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BIOINFORMATICS Doi:10.1093/Bioinformatics/Bti144
Vol. 00 no. 0 2004, pages 1–11 BIOINFORMATICS doi:10.1093/bioinformatics/bti144 Solving and analyzing side-chain positioning problems using linear and integer programming Carleton L. Kingsford, Bernard Chazelle and Mona Singh∗ Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, 35, Olden Street, Princeton, NJ 08544, USA Received on August 1, 2004; revised on October 10, 2004; accepted on November 8, 2004 Advance Access publication … ABSTRACT set of possible rotamer choices (Ponder and Richards, 1987; Motivation: Side-chain positioning is a central component of Dunbrack and Karplus, 1993) for each Cα position on the homology modeling and protein design. In a common for- backbone. The goal is to choose a rotamer for each position mulation of the problem, the backbone is fixed, side-chain so that the total energy of the molecule is minimized. This conformations come from a rotamer library, and a pairwise formulation of SCP has been the basis of some of the more energy function is optimized. It is NP-complete to find even a successful methods for homology modeling (e.g. Petrey et al., reasonable approximate solution to this problem. We seek to 2003; Xiang and Honig, 2001; Jones and Kleywegt, 1999; put this hardness result into practical context. Bower et al., 1997) and protein design (e.g. Dahiyat and Mayo, Results: We present an integer linear programming (ILP) 1997; Malakauskas and Mayo, 1998; Looger et al., 2003). In formulation of side-chain positioning that allows us to tackle homology modeling, the goal is to predict the structure for a large problem sizes. -
Grammar String: a Novel Ncrna Secondary Structure Representation
Grammar string: a novel ncRNA secondary structure representation Rujira Achawanantakun, Seyedeh Shohreh Takyar, and Yanni Sun∗ Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824 , USA ∗Email: [email protected] Multiple ncRNA alignment has important applications in homologous ncRNA consensus structure derivation, novel ncRNA identification, and known ncRNA classification. As many ncRNAs’ functions are determined by both their sequences and secondary structures, accurate ncRNA alignment algorithms must maximize both sequence and struc- tural similarity simultaneously, incurring high computational cost. Faster secondary structure modeling and alignment methods using trees, graphs, probability matrices have thus been developed. Despite promising results from existing ncRNA alignment tools, there is a need for more efficient and accurate ncRNA secondary structure modeling and alignment methods. In this work, we introduce grammar string, a novel ncRNA secondary structure representation that encodes an ncRNA’s sequence and secondary structure in the parameter space of a context-free grammar (CFG). Being a string defined on a special alphabet constructed from a CFG, it converts ncRNA alignment into sequence alignment with O(n2) complexity. We align hundreds of ncRNA families from BraliBase 2.1 using grammar strings and compare their consensus structure with Murlet using the structures extracted from Rfam as reference. Our experiments have shown that grammar string based multiple sequence alignment competes favorably in consensus structure quality with Murlet. Source codes and experimental data are available at http://www.cse.msu.edu/~yannisun/grammar-string. 1. INTRODUCTION both the sequence and structural conservations. A successful application of SCFG is ncRNA classifica- Annotating noncoding RNAs (ncRNAs), which are tion, which classifies query sequences into annotated not translated into protein but function directly as ncRNA families such as tRNA, rRNA, riboswitch RNA, is highly important to modern biology. -
120421-24Recombschedule FINAL.Xlsx
Friday 20 April 18:00 20:00 REGISTRATION OPENS in Fira Palace 20:00 21:30 WELCOME RECEPTION in CaixaForum (access map) Saturday 21 April 8:00 8:50 REGISTRATION 8:50 9:00 Opening Remarks (Roderic GUIGÓ and Benny CHOR) Session 1. Chair: Roderic GUIGÓ (CRG, Barcelona ES) 9:00 10:00 Richard DURBIN The Wellcome Trust Sanger Institute, Hinxton UK "Computational analysis of population genome sequencing data" 10:00 10:20 44 Yaw-Ling Lin, Charles Ward and Steven Skiena Synthetic Sequence Design for Signal Location Search 10:20 10:40 62 Kai Song, Jie Ren, Zhiyuan Zhai, Xuemei Liu, Minghua Deng and Fengzhu Sun Alignment-Free Sequence Comparison Based on Next Generation Sequencing Reads 10:40 11:00 178 Yang Li, Hong-Mei Li, Paul Burns, Mark Borodovsky, Gene Robinson and Jian Ma TrueSight: Self-training Algorithm for Splice Junction Detection using RNA-seq 11:00 11:30 coffee break Session 2. Chair: Bonnie BERGER (MIT, Cambrige US) 11:30 11:50 139 Son Pham, Dmitry Antipov, Alexander Sirotkin, Glenn Tesler, Pavel Pevzner and Max Alekseyev PATH-SETS: A Novel Approach for Comprehensive Utilization of Mate-Pairs in Genome Assembly 11:50 12:10 171 Yan Huang, Yin Hu and Jinze Liu A Robust Method for Transcript Quantification with RNA-seq Data 12:10 12:30 120 Zhanyong Wang, Farhad Hormozdiari, Wen-Yun Yang, Eran Halperin and Eleazar Eskin CNVeM: Copy Number Variation detection Using Uncertainty of Read Mapping 12:30 12:50 205 Dmitri Pervouchine Evidence for widespread association of mammalian splicing and conserved long range RNA structures 12:50 13:10 169 Melissa Gymrek, David Golan, Saharon Rosset and Yaniv Erlich lobSTR: A Novel Pipeline for Short Tandem Repeats Profiling in Personal Genomes 13:10 13:30 217 Rory Stark Differential oestrogen receptor binding is associated with clinical outcome in breast cancer 13:30 15:00 lunch break Session 3. -
BIOGRAPHICAL SKETCH NAME: Berger
BIOGRAPHICAL SKETCH NAME: Berger, Bonnie eRA COMMONS USER NAME (credential, e.g., agency login): BABERGER POSITION TITLE: Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training and residency training if applicable. Add/delete rows as necessary.) EDUCATION/TRAINING DEGREE Completion (if Date FIELD OF STUDY INSTITUTION AND LOCATION applicable) MM/YYYY Brandeis University, Waltham, MA AB 06/1983 Computer Science Massachusetts Institute of Technology SM 01/1986 Computer Science Massachusetts Institute of Technology Ph.D. 06/1990 Computer Science Massachusetts Institute of Technology Postdoc 06/1992 Applied Mathematics A. Personal Statement Advances in modern biology revolve around automated data collection and sharing of the large resulting datasets. I am considered a pioneer in the area of bringing computer algorithms to the study of biological data, and a founder in this community that I have witnessed grow so profoundly over the last 26 years. I have made major contributions to many areas of computational biology and biomedicine, largely, though not exclusively through algorithmic innovations, as demonstrated by nearly twenty thousand citations to my scientific papers and widely-used software. In recognition of my success, I have just been elected to the National Academy of Sciences and in 2019 received the ISCB Senior Scientist Award, the pinnacle award in computational biology. My research group works on diverse challenges, including Computational Genomics, High-throughput Technology Analysis and Design, Biological Networks, Structural Bioinformatics, Population Genetics and Biomedical Privacy. I spearheaded research on analyzing large and complex biological data sets through topological and machine learning approaches; e.g. -
ABSTRACT HISTORICAL GRAPH DATA MANAGEMENT Udayan
ABSTRACT Title of dissertation: HISTORICAL GRAPH DATA MANAGEMENT Udayan Khurana, Doctor of Philosophy, 2015 Dissertation directed by: Professor Amol Deshpande Department of Computer Science Over the last decade, we have witnessed an increasing interest in temporal analysis of information networks such as social networks or citation networks. Finding temporal interaction patterns, visualizing the evolution of graph properties, or even simply com- paring them across time, has proven to add significant value in reasoning over networks. However, because of the lack of underlying data management support, much of the work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. Unfortunately, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spread of epidemics, informa- tion diffusion, formation of online communities, and so on. In the absence of appropriate support, an analyst today has to manually navigate the added temporal complexity of large evolving graphs, making the process cumbersome and ineffective. In this dissertation, I address the key challenges in storing, retrieving, and analyzing large historical graphs. In the first part, I present DeltaGraph, a novel, extensible, highly tunable, and distributed hierarchical index structure that enables compact recording of the historical information, and that supports efficient retrieval of historical graph snapshots. I present analytical models for estimating required storage space and snapshot retrieval times which aid in choosing the right parameters for a specific scenario. I also present optimizations such as partial materialization and columnar storage to speed up snapshot retrieval. In the second part, I present Temporal Graph Index that builds upon DeltaGraph to support version-centric retrieval such as a node’s 1-hop neighborhood history, along with snapshot reconstruction. -
Michael S. Waterman: Breathing Mathematics Into Genes >>>
ISSUE 13 Newsletter of Institute for Mathematical Sciences, NUS 2008 Michael S. Waterman: Breathing Mathematics into Genes >>> setting up of the Center for Computational and Experimental Genomics in 2001, Waterman and his collaborators and students continue to provide a road map for the solution of post-genomic computational problems. For his scientific contributions he was elected fellow or member of prestigious learned bodies like the American Academy of Arts and Sciences, National Academy of Sciences, American Association for the Advancement of Science, Institute of Mathematical Statistics, Celera Genomics and French Acadèmie des Sciences. He was awarded a Gairdner Foundation International Award and the Senior Scientist Accomplishment Award of the International Society of Computational Biology. He currently holds an Endowed Chair at USC and has held numerous visiting positions in major universities. In addition to research, he is actively involved in the academic and social activities of students as faculty master Michael Waterman of USC’s International Residential College at Parkside. Interview of Michael S. Waterman by Y.K. Leong Waterman has served as advisor to NUS on genomic research and was a member of the organizational committee Michael Waterman is world acclaimed for pioneering and of the Institute’s thematic program Post-Genome Knowledge 16 fundamental work in probability and algorithms that has Discovery (Jan – June 2002). On one of his advisory tremendous impact on molecular biology, genomics and visits to NUS, Imprints took the opportunity to interview bioinformatics. He was a founding member of the Santa him on 7 February 2007. The following is an edited and Cruz group that launched the Human Genome Project in enhanced version of the interview in which he describes the 1990, and his work was instrumental in bringing the public excitement of participating in one of the greatest modern and private efforts of mapping the human genome to their scientific adventures and of unlocking the mystery behind completion in 2003, two years ahead of schedule. -
John Anthony Capra
John Anthony Capra Contact Vanderbilt University e-mail: tony.capra-at-vanderbilt.edu Information Dept. of Biological Sciences www: http://www.capralab.org/ VU Station B, Box 35-1634 office: U5221 BSB/MRB III Nashville, TN 37235-1634 phone: (615) 343-3671 Research • Applying computational methods to problems in genetics, evolution, and biomedicine. Interests • Integrating genome-scale data to understand the functional effects of genetic differences between individuals and species. • Modeling evolutionary processes that drive the creation of lineage-specific traits and diseases. Academic Vanderbilt University, Nashville, Tennessee USA Employment Assistant Professor, Department of Biological Sciences August 2014 { Present Assistant Professor, Department of Biomedical Informatics February 2013 { Present Investigator, Center for Human Genetics Research Education And Gladstone Institutes, University of California, San Francisco, CA USA Training Postdoctoral Fellow, October 2009 { December 2012 • Advisor: Katherine Pollard Princeton University, Princeton, New Jersey USA Ph.D., Computer Science, June 2009 • Advisor: Mona Singh • Thesis: Algorithms for the Identification of Functional Sites in Proteins M.A., Computer Science, October 2006 Columbia College, Columbia University, New York, New York USA B.A., Computer Science, May 2004 B.A., Mathematics, May 2004 Pembroke College, Oxford University, Oxford, UK Columbia University Oxford Scholar, October 2002 { June 2003 • Subject: Mathematics Honors and Gladstone Institutes Award for Excellence in Scientific Leadership 2012 Awards Society for Molecular Biology and Evolution (SMBE) Travel Award 2012 PhRMA Foundation Postdoctoral Fellowship in Informatics 2011 { 2013 Princeton University Wu Graduate Fellowship 2004 { 2008 Columbia University Oxford Scholar 2002 { 2003 Publications Capra JA* and Kostka D*. Modeling DNA methylation dynamics with approaches from phyloge- netics. -
Gene and Genome Duplication David Sankoff
681 Gene and genome duplication David Sankoff Genomic sequencing projects have revealed the productivity of tetraploidization. I also summarize some mathematical processes duplicating genes or entire chromosome segments. modeling and algorithmics inspired by duplication phenomena. Substantial proportions of the yeast, Arabidopsis and human gene complements are made up of duplicates. This has prompted much Gene duplication interest in the processes of duplication, functional divergence and Li et al. [2] find that duplicated genes, as identified through loss of genes, has renewed the debate on whether an early fairly selective criteria, account for ~15% of the protein genes vertebrate genome was tetraploid, and has inspired mathematical in the human genome (counting both genes in each pair). In a models and algorithms in computational biology. survey of eukaryotic genome sequences, Lynch and Conery [3••], using a somewhat different filter, accounted for ~8%, Addresses 10% and 20% of the gene complement of the fly, yeast and Centre de recherches mathématiques, Université de Montréal, worm genomes, respectively. (Other estimates put the figure CP 6128 succursale Centre-Ville, Montreal, Québec H3C 3J7, Canada; at 16% for yeast and 25% for Arabidopsis [4•].) They estimated e-mail: [email protected] highly variable rates of gene duplication, averaging ~0.01 per Current Opinion in Genetics & Development 2001, 11:681–684 gene per Myr (million years). On the basis of ratios of silent and replacement rearrangements, they found that there is 0959-437X/01/$ — see front matter © 2001 Elsevier Science Ltd. All rights reserved. typically a period of neutral or (occasionally) even slightly accelerated evolution, lasting a few Myr at most, with one of Abbreviation the copies eventually being silenced in a large majority of Myr million years cases, and the remaining ones undergoing relatively stringent purifying selection. -
Graduation 2019
Department of Graduation Computer Science Celebration & Awards Dinner 2019 Evening Schedule 6:00pm Social Time 7:00pm Welcome Dr. Sanjeev Setia, Chair Department of Computer Science 7:10pm Dinner 8:00pm Presentation of Awards Dr. Sanjeev Setia, Chair Department of Computer Science Doctor of Philosophy Computer Science Indranil Banerjee Dissertation Title: Problems on Sorting, Sets and Graphs Major Professor: Dana Richards, PhD Arda Gumusalan Dissertation Title: Dynamic Modulation Scaling Enabled Real Time Transmission Scheduling For Wireless Sensor Networks Major Professor: Robert Simon, PhD Yun Guo Dissertation Title: Towards Automatically Localizing and Repairing SQL Faults Major Professors: Jeff Offut, PhD & Amihai Motro, PhD Mohan Krishnamoorthy Dissertation Title: Stochastic Optimization based on White-box Deterministic Approximations: Models, Algorithms and Application to Service Networks Major Professors: Alexander Brodsky, PhD & Daniel Menascé, PhD Arsalan Mousavian Dissertation Title: Semantic and 3D Understanding of a Scene for Robot Perception Major Professor : Jana Kosecka, PhD Zhiyun Ren Dissertation Title: Academic Performance Prediction with Machine Learning Techniques Major Professor : Huzefa Rangwala, PhD Md A. Reza Dissertation Title: Scene Understanding for Robotic Applications Major Professor : Jana Kosecka, PhD Venkateshwar Tadakamalla Dissertation Title: Analysis and Autonomic Elasticity Control for Multi-Server/Queues Under Traffic Surges in Cloud Environments Major Professor : Daniel A. Menascé, PhD Jianchao Tan -
Emerging Topics in Biological Networks and Systems Biology Symposium at the Swedish Collegium for Advanced Study (Scas), Uppsala 9-11 October, 2017
Emerging Topics in Biological Networks and Systems Biology Symposium at the Swedish Collegium for Advanced Study (scas), Uppsala 9-11 October, 2017 mona singh, Princeton University, usa Network-based Methods for Identifying Cancer Genes Abstract: A central goal in cancer genomics is to identify the somatic alterations that underpin tumor initiation and progression. While commonly mutated cancer genes are readily identifiable, those that are rarely mutated across samples are difficult to distinguish from the large numbers of other infrequently mutated genes. Molecular interactions and networks provide a powerful frame- work with which to tackle some of the difficulties arising from the diverse somatic mutational landscapes of cancers. In this talk, I will first demonstrate that cancer genes can be discovered by identifying genes whose interaction interfaces are enriched in somatic mutations. Next, I will show how to leverage per-individual mutational profiles within the context of protein-protein interaction networks in order to identify small connected subnetworks of genes that, while not individually frequently mutated, comprise pathways that are altered across (i.e., “cover”) a large fraction of individuals. Overall, these two approaches recapitulate known cancer driver genes, and discover novel, and sometimes rarely-mutated, genes with likely roles in cancer. About: Mona Singh obtained her AB and SM degrees at Harvard University, and her PhD at MIT, all three in Computer Science. She did postdoctoral work at the Whitehead Institute for Biomedical Research. She has been on the faculty at Princeton since 1999, and currently she is Professor of Computer Science in the computer science department and the Lewis-Sigler Institute for Integrative Genomics. -
Curriculum Vitae
Curriculum Vitae Tandy Warnow Grainger Distinguished Chair in Engineering 1 Contact Information Department of Computer Science The University of Illinois at Urbana-Champaign Email: [email protected] Homepage: http://tandy.cs.illinois.edu 2 Research Interests Phylogenetic tree inference in biology and historical linguistics, multiple sequence alignment, metage- nomic analysis, big data, statistical inference, probabilistic analysis of algorithms, machine learning, combinatorial and graph-theoretic algorithms, and experimental performance studies of algorithms. 3 Professional Appointments • Co-chief scientist, C3.ai Digital Transformation Institute, 2020-present • Grainger Distinguished Chair in Engineering, 2020-present • Associate Head for Computer Science, 2019-present • Special advisor to the Head of the Department of Computer Science, 2016-present • Associate Head for the Department of Computer Science, 2017-2018. • Founder Professor of Computer Science, the University of Illinois at Urbana-Champaign, 2014- 2019 • Member, Carl R. Woese Institute for Genomic Biology. Affiliate of the National Center for Supercomputing Applications (NCSA), Coordinated Sciences Laboratory, and the Unit for Criticism and Interpretive Theory. Affiliate faculty member in the Departments of Mathe- matics, Electrical and Computer Engineering, Bioengineering, Statistics, Entomology, Plant Biology, and Evolution, Ecology, and Behavior, 2014-present. • National Science Foundation, Program Director for Big Data, July 2012-July 2013. • Member, Big Data Senior Steering Group of NITRD (The Networking and Information Tech- nology Research and Development Program), subcomittee of the National Technology Council (coordinating federal agencies), 2012-2013 • Departmental Scholar, Institute for Pure and Applied Mathematics, UCLA, Fall 2011 • Visiting Researcher, University of Maryland, Spring and Summer 2011. 1 • Visiting Researcher, Smithsonian Institute, Spring and Summer 2011. • Professeur Invit´e,Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Summer 2010. -
Structure-Based Realignment of Non-Coding Rnas in Multiple Whole Genome Alignments
Structure-based Realignment of Non-coding RNAs in Multiple Whole Genome Alignments. by Michael Ku Yu Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of ARCHIVES Masters of Engineering in Computer Science and Engineering MASSACHUE N U TE at the OF TECH IOLOY MASSACHUSETTS INSTITUTE OF TECHNOLOGY JUN 2 1 2011 June 2011 LIBRARI ES @ Massachusetts Institute of Technology 2011. All rights reserved. '$7 A uthor ............ .. .. ... ............. Department of Electrical Wgineering and Computer Science May 20, 2011 Certified by..................................... ...... Bonnie Berger Professor of Applied Mathematics and Computer Science Thesis Supervisor Accepted by.... ....................................... Christopher J. Terman Chairman, Department Committee on Graduate Theses 2 Structure-based Realignment of Non-coding RNAs in Multiple Whole Genome Alignments by Michael Ku Yu Submitted to the Department of Electrical Engineering and Computer Science on May 20, 2011, in partial fulfillment of the requirements for the degree of Masters of Engineering in Computer Science and Engineering Abstract Whole genome alignments have become a central tool in biological sequence analy- sis. A major application is the de novo prediction of non-coding RNAs (ncRNAs) from structural conservation visible in the alignment. However, current methods for constructing genome alignments do so by explicitly optimizing for sequence simi- larity but not structural similarity. Therefore, de novo prediction of ncRNAs with high structural but low sequence conservation is intrinsically challenging in a genome alignment because the conservation signal is typically hidden. This study addresses this problem with a method for genome-wide realignment of potential ncRNAs ac- cording to structural similarity.