sets with -1,0,+1 vectors - sebi
Uses standard GSEA, number of genes in leading edge as vector value, ignoring the directionality - Oliver Hofmann
Latent Dirichlet Allocation for the retrieval algorithm - Oliver Hofmann
Latent Drichilet Allocation (LDA) from text analysis works intuitively: uses documents to build up topics -- in bag-of-words text data. - sebi
Bag-of-words from gene sets, represented as combinations of sets, relevant distance measures can be applied - sebi
750+ binary phenotype comparisons from 288 experiments, focus on 105 comparisons for this analysis. Gene sets assigned to topics are coherent across a wide range of biological processes - Oliver Hofmann
...more...
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Proceedings Track: PT22
A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network
Tuesday, June 30 - 11:45 a.m. - 12:10 p.m.
Room: K2
Presenting author: Seyoung Kim, Carnegie Mellon University

ISMB/ECCB 2009 Blog
PT22: Seyoung Kim - A Multivariate Regression Approach to Association Analysis of Quantitative Trait Network
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Proceedings Track: PT23
Proteome Coverage Prediction with Infinite Markov Models
Tuesday, June 30 - 12:15 p.m. - 12:40 p.m.
Room: Victoria Hall
Presenting author: Manfred Claassen, ETH ZŸrich

ISMB/ECCB 2009 Blog
PT23: Manfred Claassen - Proteome Coverage Prediction with Infinite Markov Models
Predticiton of proteome coverage in shotgun proteomics for maximum coverage, to find a stop criterion, and to direct study design - sebi
estimating the limit of theoretical coverage first requires a quantification of the distribution of observable peptides. The computer science analogy used is the chinese restaurant process - Jim Procter
Chinese Restaurant Processes, i.e. the rich get richer, "dish" distribution: many proteins are observed over and over again - sebi
customers map to spectra, dishes map to peptides - Jim Procter
a hierarchy of chinese restaurants (== dirichlet processes) ensuring support for likely observed peptides in the root distribution of observable peptides - Jim Procter
adapting the infinite markov model to the case where identifications are false positives (account for target decoy strategy based FPR estimation) - Jim Procter
(... simply add an extra dimension that samples over the identification database - ie model the FPR random chance distribution) - Jim Procter
maximal coverage!=saturation coverage - more experiments means the quality of discoveries are worse, ie the less certain you can be about your real matches - Jim Procter
Answers that the proteome coverage prediction can provide: How many peptides can be identified? More experiments deteriorate quality of discoveries: quality constraint restricts maximum coverage - sebi
I think this is really important - but I am not expert enough to understand how this diriSim model can be applied to the 'limits' of proteomics studies, where one is looking for very specific types of proteins (e.g. PTM characterisation, etc). - Jim Procter
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Proceedings Track: PT24
Inference of Locus-Specific Ancestry in Closely Related Populations
Tuesday, June 30 - 12:15 p.m. - 12:40 p.m.
Room: K2
Presenting author: Sriram Sankararaman, University of California, Berkeley

ISMB/ECCB 2009 Blog
PT24: Sriram Sankararaman - Inference of Locus-Specific Ancestry in Closely Related Populations
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Proceedings Track: PT25
Fewer permutations, More Accurate P-values
Tuesday, June 30 - 2:15 p.m. - 2:40 p.m.
Room: Victoria Hall
Presenting author: Theo Knijnenburg, Institute for Systems Biology

ISMB/ECCB 2009 Blog
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Proceedings Track: PT26
A Geometric Approach for Classification and Comparison of Structural Variants
Tuesday, June 30 - 2:15 p.m. - 2:40 p.m.
Room: K2
Presenting author: Suzanne Sindi, Brown University

ISMB/ECCB 2009 Blog
PT26: Suzanne Sindi - A Geometric Approach for Classification and Comparison of Structural Variants
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Proceedings Track: PT27
A General Computational Method for Robustness Analysis with Applications to Synthetic Gene Networks
Tuesday, June 30 - 2:45 p.m. - 3:10 p.m.
Room: Victoria Hall
Presenting author: AurŽlien Rizk, INRIA Paris-Rocquencourt

ISMB/ECCB 2009 Blog
PT27: Aurelien Rizk - A General Computational Method for Robustness Analysis with Applications to Synthetic Gene Networks
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Proceedings Track: PT28
Joint Estimation of Gene Conversion Rates and Mean Conversion Tract Lengths from Population SNP Data
Tuesday, June 30 - 2:45 p.m. - 3:10 p.m.
Room: K2
Presenting author: Junming Yin, University of California, Berkeley

ISMB/ECCB 2009 Blog
PT28: Junming Yin - Joint Estimation of Gene Conversion Rates and Mean Conversion Tract Lengths from Population SNP Data
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Proceedings Track: PT29
E-zyme: Predicting Potential EC Numbers from the Chemical Transformation Pattern of Substrate-product Pairs
Tuesday, June 30 - 3:15 p.m. - 3:40 p.m.
Room: Victoria Hall
Presenting author: Yoshihiro Yamanishi, Ecole des Mines de Paris

ISMB/ECCB 2009 Blog
PT29: Yoshihiro Yamanishi - E-zyme: Predicting Potential EC Numbers from the Chemical Transformation Pattern of Substrate-product Pairs
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Proceedings Track: PT30
Computing Galled Networks from Real Data
Tuesday, June 30 - 3:15 p.m. - 3:40 p.m.
Room: K2
Presenting author: Daniel Huson, University of Tuebingen

ISMB/ECCB 2009 Blog
PT30: Daniel Huson - Computing Galled Networks from Real Data
Two different types, networks that provide an explicit picture with hybridization etc, or an abstract network. - Roland Krause
An early approach using a mathematical generalization were split networks, which provide an abstraction but no explanation. - Roland Krause
See dendroscope.org - Roland Krause
Go to an explicit networks where you have hybridization and speciation events, e.g. in plant evolution, horizontal gene transfer in bacteria. Both appear similar in this framework. - Roland Krause
When modelling reticulate evolution a node can have a variable input from different parents, e.g. 50/50 for plants, 95/5 for horizontal gene transfer settings. - Roland Krause
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Proceedings Track: PT32
Genotype-Phenotype Associations: Substitution Models to Detect Evolutionary Associations Between Phenotypic Variables and Genotypic Evolutionary Rate
Tuesday, June 30 - 3:45 p.m. - 4:10 p.m.
Room: K2
Presenting author: Timothy OÕConnor, University of Cambridge

ISMB/ECCB 2009 Blog
PT32: Timothy O'Connor - Genotype-Phenotype Associations: Substitution Models to Detect Evolutionary Associations Between Phenotypic Variables and Genotypic Evolutionary Rate
Can we detect a change of rate in evolution. Model is sperm competition in apes. - Roland Krause
Phylogeny is important to look at the independent signal, also use the past nodes for inference. - Roland Krause
Problems arise from the use of extant taxa and neglecting internal states. Trying to map the rate of evolution using Markov Models, including the error of the internal state reconstruction. - Roland Krause
Independent and dependent Q-matrices are introduced. - Roland Krause
Several szenarios for primate sperm competition, split into low and high cases, looked at 13 candidate genes and 2 controls, more than in the submitted paper. Model slightly modified by setting W1> W0. - Roland Krause
The known gene SEMG2 stands out after multiple testing corrections, other genes not that dramatically. - Roland Krause
Current work on the electron transport chain to look into max lifespans and body size, referring to the metabolic and the longevity hypotheses. - Roland Krause
Metabolic hypothesis slightly supported, ongoing work. - Roland Krause
It's a first model that incorporates rate changes in genotype-phenotype associations. - Roland Krause
Q: For this method, what is the statistical power (sensitivity)? A: Typically, its fairly sensitive. - Roland Krause
...more...
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Proceedings Track: PT33
Towards a Gold Standard for Promoter Prediction Evaluation
Wednesday, July 1 - 10:45 a.m. - 11:10 a.m.
Room: Victoria Hall
Presenting author: Thomas Abeel, Ghent University

ISMB/ECCB 2009 Blog
PT33: Thomas Abeel - Towards a Gold Standard for Promoter Prediction Evaluation
definition of a promoter. Multiple transcription start sites in a given transcriptional unit. - Cass Johnston
1997: first paper evaluating promoter prediction. 9 prediction programs. 15-30% success - Cass Johnston
2004 - whole genome. Precision 5-87% and recall 27-29%. - Cass Johnston
2006 - CAGE tags provide known results to test predictions - Cass Johnston
Need a gold standard as there is no consensus method at the moment, and different tools all claim to be the best. - Cass Johnston
They use data from CAGE (clustered into transcription start regions) and RefSeq (5' end as true promoter site) to test predictors - Cass Johnston
17 programs. All free for academic use and capable of being run on the whole human genome. Ran all programs over various thresholds - Cass Johnston
Plot precision v recall and calc AUC over threshold range for each of the programs. - Cass Johnston
Four programs scoring over 20% in prediction: ARTS, EP3, Eponine, ProSOM - sebi
considered 4 best scoring programs. Found classes of promoters - some with a single peak of tss, some with a couple, a few with many tss all over the region - Cass Johnston
...more...
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Proceedings Track: PT34
Constrained Mixture Estimation for Analysis and Robust Classification of Clinical Time Series
Wednesday, July 1 - 10:45 a.m. - 11:10 a.m.
Room: K2
Presenting author: Ivan Gesteira Costa, Federal University of Pernambuco

ISMB/ECCB 2009 Blog
PT34: Ivan Gesteira Costa - Constrained Mixture Estimation for Analysis and Robust Classification of Clinical Time Series
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Proceedings Track: PT35
DISCOVER: A Feature-Based Discriminative Method for Motif Search in Complex Genomes
Wednesday, July 1 - 11:15 a.m. - 11:40 a.m.
Room: Victoria Hall
Presenting author: Pradipta Ray, Carnegie Mellon University

ISMB/ECCB 2009 Blog
PT35: Pradipta Ray - DISCOVER: A Feature-Based Discriminative Method for Motif Search in Complex Genomes
The problem: find transcription factor binding sites. - Gabriele Sales
In higher organisms, binding sites operate in clusters. - Gabriele Sales
Two approaches: supervides / unsupervised. They used a supervised search. - Gabriele Sales
Traditionally used PWMs to model motifs - Cass Johnston
PWM: position weight matrix. Used to score sequence windows. - Gabriele Sales
Problem: high false positive rate. - Gabriele Sales
An evolution of this idea: hidden markov models. - Gabriele Sales
Two states: motif and background states / distributions. You learn parameters of these models from known data. - Gabriele Sales
The performance of such models has saturated in recent years - Gabriele Sales
problems with HMM / generative models: may tune to noise, rather than the signal; Seem to have hit peak performance; Difficult to incorporate other sources of information to improve predictions - Cass Johnston
...more...
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Proceedings Track: PT36
Graph Theoretical Approach To Study eQTL: A Case Study of Plasmodium Falciparum
Wednesday, July 1 - 11:15 a.m. - 11:40 a.m.
Room: K2
Presenting author: Yang Huang, National Institutes of Health

ISMB/ECCB 2009 Blog
PT36: Yang Huang - Graph Theoretical Approach To Study eQTL: A Case Study of Plasmodium Falciparum
P.falciparum is the most deadly human malaria pathogen - Oliver Hofmann
Little information about gene regulation so far, eQTL might be able to shed some light on this regulation and drug resistance - Oliver Hofmann
SNPs might affect gee expression. Consider expression as a quantitative trait like height, weight. Identify the associated locus by statistical methods. - Oliver Hofmann
Traditional tests between multiple loci, all expression. Comprehensive and without biast, but does not use the inherent data structure, computationally expensive and a problem of statistical power. - Oliver Hofmann
Alternative approach GeD, Graph-based eQTL decomposition. Include strain data in the association graph - Oliver Hofmann
Graph structure: Three types of vertices: gene linked to strain linked to locus - Oliver Hofmann
Find cliques to reduce data complexity - Oliver Hofmann
Each clique has 3 vertices (G/S/L) that are fully connected, in addition each clique is a maximal subgraph that cannot be extended further - Oliver Hofmann
Represent inherent data structures - Oliver Hofmann
Heuristic approach on eQTL cliques to look for (Locus,gene) pairs with certain patterns; refer to graph/diagram in paper - Oliver Hofmann
...more...
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Proceedings Track: PT37
Predictions of RNA Secondary Structure by Combining Homologous Sequence Information
Wednesday, July 1 - 11:45 a.m. - 12:10 p.m.
Room: Victoria Hall
Presenting author: Michiaki Hamada, Mizuho Information & Research Institute, Inc.

ISMB/ECCB 2009 Blog
PT37: Michiaki Hamada - Predictions of RNA Secondary Structure by Combining Homologous Sequence Information
Need for better RNA secondary structure prediction with increasing awareness of functional ncRNAs - Cass Johnston
Most algorithms at the moment don't allow pseudoknots - Cass Johnston
Minimum Free Energy approaches: Mfold, RNAfold etc. But many structures close to MFE - Cass Johnston
Maximizing expected accuracy CONTRAfold etc. - Cass Johnston
CentroidFold (their algorithm) is an MEA tool. Performs better than RNAFold, Sfold, Contrafold... (not sure what the test set was) - Cass Johnston
Using homology to further improve accuracy of structure prediction (previous approaches: RNAalifold, McCaskill) - Cass Johnston
Sankoff sequence/structure alignment of sets of homologous sequences plus MEA. Computationally unfeasible. - Cass Johnston
Approximate the Sankoff method such that it is practical to run the method even for long RNA sequences - Cass Johnston
Compared CentroidAliFold to other state of the art methods. Outperforms conventional secondary structure prediction (ie. MFE-based) and outperforms everything except RAF (comparable) for approaches using homology too. - Cass Johnston
Much quicker than RAF - Cass Johnston
...more...
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Proceedings Track: PT38
A Classifier-based Approach to Identify Genetic Similarities Between Diseases
Wednesday, July 1 - 11:45 a.m. - 12:10 p.m.
Room: K2
Presenting author: Marc A. Schaub, Stanford University

ISMB/ECCB 2009 Blog
PT38: Marc Schaub - A Classifier-based Approach to Identify Genetic Similarities Between Diseases
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Proceedings Track: PT39
Assessing Phylogenetic Motif Models For Predicting Transcription Factor Binding Sites
Wednesday, July 1 - 12:15 p.m. - 12:40 p.m.
Room: Victoria Hall
Presenting author: John Hawkins, University of Queensland

ISMB/ECCB 2009 Blog
PT39: John Hawkins - Assessing Phylogenetic Motif Models For Predicting Transcription Factor Binding Sites
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Proceedings Track: PT40
Model Based Clustering of Array CGH Data
Wednesday, July 1 - 12:15 p.m. - 12:40 p.m.
Room: K2
Presenting author: Sohrab Shah, University of British Columbia

ISMB/ECCB 2009 Blog
PT40: Sohrab Shah - Model Based Clustering of Array CGH Data
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Proceedings Track: PT41
Modeling Interactions between Adjacent Nucleosomes Improves Genome-wide Predictions of Nucleosome Occupancy
Wednesday, July 1 - 2:15 p.m. - 2:40 p.m.
Room: Victoria Hall
Presenting author: Shai Lubliner, Weizmann Institute of Science

ISMB/ECCB 2009 Blog
PT41: Shai Lubliner - Modeling Interactions between Adjacent Nucleosomes Improves Genome-wide Predictions of Nucleosome Occupancy
Nucleosome position affects transcriptional regulation, models for position are required for understanding of TR. - Roland Krause
75-90% of the DNA is associated with nucleosomes, play an important regulatory role. - Oliver Hofmann
Use a recently published model to produce the affinity landscape, showing the suitability of nucleosome occupancy. - Roland Krause
Genomic nuceosome affinity landscape based on a thermodynamical model - Oliver Hofmann
The model is probably published here (http://www.ncbi.nlm.nih.gov/pubmed/19451592) - Roland Krause
Can interactions between nucleosomes introduced into the model? - Roland Krause
Two kinds of interactions: direct or bridged by transcription factors or other proteins. - Roland Krause
Additional interactions are important for chromatin organization. DNA bending proteins, TF, histone modifications, etc. - Oliver Hofmann
First approach focuses on direct interaction, including the distance between the nucleosomes. - Roland Krause
Trying to capture interactions between adjacent nucleosomes (cooperative effects) - Oliver Hofmann
...more...
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Proceedings Track: PT42
Viruses Selectively Mutate Their Cd8 T Cell Epitopes â A Large Scale Immunomic Analysis
Wednesday, July 1 - 2:15 p.m. - 2:40 p.m.
Room: K2
Presenting author: Yoram Louzoun, Bar Ilan University

ISMB/ECCB 2009 Blog
PT42: Yoram Louzoun - Viruses Selectively Mutate Their Cd8 T Cell Epitopes - A Large Scale Immunomic Analysis
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Proceedings Track: PT43
Efficient Exact Motif Discovery
Wednesday, July 1 - 2:45 p.m. - 3:10 p.m.
Room: Victoria Hall
Presenting author: Tobias Marschall, TU Dortmund

ISMB/ECCB 2009 Blog
PT43: Tobias Marschall - Efficient Exact Motif Discovery
Unsupervised motif discovery on a string with no previous knowledege in an automated fashion. - Roland Krause
issues: How to measure over-representation and how to find them. - Roland Krause
184 publications in pubmed for "motif discovery algorithm" - Roland Krause
aim to establish an (almost) exact method based on a rigorous motif statistics - Mikhail Spivakov
given: query text, IUPAC motifs, random text model (background) - for now, iid - Mikhail Spivakov
Calculating a p-value of a given query text, a IUPAC motif and a random text model. - Roland Krause
want: a p-value for a motif - Mikhail Spivakov
use a novel device called probabilistic arithmetic automata - won't go into details - Mikhail Spivakov
Need to compute the distribution of occurrences by chance. Not a straight forward task, recently proposed a new approach by building a probabilistic arithmetic automata. - Roland Krause
an exact calculation - Mikhail Spivakov
...more...
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Proceedings Track: PT44
Family Classification Without Domain Chaining
Wednesday, July 1 - 2:45 p.m. - 3:10 p.m.
Room: K2
Presenting author: Jacob Joseph, Carnegie Mellon University

ISMB/ECCB 2009 Blog
PT44: Jacob Joseph - Family Classification Without Domain Chaining
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Proceedings Track: PT45
A Partition Function Algorithm for Interacting Nucleic Acid Strands
Wednesday, July 1 - 3:15 p.m.- 3:40 p.m.
Room: Victoria Hall
Presenting author: Hamid Reza Chitsaz, Simon Fraser University

ISMB/ECCB 2009 Blog
PT45: Hamid Reza Chitsaz - A Partition Function Algorithm for Interacting Nucleic Acid Strands
First part presented by Raheleh Salari - Roland Krause
Increased interest in RNA-RNA interaction prediction requires computational target prediction. - Roland Krause
ncRNAs bind to mRNA and regulate translation, including the specificity etc. - Roland Krause
Several models have been described, e.g. PairFold, RNAhybrid, RNAup, IRIS, InteRNA. - Roland Krause
Problem np-complete - Roland Krause
All current approaches do not include the probability and stability of the joint secondary structure. - Roland Krause
Interaction energy model and interaction partition function over all SS without pseudoknots or crossing interactions and zigzags. - Roland Krause
The standard model by Matthews et al (1999) assumes an energy model with independence between hairpins, bulges etc. - Roland Krause
More than one loop-loop interaction in a real example, ignore interhybrid loop, other loops termed kissing loops. - Roland Krause
# that was a little fast - Roland Krause
...more...
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Proceedings Track: PT46
Identifying Novel Constrained Elements by Exploiting Biased Substitution Patterns
Wednesday, July 1 - 3:15 p.m.- 3:40 p.m.
Room: K2
Presenting author: Xiaohui Xie, University of California Irvine

ISMB/ECCB 2009 Blog
PT46: Xiaohui Xie - Identifying Novel Constrained Elements by Exploiting Biased Substitution Patterns
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Proceedings Track: PT47
Predicting and Understanding the Stability of G-Quadruplexes
Wednesday, July 1 - 3:45 p.m. - 4:10 p.m.
Room: Victoria Hall
Presenting author: Oliver Stegle, University of Cambridge

ISMB/ECCB 2009 Blog
PT47: Oliver Stegle - Predicting and Understanding the Stability of G-Quadruplexes
G-Quadruplexes are stable structures of RNA and DNA - Roland Krause
Typically of the from GGGACTAAGGGACTTCCCACTTGG - Roland Krause
Will form spontaneously, have role in transcriptional control and telomeres. - Roland Krause
Are these patterns really stable? It's the first indicator of a functional role. Melting temp will be a proxy for stability - Allyson Lister
Overrepresented in promoter regions [Hupper and Balasumbramanian, 2007] - Roland Krause
Melting temperature can be predicted and experimentally verified. It's low throughput though, rules are limited, complicated non-linear relationships. - Roland Krause
Gaussian processes (GP) regression with different error rates across the sequence. - Roland Krause
Gives the posterior distribution of function values given a training set. - Roland Krause
Needs a covariance function (kernel), a likelihood model and hyperparameters. - Roland Krause
Product ansatz to construct a joint covariance function of concentration and sequence. - Roland Krause
...more...
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