When preforming multiple sequence alignments each aligner has a multitude of parameters that must be set, and can greatly affect alignment quality. Most users rely on the default parameter settings, which are optimal on average but may produce a low-quality alignment for the given inputs. In this talk I describe an approach called parameter advising to find a parameter setting that produces a high-quality alignment for each input. To perform parameter advising I developed a new accuracy estimator called Facet (short for “feature-based accuracy estimator”) that computes an accuracy estimate as a linear combination of efficiently computable feature functions. I further applied parameter advising (i) to ensemble alignment, which uses the advising process to choose both the aligner and its parameter settings, and (ii) to adaptive local realignment, which chooses distinct parameter choices to conform to mutation rates as they vary across the lengths of the sequences. Us! ing Facet for parameter advising boosts advising accuracy by almost 20% beyond using a single default parameter choice for the hardest-to-align benchmarks

About the Speaker

Identifying and deciphering the complex regulatory information embedded in the genome is critical to our understanding of biology and the etiology of complex diseases. The regulation of gene expression is governed largely by the occupancy of transcription factors (TFs) at various cognate binding sites. Characterizing TF binding is particularly challenging since TF occupancy is not just complex but also dynamic. Current genome-wide surveys of TF binding sites typically use chromatin immunoprecipitation (ChIP), which is limited to measuring one TF at a time, thus less scalable in profiling the dynamics of TF occupancy across cell types or conditions. This work develops novel computational frameworks to model sequencing data from DNase and/or MNase nuclease digestion assays that allows multiple TFs to be surveyed in a single experiment. These frameworks serve as an innovative and cost effective strategy which enables efficient profiling of TF occupancy landscapes across different cell types or dynamic conditions in a high-throughput manner.

The process of aging is associated with broad changes in the brain at the cognitive, neural circuit, and cellular level.  Aging of the brain progresses at different rates in the human population, but the genetic basis of those differences has remained unclear. In this study, we searched for a genetic signature of aging by combing genotype data with frontal cortex post-mortem gene experssion data from four cohorts: University of Pittsburgh, NIH Braincloud, GTEx, and the Religious Order Study/Memory and Aging project based at Rush University.

We found aging-associated genetic cvariation near synaptic genes, including one SNP that was nominally significant in all cohorts (p <0.01) and genome-wide significant in a meta-analysis (p=8.4E-9).  A comparison brain age to Alzheimer's disease and the underlying genetics showed that brain aging and APOE status are independent predictors of Alzheimer's disease. Our study provides a systematic framework to uncover the mechanisms that drive brain aging and how it relates to neurodegenerative disorders like Alzheimer's disease.

Understanding how regulatory sequence works is one of the greatest challenges facing molecular biology, and the next major hurdle in human genetics. There is now a wealth of data on individual genome sequences, chromatin profiles, and expression outputs, but much less is known about the mechanisms that specify and link them: the details of how cells identify regulatory sequences, or how their functions are exerted, are surprisingly difficult to decipher. I will describe three interlinked objectives of my research program: determination and compilation of motifs for transcription factors (TFs); mapping of their effector functions; and development of computational models of regulatory sequence identity and function.

About the Speaker

Recent advances in next-generation sequencing (NGS) technologies have provided us with an unprecedented opportunity to better characterize the molecular signatures of human cancers. One hallmark of cancer genomes is aneuploidy, which engenders abnormal copy numbers amongst broadly connected sets of alleles. Structural variations (SVs) further modify the aneuploid cancer genomes into a mixture of rearranged genomic segments with extensive somatic copy number alterations (CNAs). In this talk, I will introduce a new algorithm called Weaver to provide integrated quantification of SVs and CNAs in aneuploid cancer genomes. Such an integrated approach enables a greatly enhanced grasp of the complex genomic architectures inherent to many cancer genomes. Our evaluations demonstrated that Weaver is highly accurate and will greatly refine the structural analysis of complex cancer genomes.

About the Speaker

Understanding the genetic underpinnings of disease is important for screening, treatment, drug development, and basic biological insight. Genome and epigenome-wide associations, wherein individual or sets of (epi) genetic markers are systematically scanned for association with disease are one window into disease processes. Naively, these associations can be found by use of a simple statistical test. However, a wide variety of confounders lie hidden in the data, leading to both spurious associations and missed associations if not properly addressed. These confounders include population structure, family relatedness, and cell type heterogeneity. I will discuss state-of-the art statistical approaches, based on linear mixed models, for conducting these analyses. In these approaches, confounding factors are automatically deduced, and then corrected for. Challenges include efficient computation and model optimization for increased power. Finally, I will discuss how insights from these areas can be leveraged to tackle the problem of uncovering latent sub-phenotypes—that is uncovering hidden case clusters for imprecisely defined phenotypes such as depression and type 2 diabetes.

About the Speaker

One of the fundamental mysteries of biology lies in the ability of cells to convert from one phenotype to another in response to external control inputs. We have been studying the Epithelial-to-Mesenchymal Transition (EMT), which allows organized groups of epithelial cells to scatter into lone mesenchymal cells. EMT is critical for normal development and wound healing, and may be important for cancer metastasis. First, I’ll brief you on our efforts to use statistical methods to summarize about 12,500 publications on EMT. This analysis revealed interesting statistical anomalies. For example, the number of discrete factors that authors invoked to explain EMT and its regulation was bounded above by the capacity limit of the human brain to follow statistical interactions among variables. In the second part of my talk, I’ll show you our recent data on disorganizing mammary epithelial structures. We have used CRISPR to insert fluorescent tags directly into eight EMT-related genes (such as E-cadherin and Vimentin), which allows us to monitor the transition in real time, subject only to delays imposed by fluorophore folding/maturation times.

Jan Liphardt received a BA from Reed College (1993-96) and a PhD from Cambridge University, UK (Churchill College, 1996-99). After 2 years as a joint postdoc in the labs of Carlos Bustamante and Nacho Tinoco, Jr. (in the UC Berkeley Physics and Chemistry Depts.), he became the divisional fellow of the Physical Biosciences Division at Lawrence Berkeley National Lab. He joined the Berkeley Physics faculty in 2004. Jan is now an associate professor of Bioengineering at Stanford University. Jan is a Searle Scholar, a Sloan Research Fellow, a Hellman Fellow, and the recipient of the 2007 Mohr Davidow Ventures Innovator’s Award. Basic research in his lab is funded by federal agencies such as the NCI, NIGMS, NSF, and the DOE.

Faculty Host: Jianhua Xing

The ability of intrinsically disordered proteins (IDPs) to adopt substrate-specific three-dimensional (3D) structures in response to specific stimuli such as other proteins, small molecules, environmental and chemical changes enables them to propagate and relay a variety of control signals that ultimately determine the fate of a cell, including growth, reproduction and death. Reverse engineering the structural details of how IDPs morph and function is, therefore, a critical step towards developing novel therapeutic approaches to target cancer, diabetes, neurodegenerative and cardiovascular diseases. In this talk, I will outline some strategies we are developing at Oak Ridge National Laboratory in integrating neutron scattering techniques, molecular dynamics simulations and Bayesian inference methodologies to provide mechanistic insights into IDP function/dysfunction.

I will also take this opportunity to present my views on establishing a research career at government labs.

About the Speaker

Single-cell gene expression analysis affords a new level of resolution for studying cell state dynamics. Cell differentiation into various cell types but also the development of malignant cells are manifestations of cell state dynamics. The ability of a complex gene regulatory network to produce, without mutations, a vast diversity of robust, biologically distinct, inheritable cell states ("attractors") as manifestation of the principle of multi-stability in non-linear dynamical systems, has led to the idea that cancer are cells are trapped in "abnormal attractors" that are not meant to represent physiological cell states. This adds a layer of complication to the standard model of cancer in which Darwinian somatic selection of mutant cells that carry "driver mutations" drive tumor progression. This also means that "cancer without mutations" is in principle possible - as recently found.

We really need to overcome the orthodoxy of a rigid 1:1 mapping between genotype and phenotype in which genetic mutations are the sole agent of permanent and progressing change, and embrace non-genetic phenotypic plasticity, notably, inducible non-genetic state changes in our thinking about tumorigenesis. But to do so properly we need to go beyond hand-waging models and adopt a formal framework.

In this talk I will present the theoretical framework and the experimental findings supporting this thinking. We have formalized non-genetic cell phenotype plasticity as a dynamical system governed by the gene regulatory network. In this framework the distinct, stable biological cell states are attractor states in the high-dimensional gene expression state space.  Cancer cells occupy particular ("physiologically forbidden") attractor states, failing to descend to the “normal attractors”, and therapy constitutes a perturbation that seeks to push cells out of these cancer attractors into those that represent the apoptotic cell fates. In this formalism a transition between stable attractor states is a symmetry-breaking bifurcation in which the current attractor is destabilized and other attractors become accessible into which the cell will descend. This constitutes a much studied “critical state transition” but in a high-dimensional space. Importantly, in a complex multi-stable system ("rugged epigenetic landscape") destabilization of an attractor also opens up new access to many "hidden" attractor states never intended to be occupied by a cell and even more different from the physiological ones. Now, as the cancer cells exit the cancer attractor during treatment-induced destabilization of their state, not only will they, as desired, move to the target phenotype (the apoptotic state) but: some cells may also "spill" into these newly accessible neighboring attractors which may represent even more stem-like, hence more malignant states. These aberrant non-killed "rebellious cells" triggered by sub-lethal therapy-stress may plant the seed for recurrence.

It is in this sense that recurrence of tumors after treatment is not so much described by Darwinian "survival of the fittest" but perhaps more aptly by Nietzsche's principle: "What does not kill me strengthens me".  Because of the fundamental need for attractor destabilization in therapy, it is likely that the latter principle widely applies.  It does of course not exclude Darwinian selection of genetic mutants -in contrary it facilitates it by enhancing the probability of cells surviving treatment. Because of the importance of attractor destabilization we developed a tool to detect shifts of cell populations towards bifurcations in which attractors vanish. Indeed, we observed in single-cell resolution measurements of cells undergoing phenotype transitions signatures of postulated critical state transition, as well as the rebellious cells predicted by theory that move into attractors in the opposite direction from that of the desired transition.  It follows the general postulate that there is an inherent limitation to any, however selectively targeting cancer drug, as long as it seeks to destabilize the cancerous state.  Thus cancer therapy that seeks to kill tumor cells may be more akin to herding cats (than sheep): inherently very difficult.

About the Speaker


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