1St INTERNATIONAL CONFERENCE on ALGORITHMS for COMPUTATIONAL BIOLOGY
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RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY
RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press ISSN: 1790-5125 www.wseas.org ISBN: 978-960-474-141-0 RECENT ADVANCES in BIOLOGY, BIOPHYSICS, BIOENGINEERING and COMPUTATIONAL CHEMISTRY Proceedings of the 5th WSEAS International Conference on CELLULAR and MOLECULAR BIOLOGY, BIOPHYSICS and BIOENGINEERING (BIO '09) Proceedings of the 3rd WSEAS International Conference on COMPUTATIONAL CHEMISTRY (COMPUCHEM '09) Puerto De La Cruz, Tenerife, Canary Islands, Spain December 14-16, 2009 Recent Advances in Biology and Biomedicine A Series of Reference Books and Textbooks Published by WSEAS Press www.wseas.org Copyright © 2009, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed -
Algorithms for Computational Biology 8Th International Conference, Alcob 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings
Lecture Notes in Bioinformatics 12715 Subseries of Lecture Notes in Computer Science Series Editors Sorin Istrail Brown University, Providence, RI, USA Pavel Pevzner University of California, San Diego, CA, USA Michael Waterman University of Southern California, Los Angeles, CA, USA Editorial Board Members Søren Brunak Technical University of Denmark, Kongens Lyngby, Denmark Mikhail S. Gelfand IITP, Research and Training Center on Bioinformatics, Moscow, Russia Thomas Lengauer Max Planck Institute for Informatics, Saarbrücken, Germany Satoru Miyano University of Tokyo, Tokyo, Japan Eugene Myers Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Marie-France Sagot Université Lyon 1, Villeurbanne, France David Sankoff University of Ottawa, Ottawa, Canada Ron Shamir Tel Aviv University, Ramat Aviv, Tel Aviv, Israel Terry Speed Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia Martin Vingron Max Planck Institute for Molecular Genetics, Berlin, Germany W. Eric Wong University of Texas at Dallas, Richardson, TX, USA More information about this subseries at http://www.springer.com/series/5381 Carlos Martín-Vide • Miguel A. Vega-Rodríguez • Travis Wheeler (Eds.) Algorithms for Computational Biology 8th International Conference, AlCoB 2021 Missoula, MT, USA, June 7–11, 2021 Proceedings 123 Editors Carlos Martín-Vide Miguel A. Vega-Rodríguez Rovira i Virgili University University of Extremadura Tarragona, Spain Cáceres, Spain Travis Wheeler University of Montana Missoula, MT, USA ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Bioinformatics ISBN 978-3-030-74431-1 ISBN 978-3-030-74432-8 (eBook) https://doi.org/10.1007/978-3-030-74432-8 LNCS Sublibrary: SL8 – Bioinformatics © Springer Nature Switzerland AG 2021 This work is subject to copyright. -
Computational Pan-Genomics: Status, Promises and Challenges
bioRxiv preprint doi: https://doi.org/10.1101/043430; this version posted March 12, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Computational Pan-Genomics: Status, Promises and Challenges Tobias Marschall1,2, Manja Marz3,60,61,62, Thomas Abeel49, Louis Dijkstra6,7, Bas E. Dutilh8,9,10, Ali Ghaffaari1,2, Paul Kersey11, Wigard P. Kloosterman12, Veli M¨akinen13, Adam Novak15, Benedict Paten15, David Porubsky16, Eric Rivals17,63, Can Alkan18, Jasmijn Baaijens5, Paul I. W. De Bakker12, Valentina Boeva19,64,65,66, Francesca Chiaromonte20, Rayan Chikhi21, Francesca D. Ciccarelli22, Robin Cijvat23, Erwin Datema24,25,26, Cornelia M. Van Duijn27, Evan E. Eichler28, Corinna Ernst29, Eleazar Eskin30,31, Erik Garrison32, Mohammed El-Kebir5,33,34, Gunnar W. Klau5, Jan O. Korbel11,35, Eric-Wubbo Lameijer36, Benjamin Langmead37, Marcel Martin59, Paul Medvedev38,39,40, John C. Mu41, Pieter Neerincx36, Klaasjan Ouwens42,67, Pierre Peterlongo43, Nadia Pisanti44,45, Sven Rahmann29, Ben Raphael46,47, Knut Reinert48, Dick de Ridder50, Jeroen de Ridder49, Matthias Schlesner51, Ole Schulz-Trieglaff52, Ashley Sanders53, Siavash Sheikhizadeh50, Carl Shneider54, Sandra Smit50, Daniel Valenzuela13, Jiayin Wang70,71,72, Lodewyk Wessels56, Ying Zhang23,5, Victor Guryev16,12, Fabio Vandin57,34, Kai Ye68,69,72 and Alexander Sch¨onhuth5 1Center for Bioinformatics, Saarland University, Saarbr¨ucken, Germany; 2Max Planck Institute for Informatics, Saarbr¨ucken, -
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. -
The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-Rnns and the Protein Structure Prediction Problem
Journal of Machine Learning Research 4 (2003) 575-602 Submitted 2/02; Revised 4/03; Published 9/03 The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-RNNs and the Protein Structure Prediction Problem Pierre Baldi [email protected] Gianluca Pollastri [email protected] School of Information and Computer Science Institute for Genomics and Bioinformatics University of California, Irvine Irvine, CA 92697-3425, USA Editor: Michael I. Jordan Abstract We describe a general methodology for the design of large-scale recursive neural network architec- tures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets of DAG connec- tions to capture stationarity and control model complexity. Here we use these principles to derive several specific classes of DAG-RNN architectures based on lattices, trees, and other structured graphs. These architectures can process a wide range of data structures with variable sizes and dimensions. While the overall resulting models remain probabilistic, the internal deterministic dy- namics allows efficient propagation of information, as well as training by gradient descent, in order to tackle large-scale problems. These methods are used here to derive state-of-the-art predictors for protein structural features such as secondary structure (1D) and both fine- and coarse-grained contact maps (2D). Extensions, relationships to graphical models, and implications for the design of neural architectures are briefly discussed. -
Classifying Transport Proteins Using Profile Hidden Markov Models And
Classifying Transport Proteins Using Profile Hidden Markov Models and Specificity Determining Sites Qing Ye A Thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements for the Degree of Master of Computer Science (MCompSc) at Concordia University Montréal, Québec, Canada April 2019 ⃝c Qing Ye, 2019 CONCORDIA UNIVERSITY School of Graduate Studies This is to certify that the thesis prepared By: Qing Ye Entitled: Classifying Transport Proteins Using Profile Hidden Markov Models and Specificity Determining Sites and submitted in partial fulfillment of the requirements for the degree of Master of Computer Science (MCompSc) complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Signed by the Final Examining Committee: Chair Dr. T.-H. Chen Examiner Dr. T. Glatard Examiner Dr. A. Krzyzak Supervisor Dr. G. Butler Approved by Martin D. Pugh, Chair Department of Computer Science and Software Engineering 2019 Amir Asif, Dean Faculty of Engineering and Computer Science Abstract Classifying Transport Proteins Using Profile Hidden Markov Models and Specificity Determining Sites Qing Ye This thesis develops methods to classifiy the substrates transported across a membrane by a given transmembrane protein. Our methods use tools that predict specificity determining sites (SDS) after computing a multiple sequence alignment (MSA), and then building a profile Hidden Markov Model (HMM) using HMMER. In bioinformatics, HMMER is a set of widely used applications for sequence analysis based on profile HMM. Specificity determining sites (SDS) are the key positions in a protein sequence that play a crucial role in functional variation within the protein family during the course of evolution. -
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. -
Methodology for Predicting Semantic Annotations of Protein Sequences by Feature Extraction Derived of Statistical Contact Potentials and Continuous Wavelet Transform
Universidad Nacional de Colombia Sede Manizales Master’s Thesis Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform Author: Supervisor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez A thesis submitted in fulfillment of the requirements for the degree of Master’s on Engineering - Industrial Automation in the Department of Electronic, Electric Engineering and Computation Signal Processing and Recognition Group June 2014 Universidad Nacional de Colombia Sede Manizales Tesis de Maestr´ıa Metodolog´ıapara predecir la anotaci´on sem´antica de prote´ınaspor medio de extracci´on de caracter´ısticas derivadas de potenciales de contacto y transformada wavelet continua Autor: Tutor: Gustavo Alonso Arango Dr. Cesar German Argoty Castellanos Dominguez Tesis presentada en cumplimiento a los requerimientos necesarios para obtener el grado de Maestr´ıaen Ingenier´ıaen Automatizaci´onIndustrial en el Departamento de Ingenier´ıaEl´ectrica,Electr´onicay Computaci´on Grupo de Procesamiento Digital de Senales Enero 2014 UNIVERSIDAD NACIONAL DE COLOMBIA Abstract Faculty of Engineering and Architecture Department of Electronic, Electric Engineering and Computation Master’s on Engineering - Industrial Automation Methodology for predicting semantic annotations of protein sequences by feature extraction derived of statistical contact potentials and continuous wavelet transform by Gustavo Alonso Arango Argoty In this thesis, a method to predict semantic annotations of the proteins from its primary structure is proposed. The main contribution of this thesis lies in the implementation of a novel protein feature representation, which makes use of the pairwise statistical contact potentials describing the protein interactions and geometry at the atomic level. -
Curriculum Vitae – Prof. Anders Krogh Personal Information
Curriculum Vitae – Prof. Anders Krogh Personal Information Date of Birth: May 2nd, 1959 Private Address: Borgmester Jensens Alle 22, st th, 2100 København Ø, Denmark Contact information: Dept. of Biology, Univ. of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen, Denmark. +45 3532 1329, [email protected] Web: https://scholar.google.com/citations?user=-vGMjmwAAAAJ Education Sept 1991 Ph.D. (Physics), Niels Bohr Institute, Univ. of Copenhagen, Denmark June 1987 Cand. Scient. [M. Sc.] (Physics and mathematics), NBI, Univ. of Copenhagen Professional / Work Experience (since 2000) 2018 – Professor of Bionformatics, Dept of Computer Science (50%) and Dept of Biology (50%), Univ. of Copenhagen 2002 – 2018 Professor of Bionformatics, Dept of Biology, Univ. of Copenhagen 2009 – 2018 Head of Section for Computational and RNA Biology, Dept. of Biology, Univ. of Copenhagen 2000–2002 Associate Prof., Technical Univ. of Denmark (DTU), Copenhagen Prices and Awards 2017 – Fellow of the International Society for Computational Biology https://www.iscb.org/iscb- fellows-program 2008 – Fellow, Royal Danish Academy of Sciences and Letters Public Activities & Appointments (since 2009) 2014 – Board member, Elixir, European Infrastructure for Life Science. 2014 – Steering committee member, Danish Elixir Node. 2012 – 2016 Board member, Bioinformatics Infrastructure for Life Sciences (BILS), Swedish Research Council 2011 – 2016 Director, Centre for Computational and Applied Transcriptomics (COAT) 2009 – Associate editor, BMC Bioinformatics Publications § Google Scholar: https://scholar.google.com/citations?user=-vGMjmwAAAAJ § ORCID: 0000-0002-5147-6282. ResearcherID: M-1541-2014 § Co-author of 130 peer-reviewed papers and 2 monographs § 63,000 citations and h-index of 74 (Google Scholar, June 2019) § H-index of 54 in Web of science (June 2019) § Publications in high-impact journals: Nature (5), Science (1), Cell (1), Nature Genetics (2), Nature Biotechnology (2), Nature Communications (4), Cell (1, to appear), Genome Res. -
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. -
Computational Methods Addressing Genetic Variation In
COMPUTATIONAL METHODS ADDRESSING GENETIC VARIATION IN NEXT-GENERATION SEQUENCING DATA by Charlotte A. Darby A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland June 2020 © 2020 Charlotte A. Darby All rights reserved Abstract Computational genomics involves the development and application of computational meth- ods for whole-genome-scale datasets to gain biological insight into the composition and func- tion of genomes, including how genetic variation mediates molecular phenotypes and disease. New biotechnologies such as next-generation sequencing generate genomic data on a massive scale and have transformed the field thanks to simultaneous advances in the analysis toolkit. In this thesis, I present three computational methods that use next-generation sequencing data, each of which addresses the genetic variations within and between human individuals in a different way. First, Samovar is a software tool for performing single-sample mosaic single-nucleotide variant calling on whole genome sequencing linked read data. Using haplotype assembly of heterozygous germline variants, uniquely made possible by linked reads, Samovar identifies variations in different cells that make up a bulk sequencing sample. We apply it to 13cancer samples in collaboration with researchers at Nationwide Childrens Hospital. Second, scHLAcount is a software pipeline that computes allele-specific molecule counts for the HLA genes from single-cell gene expression data. We use a personalized reference genome based on the individual’s genotypes to reveal allele-specific and cell type-specific gene expression patterns. Even given technology-specific biases of single-cell gene expression data, we can resolve allele-specific expression for these genes since the alleles are often quite different between the two haplotypes of an individual. -
Big Data, Moocs, and ... (PDF)
HHMI Constellation Studios for Science Education November 13-15, 2015 | HHMI Headquarters | Chevy Chase, MD Big Data, MOOCs, and Quantitative Education for Biologists Co-Chairs Pavel Pevzner, University of California- San Diego Sarah Elgin, Washington University Studio Objectives Discuss existing challenges in bioinformatics education with experts in computational biology and quantitative biology education, Evaluate best practices in teaching quantitative and computational biology, and Collaborate with scientist educators to develop instructional modules to support a biology curriculum that includes quantitative approaches. Friday | November 13 4:00 pm Arrival Registration Desk 5:30 – 6:00 pm Reception Great Hall 6:00 – 7:00 pm Dinner Dining Room 7:00 – 7:15 pm Welcome K202 David Asai, HHMI Cynthia Bauerle, HHMI Pavel Pevzner, University of California-San Diego Sarah Elgin, Washington University Alex Hartemink, Duke University 7:15 – 8:00 pm How to Maximize Interaction and Feedback During the Studio K202 Cynthia Bauerle and Sarah Simmons, HHMI 8:00 – 9:00 pm Keynote Presentation K202 "Computing + Biology = Discovery" Speakers: Ran Libeskind-Hadas, Harvey Mudd College Eliot Bush, Harvey Mudd College 9:00 – 11:00 pm Social The Pilot Saturday | November 14 7:30 – 8:15 am Breakfast Dining Room 8:30 – 10:00 am Lecture session 1 K202 Moderator: Pavel Pevzner 834a-854a “How is body fat regulated?” Laurie Heyer, Davidson College 856a-916a “How can we find mutations that cause cancer?” Ben Raphael, Brown University “How does a tumor evolve over time?” 918a-938a Russell Schwartz, Carnegie Mellon University “How fast do ribosomes move?” 940a-1000a Carl Kingsford, Carnegie Mellon University 10:05 – 10:55 am Breakout working groups Rooms: S221, (coffee available in each room) N238, N241, N140 1.