Gregory Butler

OrfPredictor: predicting protein-coding regions in EST-derived sequences

OrfPredictor is a web server designed for identifying protein-coding regions in expressed sequence tag (EST)-derived sequences. For query sequences with a hit in BLASTX, the program predicts the coding regions based on the translation reading frames identified in BLASTX alignments, otherwise, it predicts the most probable coding region based on the intrinsic signals of the query sequences. The output is the predicted peptide sequences in the FASTA format, and a definition line that includes the query ID, the translation reading frame and the nucleotide positions where the coding region begins and ends. OrfPredictor facilitates the annotation of EST-derived sequences, particularly, for large-scale EST projects. OrfPredictor is available at .

Comparative genomic analysis of the thermophilic biomass-degrading fungi Myceliophthora thermophila and Thielavia terrestris

Thermostable enzymes and thermophilic cell factories may afford economic advantages in the production of many chemicals and biomass-based fuels. Here we describe and compare the genomes of two thermophilic fungi, Myceliophthora thermophila and Thielavia terrestris. To our knowledge, these genomes are the first described for thermophilic eukaryotes and the first complete telomere-to-telomere genomes for filamentous fungi. Genome analyses and experimental data suggest that both thermophiles are capable of hydrolyzing all major polysaccharides found in biomass. Examination of transcriptome data and secreted proteins suggests that the two fungi use shared approaches in the hydrolysis of cellulose and xylan but distinct mechanisms in pectin degradation. Characterization of the biomass-hydrolyzing activity of recombinant enzymes suggests that these organisms are highly efficient in biomass decomposition at both moderate and high temperatures. Furthermore, we present evidence suggesting that aside from representing a potential reservoir of thermostable enzymes, thermophilic fungi are amenable to manipulation using classical and molecular genetics.

TargetIdentifier: a webserver for identifying full-length cDNAs from EST sequences

TargetIdentifier is a webserver that identifies full-length cDNA sequences from the expressed sequence tag (EST)-derived contig and singleton data. To accomplish this TargetIdentifier uses BLASTX alignments as a guide to locate protein coding regions and potential start and stop codons. This information is then used to determine whether the EST-derived sequences include their translation start codons. The algorithm also uses the BLASTX output to assign putative functions to the query sequences. The server is available at .

Computing in permutation and matrix groups. II. Backtrack algorithm

This is the second paper in a series which discusses computation in permutation and matrix groups of very large order. The essential aspects of a backtrack algorithm which searches these groups are presented. We then uniformly describe algorithms for computing centralizers, intersections, and set stabilizers, as well as an algorithm which determines whether two elements are conjugate.

A framework for framework documentation

Frameworks are quite di cult to understand when one rst uses them: the design is very abstract, to factor out commonality; the design is incomplete, requiring additional subclasses to create an application; the design provides exibility for several hotspots, not all of which are needed in the application at hand; and the collaborations and the resulting dependencies between classes can be indirect and obscure. Many approaches to documenting frameworks have been tried, though with di erent aims and audiences in mind. In this paper, we present a task-oriented framework for framework documentation. Our framework is based on rst identifying a set of framework (re)use cases, which we then decompose into a set of elementary engineering tasks. Each such task requires a set of documentation primitives, enabling us to specify a minimal set of documentation primitives for each framework usage scenario. We study some major framework documentation approaches in light of this framework, identifying possible de ciences and highlighting directions for further research.

Computing in permutation and matrix groups. I. Normal closure, commutator subgroups, series

This paper is the first in a series which discusses computation in permutation and matrix groups of very large order. The fundamental concepts are defined, and some algorithms which perform elementary operations are presented. Algorithms to compute normal closures, commutator subgroups, derived series, lower central series, and upper central series are presented.

A General backtrack algorithm for the isomorphism problem of combinatorial objects

Our aim is to present a practical algorithm for the isomorphism problem that can be easily adapted to any class of combinatorial objects. We investigate the underlying principles of backtrack algorithms that determine a canonical representative of a combinatorial object. We identify the parts of the algorithm that are dependent on the class of combinatorial objects and those parts that are independent of the class. An interface between the two parts is developed to provide a general backtrack algorithm for the isomorphism problem of combinatorial objects that incorporates the technique of branch-and-bound, and that also uses the automorphisms of the combinatorial object to prune the search tree. Our general algorithm incorporates from computational group theory an algorithm known as the base change algorithm. The base change algorithm allows one to recover as much information as possible about the automorphism group when a new branch of the search tree is processed. Thus, it can lead to greater pruning of the search tree. This work is intended to lead to a better understanding of the practical isomorphism algorithms. It is not intended as a contribution to the theoretical study of the complexity of the isomorphism problem.

Retrieving information from data flow diagrams

For reverse engineering, we need tools that can extract information from documents written before routine digital storage was feasible. Documents contain both text and diagrams; data flow diagrams play a prominent role in software documents. Using current techniques, it is possible to recover the information in a data flow diagram by scanning the printed document and processing the data obtained. Feature extraction and syntax analysis enable the construction of a validated, formalized model of the data flow diagram. Understanding, however, requires a semantic interpretation. We describe a semantic model for data flow diagrams using general techniques that can be applied to other kinds of diagram. We argue that a semantic model is an essential component of a document understanding system; without a semantic model, it is difficult or impossible to extract useful information from a document.

Use case refactoring: a tool and a case study

Use case models are widely used for requirements engineering to capture functional and nonfunctional requirements, guide scenario-based design and validation, and to manage projects. Our tool for use case development and evolution supports reorganization (refactoring) of use case models as well as the extension of use case models to include new functional and nonfunctional requirements. The tool is based on a three-level metamodel covering the environment or context of a use case model, the structure of use cases, and the event or message-passing details of a scenario. In this paper we describe the tool that we have developed, and demonstrate its application to a case study for bank teller machines (ATMs). We show that the concept of refactoring can be applied to use case models as an aid to their development and evolution. We are now working on a firm semantic foundation for use cases in order to verify the behaviour-preserving property of individual refactorings.

SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

BackgroundLocating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them.ResultsSnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data.ConclusionsSnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/.