Academic Year 2013 Seminars:
Wednesday, May 15
4:00 p.m. - SWIG Boardroom (CIT 241)
Frederick RothFrederick P (Fritz) Roth
Professor, Donnelly Centre for Cellular & Biomedical Research
University of Toronto
"The Dark Side of the Interactome, Barcode Fusion Genetics, and Prix Fixe Menus"
Comparison of a recent (unpublished) map of 14,000 new binary human interactions with other human interaction maps. 2) The 'Barcode Fusion Genetics' strategy to evaluate the fitness of pools of strains each carrying two barcoded alterations. We are using different variants of the method to map yeast genetic interactions and human protein interactions. 3) The Prix Fixe Menu method for computationally identifying causal genes from genome-wide association (GWA) studies.
Hosted by Ben Raphael
SUMMER SEMINARS:
Thursday, August 16th, 2012
11:00 a.m. - SWIG Boardroom (CIT241)
Jian MaAssistant Professor, Dept of Bioengineering
University of Illinois at Urbana-Champaign
College of Engineering
"Self-training Algorithm for Splice Junction Detection Using RNA-seq"
RNA-seq has proven to be a powerful technique for transcriptome profiling based on next-generation sequencing (NGS) technologies. However, due to the short length of NGS reads, it is challenging to accurately map RNA-seq reads to splice junctions (SJs), which is a critically important step in the analysis of alternative splicing (AS) and isoform construction. In this presentation, I will introduce a new method that we recently developed, called TrueSight. Both simulations and real data evaluations showed that TrueSight achieved higher sensitivity and specificity than other methods. We applied TrueSight to discover novel splice forms in honey bee transcriptomes and found that 65% of honey bee multi-exon genes are alternatively spliced. Utilizing new high coverage transcriptome profiling data and gene models improved by TrueSight, our quantitative analysis revealed that the expression ratio of the splice variants of a single gene is significantly correlated with various genomic features. We believe this new tool will be highly useful to comprehensively study the biology of alternative splicing.
Tuesday, August 7, 2012
1:00 p.m. - SWIG Boardroom (CIT 241)
Sagi Snir Professor, Department of Evolutionary & Environmental Biology
University of Haifa
"Using Computational Tools for Piecing Together Small Trees into the Large Tree of Life"
The reconstruction of evolutionary trees is a fundamental task in Biology. The increasing amount of available genomic sequences over thousands of taxa, gave rise to the task of large scale phylogenetic reconstruction. Since accurate reconstruction is limited to few dozens of taxa, the supertree approach, aims at accurately reconstructing small trees over overlapping taxa sets and subsequently amalgamate these trees into a tree over the full taxa set. Perhaps the simplest version of this task that is still widely applicable, yet quite challenging, is quartet based reconstruction. This problem lies at the root of many tree reconstruction methods and theoretical as well as experimental results have been reported. Nevertheless, fundamental problems such as dealing with conflicting quartet trees or even with arbitrary congruent quartet trees remain problematic. In a series of works we have developed a graph theoretically based approach for the supertree task. Our approach is based on a divide and conquer algorithm where our divide step uses a semi-definite programming (SDP) formulation of MaxCut in a graph representing relationships between the organisms. We devised an extremely fast SDP-like heuristic that allows us to extend the input data from several thousands of quartet trees over few dozens of species to tens of millions of quartet trees over thousands of species. These results are promising in the realm of large scale phylogenetic reconstruction. Based on works with Satish Rao and Raphy Yuster. The talk is self-contained and requires no prior knowledge in Biology.
Wednesday, September 19
5:00 p.m. - SWIG Boardroom (CIT 241)
D.E. Shaw Research Event
D.E. ShawD. E. Shaw Research is an independent research laboratory that conducts basic scientific research in the field of computational biochemistry under the direct scientific leadership of Dr. David E. Shaw. Our group is currently focusing on molecular simulations involving proteins and other biological macromolecules of potential interest from both a scientific and a pharmaceutical perspective. Members of the lab include computational chemists and biologists, computer scientists and applied mathematicians, and computer architects and engineers, all working collaboratively within a tightly coupled interdisciplinary research environment.
Our lab has designed and constructed a massively parallel supercomputer called Anton specifically for the execution of molecular dynamics (MD) simulations. Each Anton computer can simulate a single MD trajectory as much as a millisecond or so in duration -- a timescale at which biologically significant phenomena occur. Anton has already generated the world’s longest MD trajectory.
About The Speakers:
Andrew Taube
Andrew Taube is involved in the development of improved force fields for biomolecular simulation. Prior to joining DESRES, Andrew was a John von Neumann Post Doctoral Research Fellow at Sandia National Laboratories. His work focused on using quantum mechanical methods to improve the accuracy of chemical simulations. Andrew received a Ph.D. in physical chemistry from the University of Florida, and a B.S. in chemistry and mathematics from Duke University.
Hillary Green
Hillary Green is involved with simulation studies of biological systems. Hillary graduated from the University of California, Berkeley with a B.S. in materials science and engineering. As an undergraduate researcher at Lawrence Berkeley National Lab, she performed molecular dynamics simulations of the melting of gold nanoparticles. In her spare time, Hillary enjoys sewing stuffed animals, cooking, and teaching math.
Wednesday, September 26
4:00 p.m. - SWIG Boardroom (CIT 241)
Daniel Udwary Daniel Udwary
Assistant Professor of Pharmacognosy
Department of Biomedical & Pharmaceutical Sciences
University of Rhode Island
"SMOR: A database and web analysis tool to identify bacterial secondary metabolism and enable drug discovery"
Many species of bacteria, fungi and plants produce specialized biologically active small molecules used in their natural environment for chemical defense, communication, pigmentation and metal binding, and these so-called natural products, or secondary metabolites, are often collected and utilized by man as pharmaceuticals (commonly antimicrobials and anticancer agents), as dyes, in agriculture, or as inspiration or starting materials for complex chemical syntheses. By virtue of evolutionary pre-selection by the producing organism, naturally derived compounds should be a rich source of bioactive starting materials for drug discovery. Because the carbon or peptide skeletons of natural product molecules are often biosynthesized by one of a relatively small number of enzyme families, the genes responsible can be readily identified in the exponentially increasing number of microbial genomes being sequenced, and in many cases partial chemical structures of the products of these pathways can be predicted. Unfortunately, secondary metabolism genes are very often overlooked or incorrectly annotated by automated genetic analysis tools because of their evolutionary relationships to primary metabolic genes. Currently there is no specialized storehouse for secondary metabolic gene cluster information, nor is there a dedicated online site for discussion and critical analysis. To aid our drug discovery efforts, we have constructed SMOR, a Secondary Metabolism Online Repository. The SMOR analysis software routinely searches for secondary metabolism gene clusters within newly deposited genomes in NCBI's RefSeq resource, re-annotates genes and domains, and stores the information in an easily-searchable MySQL database. Users may examine, comment on, and discuss data through a user-friendly web interface. It is the intention that by enabling community-wide involvement in analysis of microbial secondary metabolism, SMOR will become a useful resource for early-stage drug discovery and biochemical investigations. Database URL: http://www.secondarymetabolism.com
Hosted by Sorin Istrail
Wednesday, October 3
4:00 p.m. - SWIG Boardroom (CIT 241)
Marta Gomez-Chiarri
Marta Gomez-Chiarri
Professor, Department of Fisheries, Animal & Veterinary Science
University of Rhode Island
"Genomic approaches to investigating disease resistance in oysters"
Oysters are keystone species, providing important ecological and economical services in coastal waters. Several bacterial and parasitic diseases of the Eastern oyster, Crassostreae virginica, have expanded in range and increased in severity in recent decades, causing severe mortalities in wild and cultured populations in the Mid-Atlantic and Northeast coasts of the United States. Our laboratory is focused on elucidating mechanisms of disease resistance in Eastern oysters. In order to investigate the mechanisms allowing oysters to survive disease challenges, we have studied the responses of oysters in response to experimental challenge with the bacterial pathogen Roseovarius crassostreae using differential gene expression analysis of gene families. Oysters from two families, one susceptible and one resistant to the pathogen, were challenged with R. crassostreae and tissues were collected at different time points after challenge. The cDNA of oysters from each treatment and time point was sequenced on an Illumina GAIIx platform. We compared the performance of different methods for the de-novo assembly of the oyster transcriptome. Differential gene expression analysis showed a significant difference in the response of oysters susceptible and resistant to the disease. Predicted protein sequences were analyzed for the presence of gene families using TribeMCL and OrthoMCL, and several gene families were selected for further analysis based on differential gene expression patterns. We have also developed a novel visualization tool that allows for mining gene family networks for interesting patterns of gene expression. We have identified several motifs and novel highly diverse gene families that may have an important role in immune defenses in oysters. The identification of potential genes and pathways of the effective host defense response in the eastern oyster is important not only to provide a basis for enhanced breeding techniques, but will also contribute to the evolutionary understanding of innate immunity.
Hosted by Sorin Istrail
Wednesday, October 10
4:00 p.m. - SWIG Boardroom (CIT 241)
Gary Stormo
Gary Stormo
Center for Genome Sciences, Dept of Genetics
School of Medicine, Washington University
"Protein-DNA Interactions: Experimental and Computational Approaches"
Hosted by Sorin Istrail
Wednesday, October 17
4:00 p.m. - SWIG Boardroom (CIT 241)
David RandDavid Rand
Professor of Biology
Brown University
“Mito-Nuclear Co-evolution, Epistasis and Systems Biology”
Wednesday, October 24
4:00 p.m. - SWIG Boardroom (CIT 241)
Matthew Stephens
Matthew Stephens
Professor, Dept of Human Genetics, Dept of Statistics
University of Chicago
"A unified framework for testing multiple phenotypes for association
with genetic variants."
In many ongoing genome-wide association studies, multiple related phenotypes are available for testing for association with genetic variants. In most cases, however, these related phenotypes are analysed independently from one another. For example, several studies have measured multiple lipid-related phenotypes, such as LDL-cholestrol, HDL-cholestrol, and Triglycerides, but in most cases the primary analysis has been a simple univariate scan for each phenotype. This type of univariate analysis fails to make full use of potentially rich phenotypic data.
While this observation is in some sense obvious, much less obvious is the right way to go about examining associations with multiple phenotypes. Common existing approaches include the use of methods such as MANOVA, canonical correlations, or Principal Components Analysis, to identify linear combinations of outcome that are associated with genetic variants. However, if such methods give a significant result, these associations are not always easy to interpret. Indeed the usual approach to explaining observed multivariate associations is to revert to univariate tests, which seems far from ideal.
In this work we outline an approach to dealing with multiple phenotypes based on Bayesian multivariate regression. The method attempts to identify which subset of phenotypes is associated with a given genotype.
In this way it incorporates the null model (no phenotypes associated with genotype); the simple univariate alternative (only one phenotype associated with genotype) and the general alternative (all phenotypes associated with genotype) into a single unified framework. In particular our approach both tests for and explains multivariate associations within a single model, avoiding the need to resort to univariate tests when explaining and interpreting significant multivariate findings. We illustrate the approach on examples, and show how, when combined with multiple phenotype data, the method can improve both power and interpretation of association analyses.
Hosted by Sorin Istrail
Wednesday, November 7
4:00 p.m. - SWIG Boardroom (CIT 241)
Casey Dunn
Casey Dunn, Assistant Professor of Biology
Dunn Lab, Brown University
Dept of Ecology & Evolutionary Biology
"Phylogenetics and functional genomics in non-model organisms".
Wednesday, November 14
4:00 p.m. - SWIG Boardroom (CIT 241)
Guillaume Borque
Guillaume Bourque
Associate Professor, Department of Human Genetics
McGill University & Genome Quebec Innovation Center
"The role of genomic repeats in host gene regulation"
Next-generation sequencing (NGS) technologies (e.g. ChIP-Seq, RNA-Seq) have supplanted array-based technologies because of their accuracy, comprehensiveness and cost. One of the strengths of these experiments is that they enable an unbiased look at the functional contributions of the genome including the contributions of repetitive regions. We will present results that demonstrate the ubiquitous role that play repeats in gene regulation. In particular, we will show that species-specific transposable elements (TEs) have been an important source of new regulatory elements and have contributed more than 20% of the binding sites of key transcription factors in human ES cells. Using DNase I hypersensitivity data sets from ENCODE in normal, embryonic, and cancer cells, we will also show that 44% of open chromatin regions are in TEs and that this proportion reaches 63% for primate-specific regions. We also show that distinct subfamilies of endogenous retroviruses (ERVs) have contributed significantly more accessible regions than expected by chance with up to 80% of their instances in open chromatin. In this way, we will demonstrate that TEs, and in particular ERVs, have contributed hundreds of thousands of novel regulatory elements to the primate lineage and reshaped the human transcriptional landscape.
Hosted by Ben Raphael
Wednesday, December 5
4:00 p.m. - SWIG Boardroom (CIT 241)
Marta Luksza
Marta Luksza
Dana Pe'er Lab of Computational Systems Biology
Columbia University
"A predictive fitness model for influenza"
The seasonal human influenza A (H3N2) virus undergoes rapid evolution, which produces significant year-to-year sequence turnover in the population of circulating viral strains. Adaptive mutations occur primarily in antigenic epitopes, the antibody-binding domains of the viral surface protein haemagglutinin. Here we develop a fitness model for haemagglutinin sequences that predicts frequency changes in the viral population from one year to the next. In particular, the model accurately predicts influenza's characteristic punctuated pattern of epitope amino acid substitutions. We discuss consequences for the epidemiology of influenza and other fast-evolving pathogens.
Hosted by Daniel Weinreich
Prospective Postdoctoral Seminars:
Fernando Mendez (University of Arizona) - Tuesday, January 8th - 10:00 - 11:00 am - SWIG Boardroom CIT 241
Wednesday, January 23
4:00 p.m. - SWIG Boardroom (CIT 241)
Brian Tjaden
Brian Tjaden
Theresa Mall Mullarkey Associate Professor of Computer Science
Wellsley College
"The Elusive Gene"
Over the past couple of decades, the number of sequenced nucleic acids has increased at an exponential rate. In this talk, we look at the large data sets generated by high throughput sequencing technology and at algorithms for extracting insights from this wealth of data. We will focus on computational models and methods that incorporate RNA sequencing data for the purpose of elucidating some of the most elusive genes in a genome, namely bacterial small RNA genes. We will present results on how these elusive small RNA genes may be identified in a genome and how their action within the cell can be determined.
Hosted by Erica Larschan & Ben Raphael
Wednesday, March 13
4:00 p.m. - SWIG Boardroom (CIT 241)
Julius LucksJulius Lucks, Assistant Professor
Dept of Chemical & Biomolecular Engineering, James C. & Rebecca Q. Morgan Sesquicentennial Faculty Fellow
Cornell University
"A Platform for Engineering RNA Regulatory Networks using High Throughput RNA Structure Characterization"
A central goal of synthetic biology is to develop a toolkit of building blocks that can be used to engineer whole biomolecular and cellular systems from the bottom up. A key component of this toolkit are biological parts that regulate gene expression in ways that can be used to construct networks that integrate and propagate cellular information according to design. Recently, non-coding RNA-based gene regulatory mechanisms have emerged as powerful and versatile substrates for regulatory parts development. In particular, our work has focused on engineering an RNA-based transcription attenuation mechanism in bacteria, which we have shown can be configured in different ways to independently regulate multiple targets, integrate signals and evaluate genetic logics, and directly propagate signals in RNA genetic networks. In addition, we have shown that we can engineer these RNAs to change their fold and thus function in response to small molecule and protein signals, giving us additional versatility in making any network link tunable. However, the construction of more sophisticated RNA-based circuitry requires larger numbers of attenuators, which are not found in nature. Motivated by modularity observed in natural RNA riboswitch regulators, we have devised a strategy to expand our library of attenuators by creating chimeric fusions between our regulator and RNA binding domains from other families of natural non-coding RNAs. We show that functional attenuators can be constructed through specific chimeric fusions. Furthermore, by applying SHAPE-Seq, our high-throughput RNA structure characterization platform, we are beginning to develop RNA structure/function design rules that will allow the creation of larger numbers of orthogonal RNA transcription regulators. In this talk I will describe the foundations of our RNA regulatory platform, and the technology behind SHAPE-Seq. I will conclude with thoughts about how these efforts will expand our ability to construct larger synthetic RNA networks, as well as contribute to a systems-level understanding of RNA structure/function modularity that will lead to a deeper understanding of RNA’s role in biology.
Hosted by Chip Lawrence
Wednesday, March 20
4:00 p.m. - SWIG Boardroom (CIT 241)
Eric Stone
Eric Stone
Associate Professor of Genetics
Bioinformatics Research Center
North Carolina State University
"Down the rabbit hole in support of Drosophila systems genetics"
In this talk, I will introduce the Drosophila melanogaster genetic reference panel (DGRP) as a resource for interrogating the genotype-phenotype map. My emphasis will be on just-in-time methods development, along with how those methods have provided insight into various biological processes. In particular, I will discuss some unforeseen challenges in linking genomic and endophenotypic variation to variation in organismal phenotypes. I will attempt to summarize what has been learned as well as the next set of challenges on the horizon.
Hosted by David Rand
Wednesday, April 3
4:00 p.m. - SWIG Boardroom (CIT 241)
Ben EvansBen Evans
Associate Professor, Biology Department
McMaster University
Ontario, Canada
"Monkey sex chromosomes: the impact of social system and demography on genome evolution."
Because it is present in two copies in females but only one in males, the primate X chromosome is expected to have fewer copies than autosomal chromosomes. Under certain conditions including a sex ratio equal to one, the copy number ratio of X chromosomes to autosomal chromosomes is expected to be 0.75. In this seminar I will discuss simulation studies that explore the effect of social systems and population size dynamics on genome evolution, including the X to autosomal chromosome copy number ratio. I will then relate these findings to molecular polymorphism data from papionin monkeys, and discuss evolutionary implications that are not particularly consistent with expectations based on long term field studies these monkeys.
Hosted by Will Fairbrother
Wednesday, April 10
4:00 p.m. - SWIG Boardroom (CIT 241)
David MathewsDavid Mathews
Associate Professor, Dept of Biochemistry and Biophysics
University of Rochester, Medical Center
“RNA Partition Functions: Structure Prediction Beyond a Single Lowest Free Energy Structure”
Hosted by Chip Lawrence
Wednesday, April 24
4:00 p.m. - SWIG Boardroom (CIT 241)
James Padbury
James Padbury
Oh-Zopfi Professor of Pediatrics & Perinatal Research
Brown University
Chair for Research Women and Infants' Hospital
“Bioinformatic Approach to Complex Diseases”
While genome-wide association studies (GWAS) have become a popular approach to the investigation of complex phenotypes, they have failed to demonstrate the “missing heritability” in many common diseases. We have developed an alternative approach to identify a more manageable set of genes which, nonetheless, incorporate some elements of the discovery process in genome-wide association. We have used web-based semantic data mining and natural language processing to extract published literature with a priori biological evidence for association of specific genes or genetic variants with the phenotype of interest. This is combined with aggregation of data from public databases, expression data, linkage analyses and other high dimension datasets like proteomics. Lastly, we use pathway analysis to impute missing genes that likely belong to the dataset, lacking only experimental evidence. Using this approach to analyze the genetic architecture of preterm birth, we have identified 19 significant pathways including 53 significant genes, which withstood FDR correction for multiple testing (Genomics 101:163, 2013). We will show our results for this important disease and illustrate our progress in targeted re-sequencing these genomic regions. We will also illustrate the generalizability of this approach to other complex disorders and phenotypes.
Friday, May 3 - All Day
Evolution of Cancer Symposium
Alpert Medical School - Lecture Hall 170
Presented with the Department of Pathology & Laboratory Medicine
Wednesday, May 8
4:00 p.m. - SWIG Boardroom (CIT 241)
Shamil SunyaevShamil Sunyaev
Associate Professor, Division of Genetics, Dept. of Medicine
Brigham & Women's Hospital & Harvard Medical School
"Human germ line and somatic mutation rates: evolution, biology and statistical genetics"
Sequencing technology enabled systematic identification of de novo
germ line mutations and somatic mutations in cancer. Mutation rate
appears to be variable along the human genome. Replication timing,
chromatin accessibility and negative selection maintaining
hypermutable sequence contexts all contribute to the mutation rate
heterogeneity. The data on somatic mutations suggest that Global
Genome Repair (GGR) system is responsible for the dependence of
mutation rate on chromatin accessibility. Two evolutionary models
may potentially explain the origin of mutation rate heterogeneity.
The heterogeneity of mutation rate along the human genome has
important consequences for evolutionary genomics and for statistical
genetics approaches based on recurrent mutations. Analysis of de novo
mutations also helps finding genes underlying Mendelian diseases.
Hosted by Will Fairbrother