bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Overview of Gene Identification: Approaches, Strategies, and Considerations
Authors: Baxevanis Andreas D. 
Published: June 2004

AbstractModern biology is on the verge of officially ushering in a new era in science with the completion of the sequencing of the human genome in April 2003. While often erroneously called the “post‐genome era”, this will actually truly mark the beginning of the “genome era,” a time in which the availability of sequence data for many genomes will have a significant effect on how science is performed in the 21st century. This unit offers an overview of many of the gene prediction methods that are currently available and offers a general assessment of how well the methods work for various problems.
Using MZEF to Find Internal Coding Exons
Authors: Zhang Micheal Q. 
Published: June 2004

AbstractMZEF (Michael Zhang's Exon Finder) was designed to help identify one of the most important classes of exons, i.e. the internal coding exons, in human genomic DNA sequences. It is neither for predicting intronless genes, nor for assembling predicted exons into complete gene models. There is also a mouse version (mMZEF) and an Arabidopsis version (aMZEF). This unit presents the Unix and Web versions of MZEF and reviews how to interpret the MZEF results.
Using geneid to Identify Genes
Authors: Blanco Enrique , Parra Genís , Guigó Roderic 
Published: June 2004

AbstractThe unit describes the usage of geneid. geneid is a very efficient gene‐finding program. It allows for the analysis of large genomic sequences, including whole mammalian chromosomes. These sequences can be partially annotated, and geneid can be used to refine this initial annotation. Parameter configurations exist for a number of eukaryotic species. Geneid produces output in a variety of standard formats. The results, thus, can be processed by a variety of software tools, including visualization programs. Geneid software is in the public domain, and it is undergoing constant development. It is easy to install and use. Exhaustive benchmark evaluations show that geneid compares favorably with other existing gene finding tools.
Using GlimmerM to Find Genes in Eukaryotic Genomes
Authors: Pertea Mihaela , Salzberg Steven L. 
Published: June 2004

AbstractGlimmerM is a eukaryotic gene finder that has been used in the annotation of the genomes of Plasmodium falciparum (the malaria parasite), the model plant Arabidopsis thaliana, Oryza sativa (rice), the parasite Theileria parva, and the fungus Aspergillus fumigatus. A unique feature of the system compared to other eukaryotic gene finders is a module that allows users to provide their own data and train GlimmerM for any organism.
Prokaryotic Gene Prediction Using GeneMark and GeneMark.hmm
Authors: Borodovsky Mark , Mills Ryan , Besemer John , Lomsadze Alex 
Published: June 2004

AbstractIn this unit, the GeneMark and GeneMark.hmm programs are presented as two different methods for the in silico prediction of genes in prokaryotes. GeneMark can be used for whole genome analysis as well as for the local analysis of a particular gene and its surrounding regions. GeneMark.hmm makes use of Hidden Markov models to find the transition points (boundaries) between protein coding states and noncoding states and can be efficiently used for larger genome sequences. These methods can be used in conjunction with each other for a higher sensitivity of gene detection.
Eukaryotic Gene Prediction Using GeneMark.hmm
Authors: Borodovsky Mark , Lomsadze Alex , Ivanov Nikolai , Mills Ryan 
Published: June 2004

AbstractIn this unit, eukaryotic GeneMark.hmm is presented as a method for detecting genes in eukaryotic DNA sequences. The eukaryotic GeneMark.hmm uses Markov models of protein coding and noncoding sequences, as well as positional nucleotide frequency matrices for prediction of the translational start, translational termination and splice sites. All these models along with length distributions of exons, introns and intergenic regions are integrated into one Hidden Markov model. The unit describes running the program over the Internet and locally on a Unix machine. It also discusses GeneMarkS EV, which can be used to detect genes in eukaryotic viruses.
Application of FirstEF to Find Promoters and First Exons in the Human Genome
Authors: Davuluri Ramana V. 
Published: June 2004

AbstractPredicting first exons and promoters is an important part of gene finding in DNA sequence analysis. This unit presents FirstEF as a method for predicting the first exons and promoters. A combines FirstEF predictions with other information such as cDNA/EST matches.
GrailEXP and Genome Analysis Pipeline for Genome Annotation
Authors: Uberbacher Edward C. , Hyatt Doug , Shah Manesh 
Published: June 2004

AbstractThe Basic Protocol describes the use of GrailEXP, the latest version of the gene finding system from Oak Ridge National Laboratory. GrailEXP provides gene models, by making use of sequence similarity with Expressed Sequence Tags (ESTs) and known genes. GrailEXP also provides alternatively spliced constructs for each gene based on the available EST evidence. The Support Protocol describes the use of the Genome Analysis Pipeline, a web application which allows users to perform comprehensive sequence analysis by offering a selection from a wide choice of supported gene finders, other biological feature finders, and database searches.
Using RepeatMasker to Identify Repetitive Elements in Genomic Sequences
Authors: Chen Nansheng 
Published: June 2004

AbstractThe RepeatMasker program is used for identifying repetitive elements in nucleotide sequences for further detailed analyses. Users can run RepeatMasker remotely via a Web site, or, for larger input sequences, the program and its dependent programs may be downloaded and run locally on Unix/Linux computers. The protocols in this chapter detail how to use RepeatMasker both remotely and locally to extract repetitive elements data and mask these repetitive elements in nucleotide sequences.
Introduction to Inferring Evolutionary Relationships
Authors: Page Roderic D.M. 
Published: June 2004

AbstractThis unit provides a general introduction to phylogeny. It defines common terms and discusses the issue of rooting trees, in addition to comparing gene and species trees. Methods for inferring phylogenies, such as distance methods, parsimony methods, and maximum likelihood are also presented. The unit concludes with discussion of how to assess tree confidence.
Visualizing Phylogenetic Trees Using TreeView
Authors: Page Roderic D.M. 
Published: June 2004

AbstractTreeView provides a simple way to view the phylogenetic trees produced by a range of programs, such as PAUP*, PHYLIP, TREE‐PUZZLE, and ClustalX. While some phylogenetic programs (such as the Macintosh version of PAUP*) have excellent tree printing facilities, many programs do not have the ability to generate publication quality trees. TreeView addresses this need. The program can read and write a range of tree file formats, display trees in a variety of styles, print trees, and save the tree as a graphic file. Protocols in this unit cover both displaying and printing a tree. Support protocols describe how to download and install TreeView, and how to display bootstrap values in trees generated by ClustalX and PAUP*.
Getting a Tree Fast: Neighbor Joining and Distance‐Based Methods
Authors: Gascuel Olivier 
Published: June 2004

AbstractThis unit provides instructions on how to construct a phylogenetic tree with several distance‐based methods. Neighbor Joining (NJ) and other distance‐based approaches (e.g., BIONJ, WEIGHBOR and FITCH) are fast methods for building phylogenetic trees. This makes them particularly effective for large‐scale studies or for bootstrap applications, which require runs on multiple data sets. Like maximum‐likelihood methods, distance methods are based on a sequence evolution model that is used to estimate the distance matrix. Computer simulations indicate that the topological accuracy of BIONJ, WEIGHBOR and FITCH is significantly better than that of NJ, and that the best distance‐based methods are equivalent to parsimony in most cases.
Maximum‐Likelihood Analysis Using TREE‐PUZZLE
Authors: Schmidt Heiko A. , von Haeseler Arndt 
Published: June 2004

AbstractTREE‐PUZZLE provides means to analyze and reconstruct evolutionary relationships and trees based on quartets, i.e. groups of 4 sequences. explains how to reconstruct trees based on the maximum‐likelihood principle and quartet puzzling. discusses likelihood mapping, a method to visualize phylogenetic content in a multiple sequence alignment. explains how to compare tree topologies using different tests.
What If I Don't Have a Tree?: Split Decomposition and Related Models
Authors: Huson Daniel H. 
Published: June 2004

AbstractA set of aligned character sequences or a matrix of evolutionary distances often contains a number of different and sometimes conflicting phylogenetic signals, and thus does not always support a unique tree. The method of split decomposition addresses this problem. For ideal data, this method gives rise to a phylogenetic tree, whereas less ideal data are represented by a tree‐like network that may indicate evidence of different and conflicting phylogenies. The SplitsTree program, described here, implements this approach and can be used to compute and visualize phylogenetic networks called splits graphs. It also implements a number of distance transformations, the computation of parsimony splits, spectral analysis and bootstrapping.
Using PEBBLE for the Evolutionary Analysis of Serially Sampled Molecular Sequences
Authors: Goode Matthew , Rodrigo Allen G. 
Published: June 2004

AbstractThe PEBBLE (Phylogenetics, Evolutionary Biology, and Bioinformatics in a moduLar Environment) application is a relative newcomer to the field of phylogenetic applications. Although designed as a customizable generalist application, PEBBLE was initially developed to implement procedures for the analysis of sequences associated with different sampling times, e.g., rapidly evolving viral genes sampled over the course of infection, or ancient DNA sequences. The basic protocol describes the use of PEBBLE to infer a phylogenetic tree using the sUPGMA algorithm, and the inference of substitution rate parameters using maximum likelihood. The alternate and support protocols describe the simulation capabilities of PEBBLE, and general use of the PEBBLE application, respectively.
The Gene Ontology (GO) Project: Structured Vocabularies for Molecular Biology and Their Application to Genome and Expression Analysis
Authors: Blake Judith A. , Harris Midori A. 
Published: June 2004

AbstractScientists wishing to utilize genomic data have quickly come to realize the benefit of standardizing descriptions of experimental procedures and results for computer‐driven information retrieval systems. The focus of the Gene Ontology project is three‐fold. First, the project goal is to compile the Gene Ontologies; structured vocabularies describing domains of molecular biology. Second, the project supports the use of these structured vocabularies in the annotation of gene products. Third, the gene product‐to‐GO annotation sets are provided by participating groups to the public through open access to the GO database and Web resource. This unit describes the current ontologies and what is beyond the scope of the Gene Ontology project. It addresses the issue of how GO vocabularies are constructed and related to genes and gene products. It concludes with a discussion of how researchers can access, browse, and utilize the GO project in the course of their own research.
Analysis of Gene‐Expression Data Using J‐Express
Authors: Jonassen Inge , Dysvik Bjarte 
Published: June 2004

AbstractThe J‐Express package has been designed to facilitate the analysis of microarray data with an emphasis on efficiency, usability, and comprehensibility. The J‐Express system provides a powerful and integrated platform for the analysis of microarray gene expression data. It is platform independent in that it requires only the availability of a Java virtual machine on the system. The system includes a range of analysis tools and a project management system supporting the organization and documentation of an analysis project. This unit describes the J‐Express tool emphasizing central concepts and principles, and gives examples of how it can be used to explore gene expression data sets.
DRAGON and DRAGON View: Information Annotation and Visualization Tools for Large‐Scale Expression Data
Authors: Bouton Christopher M.L.S. , Pevsner Jonathan 
Published: June 2004

AbstractThe Database Referencing of Array Genes ONline (DRAGON) database system consists of information derived from publicly available databases including UniGene, SWISS‐Prot, Pfam, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Through a Web‐accessible interface, the DRAGON Annotate tool rapidly supplies information pertaining to a range of biological characteristics of all the genes in any large‐scale gene expression data set. The subsequent inclusion of this information during data analysis and visualization allows for deeper insight into gene expression patterns. The set of DRAGON View tools provides methods for the analysis and visualization of expression patterns in relation to annotated information. Instead of incorporating the standard set of clustering and graphing tools available in many large‐scale expression data analysis software packages, DRAGON View has been specifically designed to allow for the analysis of expression data in relation to the biological characteristics of gene sets.
Using GenMAPP and MAPPFinder to View Microarray Data on Biological Pathways and Identify Global Trends in the Data
Authors: Dahlquist Kam D. 
Published: June 2004

AbstractGenMAPP (Gene MicroArray Pathway Profiler) is a free, stand‐alone computer program designed for viewing and analyzing gene expression data on MAPPs representing biological pathways or any other functional grouping of genes. A MAPP is a special file format produced with the graphics tools in GenMAPP that depicts the biological relationship between genes or gene products. When a MAPP is linked to an expression dataset, GenMAPP automatically and dynamically color‐codes the genes on the MAPP according to criteria supplied by the user. MAPPFinder is an accessory program that works with GenMAPP and the annotations from the Gene Ontology (GO) Consortium to identify global biological trends in gene expression data. MAPPFinder relates the microarray dataset to the GO hierarchy and calculates a percentage and statistical score for genes meeting the user's criterion for a meaningful gene expression change for each GO biological process, cellular component, and molecular function term.
Integrating Whole‐Genome Expression Results into Metabolic Networks with Pathway Processor
Authors: Cavalieri Duccio , Grosu Paul 
Published: June 2004

AbstractGenes never act alone in a biological system, but participate in a cascade of networks. As a result, analyzing microarray data from a pathway perspective leads to a new level of understanding the system. The authors' group has recently developed Pathway Processor (http://cgr.harvard.edu/cavalieri/pp.html), an automatic statistical method to determine which pathways are most affected by transcriptional changes and to map expression data from multiple whole‐genome expression experiments on metabolic pathways. This unit presents applications of the Pathway Processor software.
Prediction of Protein‐Protein Interaction Networks
Authors: Gomez Shawn M. 
Published: June 2004

AbstractThis unit offers a general overview of several techniques that have been developed for inferring functional and/or protein‐protein interaction networks. The majority of these use whole‐genome sequences as their primary input source of data. In addition, a few methods that utilize both protein features and experimental protein‐protein interaction data directly in the prediction of new interactions have recently been developed. As a result, a slightly more detailed description of one such method is also provided. While not an exhaustive list of approaches, it is hoped that the reader will gain a sense of how these approaches are implemented, an idea of their relative strengths and weaknesses, as well as some perspective on the type of work being conducted in this highly active area of research.
Evaluation of Electrostatic Interactions
Authors: Green David F. , Tidor Bruce 
Published: June 2004

AbstractDescribed here are several computational procedures for the analysis of electrostatic interactions in molecular complexes, all based on a continuum model of solvation. The first section describes how to compute the residual potential, a measure of how electrostatically complementary a ligand is for its receptor. The second procedure describes electrostatic component analysis, a method by which the electrostatic contribution to the binding free energy can be broken up into terms directly attributable to individual chemical groups. Finally, electrostatic affinity optimization is described. This procedure is particularly useful in determining what portions of a ligand are the most suboptimal, and thus provide the greatest opportunity for the design of improvements.
Using DelPhi to Compute Electrostatic Potentials and Assess Their Contribution to Interactions
Authors: Oron Assaf , Wolfson Haim , Gunasekaran Kannan , Nussinov Ruth 
Published: June 2004

AbstractThere is a general agreement that electrostatic interactions play a significant role in the structure and function of biological molecules. However, obtaining quantitative estimation of the electrostatic energy requires computational models that capture the microscopic nature of the heterogeneous environment of macromolecules. This protocol offers elaboration on one of the common methods to calculate the electrostatic energetic contributions using continuum electrostatics. The method involves solving the Poisson‐Boltzmann (PB) equation numerically and regarding the solute as having a homogenous dielectric constant. In order to apply this method, a three dimensional structure of the molecule derived from experimental data (crystallography, NMR) or modeling techniques is required. The protocol will focus on the DelPhi program (Accelrys Inc. San Diego), which is one of the most common programs used for the estimation of electrostatic free energy contribution. A simple procedure of assigning criteria and parameters (charge distribution, solvent and solute dielectric constants, iterations, grid resolution, etc) enables one to illustrate an electrostatic potential map and estimate the electrostatic free energy, although with limited accuracy.
Searching the MINT Database for Protein Interaction Information
Authors: Cesareni Gianni , Helmer‐Citterich Manuela 
Published: June 2004

AbstractThe Molecular Interactions Database (MINT) is a relational database designed to store information about protein interactions. Expert curators extract the relevant information from the scientific literature and deposit it in a computer readable form. Currently (April 2003) MINT contains information on 4300 proteins and >3000 interactions. Although MINT contains interactions among proteins for several different species, the curators have initially focused on protein interactions in mammalian species. Protocols are provided here for searching MINT over the Internet, using the MINT Viewer.
Creating Databases for Biological Information: An Introduction
Authors: Stein Lincoln 
Published: June 2004

AbstractThe essence of bioinformatics is dealing with large quantities of information. Whether it be sequencing data, microarray data files, mass spectrometric data (e.g., fingerprints), the catalog of strains arising from an insertional mutagenesis project, or even large numbers of PDF files, there inevitably comes a time when the information can simply no longer be managed with files and directories. This is where databases come into play. This unit briefly reviews the characteristics of several database management systems, including flat file, indexed file, and relational databases, as well as ACeDB. It compares their strengths and weaknesses and offers some general guidelines for selecting an appropriate database management system.
Structured Query Language (SQL) Fundamentals
Authors: Jamison D. Curtis 
Published: June 2004

AbstractRelational databases provide the most common platform for storing data. The Structured Query Language (SQL) is a powerful tool for interacting with relational database systems. SQL enables the user to concoct complex and powerful queries in a straightforward manner, allowing sophisticated data analysis using simple syntax and structure. This unit demonstrates how to use the MySQL package to build and interact with a relational database.
Modeling Biology Using Relational Databases
Authors: Peitzsch Robert M. 
Published: June 2004

AbstractThere are several different methodologies that can be used for designing a database schema; no one is the best for all occasions. This unit demonstrates two different techniques for designing relational tables and discusses when each should be used. These two techniques presented are (1) traditional Entity‐Relationship (E‐R) modeling and (2) a hybrid method that combines aspects of data warehousing and E‐R modeling. The method of choice depends on (1) how well the information and all its inherent relationships are understood, (2) what types of questions will be asked, (3) how many different types of data will be included, and (4) how much data exists.
Viewing and Editing Assembled Sequences Using Consed
Authors: Gordon David 
Published: June 2004

AbstractConsed is a program for viewing and editing assemblies prepared by the Phrap assembly program. This unit presents a general introduction to Consed. It also provides instructions for identifying problem regions and searching in Consed. Additional protocols describe creating tags, picking primers, correcting joins and adding and removing reads. Instructions are also given for the use of Assembly View, predicting the results of a restriction digestion, the use of Consed in conjunction with PolyPhred, and using Autofinish, as well as a procedure for running Phred and Phrap.
X Window Survival Guide
Authors: Stein Lincoln D. 
Published: June 2004

AbstractLogging‐in to a Unix system from a console typically initiates a graphical desktop environment that is similar to the Microsoft Windows and Apple Macintosh desktops. Logging‐in remotely to a Unix system, however, typically limits the user to a small text‐only window, which is unable to launch graphical applications. This appendix describes the two main options for overcoming this obstacle: a Virtual Network Computer (VNC) and the X Window System.
Optimization algorithms for bilinear model–based predictive control problems
Authors: Bloemen H. H. J. , van den Boom T. J. J. , Verbruggen H. B. 
Published: June 2004

AbstractModel‐based predictive control (MPC) for discrete‐time bilinear state–space models is considered. The optimization problem of the bilinear MPC algorithm is nonlinear in general. It is demonstrated that the structural properties of the bilinear state–space model provide a way to formulate the nonlinear optimization problem as a sequence of quadratic programming problems that exactly represent the original objective function. The proposed optimization algorithm is compared to one that is based on a linearization about an input trajectory. To benefit from the advantages of both algorithms, a hybrid algorithm is proposed, which outperforms the other two in most cases. The applicability of the proposed bilinear MPC algorithm is demonstrated on a polymerization process. 2004 American Institute of Chemical Engineers AIChE J, 50:1453–1461, 2004