A General Sequence Processing and Analysis Program for Protein Engineering Ryan L
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Benchmarking of Bioperl, Perl, Biojava, Java, Biopython, and Python for Primitive Bioinformatics Tasks 6 and Choosing a Suitable Language
Taewan Ryu : Benchmarking of BioPerl, Perl, BioJava, Java, BioPython, and Python for Primitive Bioinformatics Tasks 6 and Choosing a Suitable Language Benchmarking of BioPerl, Perl, BioJava, Java, BioPython, and Python for Primitive Bioinformatics Tasks and Choosing a Suitable Language Taewan Ryu Dept of Computer Science, California State University, Fullerton, CA 92834, USA ABSTRACT Recently many different programming languages have emerged for the development of bioinformatics applications. In addition to the traditional languages, languages from open source projects such as BioPerl, BioPython, and BioJava have become popular because they provide special tools for biological data processing and are easy to use. However, it is not well-studied which of these programming languages will be most suitable for a given bioinformatics task and which factors should be considered in choosing a language for a project. Like many other application projects, bioinformatics projects also require various types of tasks. Accordingly, it will be a challenge to characterize all the aspects of a project in order to choose a language. However, most projects require some common and primitive tasks such as file I/O, text processing, and basic computation for counting, translation, statistics, etc. This paper presents the benchmarking results of six popular languages, Perl, BioPerl, Python, BioPython, Java, and BioJava, for several common and simple bioinformatics tasks. The experimental results of each language are compared through quantitative evaluation metrics such as execution time, memory usage, and size of the source code. Other qualitative factors, including writeability, readability, portability, scalability, and maintainability, that affect the success of a project are also discussed. The results of this research can be useful for developers in choosing an appropriate language for the development of bioinformatics applications. -
Contributions to Biostatistics: Categorical Data Analysis, Data Modeling and Statistical Inference Mathieu Emily
Contributions to biostatistics: categorical data analysis, data modeling and statistical inference Mathieu Emily To cite this version: Mathieu Emily. Contributions to biostatistics: categorical data analysis, data modeling and statistical inference. Mathematics [math]. Université de Rennes 1, 2016. tel-01439264 HAL Id: tel-01439264 https://hal.archives-ouvertes.fr/tel-01439264 Submitted on 18 Jan 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. HABILITATION À DIRIGER DES RECHERCHES Université de Rennes 1 Contributions to biostatistics: categorical data analysis, data modeling and statistical inference Mathieu Emily November 15th, 2016 Jury: Christophe Ambroise Professeur, Université d’Evry Val d’Essonne, France Rapporteur Gérard Biau Professeur, Université Pierre et Marie Curie, France Président David Causeur Professeur, Agrocampus Ouest, France Examinateur Heather Cordell Professor, University of Newcastle upon Tyne, United Kingdom Rapporteur Jean-Michel Marin Professeur, Université de Montpellier, France Rapporteur Valérie Monbet Professeur, Université de Rennes 1, France Examinateur Korbinian Strimmer Professor, Imperial College London, United Kingdom Examinateur Jean-Philippe Vert Directeur de recherche, Mines ParisTech, France Examinateur Pour M.H.A.M. Remerciements En tout premier lieu je tiens à adresser mes remerciements à l’ensemble des membres du jury qui m’ont fait l’honneur d’évaluer mes travaux. -
The Bioperl Toolkit: Perl Modules for the Life Sciences
Downloaded from genome.cshlp.org on January 25, 2012 - Published by Cold Spring Harbor Laboratory Press The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich, David Block, Kris Boulez, et al. Genome Res. 2002 12: 1611-1618 Access the most recent version at doi:10.1101/gr.361602 Supplemental http://genome.cshlp.org/content/suppl/2002/10/20/12.10.1611.DC1.html Material References This article cites 14 articles, 9 of which can be accessed free at: http://genome.cshlp.org/content/12/10/1611.full.html#ref-list-1 Article cited in: http://genome.cshlp.org/content/12/10/1611.full.html#related-urls Email alerting Receive free email alerts when new articles cite this article - sign up in the box at the service top right corner of the article or click here To subscribe to Genome Research go to: http://genome.cshlp.org/subscriptions Cold Spring Harbor Laboratory Press Downloaded from genome.cshlp.org on January 25, 2012 - Published by Cold Spring Harbor Laboratory Press Resource The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich,1,18,19 David Block,2,18 Kris Boulez,3 Steven E. Brenner,4 Stephen A. Chervitz,5 Chris Dagdigian,6 Georg Fuellen,7 James G.R. Gilbert,8 Ian Korf,9 Hilmar Lapp,10 Heikki Lehva¨slaiho,11 Chad Matsalla,12 Chris J. Mungall,13 Brian I. Osborne,14 Matthew R. Pocock,8 Peter Schattner,15 Martin Senger,11 Lincoln D. Stein,16 Elia Stupka,17 Mark D. Wilkinson,2 and Ewan Birney11 1University Program in Genetics, Duke University, Durham, North Carolina 27710, USA; 2National Research Council of -
Sequence Analysis Instructions
Sequence Analysis Instructions In order to predict your drug metabolizing phenotype from your CYP2D6 gene sequence, you must determine: 1) The assembled sequence from your two opposing sequencing reactions 2) If your PCR product even represents the human CYP2D6 gene, 3) The location of your sequence within the CYP2D6 gene, 4) Whether differences between your alleles and CYP2D6*1 sequence represents sequencing errors or polymorphisms in your sequence, and 5) The effect(s) of any polymorphisms on CYP2D6 protein sequence. As you analyze your gene sequence, copy and paste your analyses and results into a text file for your final lab report. If some factor, like the quality of your sequence, prevents you from carrying out the complete analysis, your grade will not be penalized—just complete as many of the steps below as possible, and include an explanation of why you could not complete the analysis in your final report. Font appearing in bold green italics describes questions to answer and items to include in your final report. Part 1: Assembling your CYP2D6 sequence from both directions The sequencing reaction only produces 700-800 bases of good sequence. To get most of the sequence of the 1.2 Kbp PCR product, sequencing reactions were performed from both directions on the PCR product. These need to be assembled into one sequence using the overlap of the two sequences. In order to assure that it is going in the forward direction, it is best to derive the reverse complement sequence from the reverse sequencing file. Take the text file and paste it into a program like http://www.bioinformatics.org/sms/rev_comp.html. -
Agenda: Gene Prediction by Cross-Species Sequence Comparison Leila Taher1, Oliver Rinner2,3, Saurabh Garg1, Alexander Sczyrba4 and Burkhard Morgenstern5,*
Nucleic Acids Research, 2004, Vol. 32, Web Server issue W305–W308 DOI: 10.1093/nar/gkh386 AGenDA: gene prediction by cross-species sequence comparison Leila Taher1, Oliver Rinner2,3, Saurabh Garg1, Alexander Sczyrba4 and Burkhard Morgenstern5,* 1International Graduate School for Bioinformatics and Genome Research, University of Bielefeld, Postfach 10 01 31, 33501 Bielefeld, Germany, 2GSF Research Center, MIPS/Institute of Bioinformatics, Ingolsta¨dter Landstrasse 1, 85764 Neuherberg, Germany, 3Brain Research Institute, ETH Zuurich,€ Winterthurerstrasse 190, CH-8057 Zuurich,€ Switzerland, 4Faculty of Technology, Research Group in Practical Computer Science, University of Bielefeld, Postfach 10 01 31, 33501 Bielefeld, Germany and 5Institute of Microbiology and Genetics, University of Go¨ttingen, Goldschmidtstrasse 1, 37077 Go¨ttingen, Germany Received March 5, 2004; Revised and Accepted March 24, 2004 ABSTRACT Thus, with the huge amount of data produced by sequencing projects, the development of high-quality gene-prediction Automatic gene prediction is one of the major tools has become a priority in bioinformatics. For eukaryotic challenges in computational sequence analysis. organisms, gene prediction is particularly challenging, since Traditional approaches to gene finding rely on statis- protein-coding exons are usually separated by introns of vari- tical models derived from previously known genes. By able length. Most traditional methods for gene prediction are contrast, a new class of comparative methods relies intrinsic methods; they use hidden Markov models (HMMs) or on comparing genomic sequences from evolutionary other stochastic approaches describing the statistical composi- related organisms to each other. These methods are tion of introns, exons, etc. Such models are trained with already based on the concept of phylogenetic footprinting: known genes from the same or a closely related species; the they exploit the fact that functionally important most popular of these tools is GenScan (1). -
Review of Java
Review of Java z Classes are object factories ¾ Encapsulate state/data and behavior/methods ¾ Ask not what you can do to an object, but what … z A program is created by using classes in libraries provided and combining these with classes you design/implement ¾ Design classes, write methods, classes communicate ¾ Communication is via method call z We've concentrated on control within and between methods ¾ Data types: primitive, array, String ¾ Control: if, for-loop, while-loop, return Genome Revolution: COMPSCI 006G 3.1 Smallest of 2, 3, …,n z We want to print the lesser of two elements, e.g., comparing the lengths of two DNA strands int small = Math.min(s1.length(),s2.length()); z Where does min function live? How do we access it? ¾ Could we write this ourselves? Why use library method? public class Math { public static int min(int x, int y) { if (x < y) return x; else return y; } } Genome Revolution: COMPSCI 006G 3.2 Generalize from two to three z Find the smallest of three strand lengths: s1, s2, s3 int small = … z Choices in writing code? ¾ Write sequence of if statements ¾ Call library method ¾ Advantages? Disadvantages? Genome Revolution: COMPSCI 006G 3.3 Generalize from three to N z Find the smallest strand length of N (any number) in array public int smallest(String[] dnaCollection) { // return shortest length in dnaCollection } z How do we write this code? Where do we start? ¾ ¾ ¾ Genome Revolution: COMPSCI 006G 3.4 Static methods analyzed z Typically a method invokes behavior on an object ¾ Returns property of object, e.g., s.length(); -
Plat: a Web Based Protein Local Alignment Tool
University of Rhode Island DigitalCommons@URI Open Access Master's Theses 2017 Plat: A Web Based Protein Local Alignment Tool Stephen H. Jaegle University of Rhode Island, [email protected] Follow this and additional works at: https://digitalcommons.uri.edu/theses Recommended Citation Jaegle, Stephen H., "Plat: A Web Based Protein Local Alignment Tool" (2017). Open Access Master's Theses. Paper 1080. https://digitalcommons.uri.edu/theses/1080 This Thesis is brought to you for free and open access by DigitalCommons@URI. It has been accepted for inclusion in Open Access Master's Theses by an authorized administrator of DigitalCommons@URI. For more information, please contact [email protected]. PLAT: A WEB BASED PROTEIN LOCAL ALIGNMENT TOOL BY STEPHEN H. JAEGLE A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE UNIVERSITY OF RHODE ISLAND 2017 MASTER OF SCIENCE THESIS OF STEPHEN H. JAEGLE APPROVED: Thesis Committee: Major Professor Lutz Hamel Victor Fay-Wolfe Ying Zhang Nasser H. Zawia DEAN OF THE GRADUATE SCHOOL UNIVERSITY OF RHODE ISLAND 2017 ABSTRACT Protein structure largely determines functionality; three-dimensional struc- tural alignment is thus important to analysis and prediction of protein function. Protein Local Alignment Tool (PLAT) is an implementation of a web-based tool with a graphic interface that performs local protein structure alignment based on user-selected amino acids. Global alignment compares entire structures; local alignment compares parts of structures. Given input from the user and the RCSB Protein Data Bank, PLAT determines an optimal translation and rotation that minimizes the distance between the structures defined by the selected inputs. -
Software List for Biology, Bioinformatics and Biostatistics CCT
Software List for biology, bioinformatics and biostatistics v CCT - Delta Software Version Application short read assembler and it works on both small and large (mammalian size) ALLPATHS-LG 52488 genomes provides a fast, flexible C++ API & toolkit for reading, writing, and manipulating BAMtools 2.4.0 BAM files a high level of alignment fidelity and is comparable to other mainstream Barracuda 0.7.107b alignment programs allows one to intersect, merge, count, complement, and shuffle genomic bedtools 2.25.0 intervals from multiple files Bfast 0.7.0a universal DNA sequence aligner tool analysis and comprehension of high-throughput genomic data using the R Bioconductor 3.2 statistical programming BioPython 1.66 tools for biological computation written in Python a fast approach to detecting gene-gene interactions in genome-wide case- Boost 1.54.0 control studies short read aligner geared toward quickly aligning large sets of short DNA Bowtie 1.1.2 sequences to large genomes Bowtie2 2.2.6 Bowtie + fully supports gapped alignment with affine gap penalties BWA 0.7.12 mapping low-divergent sequences against a large reference genome ClustalW 2.1 multiple sequence alignment program to align DNA and protein sequences assembles transcripts, estimates their abundances for differential expression Cufflinks 2.2.1 and regulation in RNA-Seq samples EBSEQ (R) 1.10.0 identifying genes and isoforms differentially expressed EMBOSS 6.5.7 a comprehensive set of sequence analysis programs FASTA 36.3.8b a DNA and protein sequence alignment software package FastQC -
A Comparison of Latent Class and Sequence Analysis
Classifying life course trajectories: a comparison of latent class and sequence analysis Nicola Barban DONDENA \Carlo F. Dondena" Centre for Research on Social Dynamics, Universit`aBocconi, Milan, Italy [email protected] Francesco C. Billari DONDENA \Carlo F. Dondena" Centre for Research on Social Dynamics, Department of Decision Sciences and IGIER Universit`aBocconi, Milan, Italy Draft version. Abstract In this article we compare two techniques that are widely used in the analysis of life course trajectories, i.e. latent class analysis (LCA) and sequence analysis (SA). In particular, we focus on the use of these techniques as devices to obtain classes of individual life course trajectories. We first compare the consistency of the clas- sification obtained via the two techniques using an actual dataset on the life course trajectories of young adults. Then, we adopt a simulation approach to measure the ability of these two methods to correctly classify groups of life course trajecto- ries when specific forms of \random" variability are introduced within pre-specified classes in an artificial datasets. In order to do so, we introduce simulation opera- tors that have a life course and/or observational meaning. Our results contribute on the one hand to outline the usefulness and robustness of findings based on the classification of life course trajectories through LCA and SA, on the other hand to illuminate on the potential pitfalls of actual applications of these techniques. 1 Introduction In recent years, there has been a significantly growing interest in the holistic study of life course trajectories, i.e. in considering whole trajectories as a unit of analysis, both in a social science setting and in epidemiological and medical studies. -
The Bioperl Toolkit: Perl Modules for the Life Sciences
Downloaded from genome.cshlp.org on October 30, 2013 - Published by Cold Spring Harbor Laboratory Press View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Cold Spring Harbor Laboratory Institutional Repository The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich, David Block, Kris Boulez, et al. Genome Res. 2002 12: 1611-1618 Access the most recent version at doi:10.1101/gr.361602 Supplemental http://genome.cshlp.org/content/suppl/2002/10/20/12.10.1611.DC1.html Material References This article cites 14 articles, 9 of which can be accessed free at: http://genome.cshlp.org/content/12/10/1611.full.html#ref-list-1 Creative This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the Commons first six months after the full-issue publication date (see License http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as described at http://creativecommons.org/licenses/by-nc/3.0/. Email Alerting Receive free email alerts when new articles cite this article - sign up in the box at the Service top right corner of the article or click here. To subscribe to Genome Research go to: http://genome.cshlp.org/subscriptions Cold Spring Harbor Laboratory Press Resource The Bioperl Toolkit: Perl Modules for the Life Sciences Jason E. Stajich,1,18,19 David Block,2,18 Kris Boulez,3 Steven E. Brenner,4 Stephen A. Chervitz,5 Chris Dagdigian,6 Georg Fuellen,7 James G.R. -
The Biojava Tutorial the Biojava Tutorial
The BioJava Tutorial The BioJava Tutorial BioJava is a library of open source classes intended as a framework for applications which analyse or present biological sequence data. This tutorial illustrates the core sequence-handling interfaces available to the application programmer, and explains how BioJava differs from other sequence-handling libraries. For more comprehensive descriptions of the BioJava API, please consult the JavaDoc documentation. 1. Symbols and SymbolLists 2. Sequences and Features 3. Sequence I/O basics 4. ChangeEvent overview 5. ChangeEvent example using Distribution objects 6. Implementing Changeable 7. Blast-like parsing (NCBI Blast, WU-Blast, HMMER) 8. walkthrough of one of the dynamic programming examples 9. Installing BioSQL The BioJava tutorial, like BioJava itself, is a work in progress, and all suggestions (and offers to write extra chapters ;) are welcome. If you see any glaring errors, or would like to contribute some documentation, please contact Thomas Down or the biojava-l mailing list. http://www.biojava.org/tutorials/index.html [02/04/2003 13.39.36] BioJava.org - Main Page BioJava.org Open Bio sites About BioJava bioperl.org The BioJava Project is an open-source project dedicated to providing Java tools biopython.org for processing biological data. This will include objects for manipulating bioxml.org sequences, file parsers, CORBA interoperability, DAS, access to ACeDB, biodas.org dynamic programming, and simple statistical routines to name just a few things. biocorba.org The BioJava library is useful for automating those daily and mundane bioinformatics tasks. As the library matures, the BioJava libraries will provide a Documentation foundation upon which both free software and commercial packages can be Overview developed. -
Bioinformatics: a Practical Guide to the Analysis of Genes and Proteins, Second Edition Andreas D
BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto BIOINFORMATICS SECOND EDITION METHODS OF BIOCHEMICAL ANALYSIS Volume 43 BIOINFORMATICS A Practical Guide to the Analysis of Genes and Proteins SECOND EDITION Andreas D. Baxevanis Genome Technology Branch National Human Genome Research Institute National Institutes of Health Bethesda, Maryland USA B. F. Francis Ouellette Centre for Molecular Medicine and Therapeutics Children’s and Women’s Health Centre of British Columbia University of British Columbia Vancouver, British Columbia Canada A JOHN WILEY & SONS, INC., PUBLICATION New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or ALL CAPITAL LETTERS. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright ᭧ 2001 by John Wiley & Sons, Inc. 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 or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher.