bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exact genetic linkage computations for general pedigrees
Authors: Fishelson M. , Geiger D. 
Published: May 2011

Abstract
Motivation: Genetic linkage analysis is a useful statistical tool for mapping disease genes and for associating functionality of genes with their location on the chromosome. There is a need for a program that computes multipoint likelihood on general pedigrees with many markers that also deals with two-locus disease models.
Results: In this paper we present algorithms for performing exact multipoint likelihood calculations on general pedigrees with a large number of highly polymorphic markers, taking into account a variety of disease models. We have implemented these algorithms in a new computer program called superlink which outperforms leading linkage software with regards to functionality, speed, memory requirements and extensibility.
Availability: superlink is available at http://bioinfo.cs.technion.ac.il/superlink
Contact: fmaayan@cs.technion.ac.ildang@cs.technion.ac.il
Keywords: Bayesian networks; Fastlink; Genehunter; linkage analysis; Vitesse.
Representing genetic sequence data for pharmacogenomics: an
evolutionary approach using ontological and relational models
Authors: Rubin Daniel L. , Shafa Farhad , Oliver Diane E. , Hewett Micheal , Altman Russ B. 
Published: May 2011

Abstract
Motivation: The information model chosen to store biological data affects the types of queries possible, database performance, and difficulty in updating that information model. Genetic sequence data for pharmacogenetics studies can be complex, and the best information model to use may change over time. As experimental and analytical methods change, and as biological knowledge advances, the data storage requirements and types of queries needed may also change.
Results: We developed a model for genetic sequence and polymorphism data, and used XML Schema to specify the elements and attributes required for this model. We implemented this model as an ontology in a frame-based representation and as a relational model in a database system. We collected genetic data from two pharmacogenetics resequencing studies, and formulated queries useful for analysing these data. We compared the ontology and relational models in terms of query complexity, performance, and difficulty in changing the information model. Our results demonstrate benefits of evolving the schema for storing pharmacogenetics data: ontologies perform well in early design stages as the information model changes rapidly and simplify query formulation, while relational models offer improved query speed once the information model and types of queries needed stabilize.
Availability: Our ontology and relational models are available at http://smi-web.stanford.edu/projects/helix/pubs/ismb02/.
Contact: rubin@smi.stanford.eduruss.altman@stanford.eduhelp@pharmgkb.org
Keywords: ontologies; relational databases; schema; data models; pharmacogenomics.
Evaluating functional network inference using simulations of complex
biological systems
Authors: Smith V. Anne , Jarvis Erich D. , Hartemink Alexander J. 
Published: May 2011

Abstract
Motivation: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data.
Results: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system—including the irrelevance of unregulated variables—from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.
Availability: Source code and simulated data are available upon request.
Contact: amink@cs.duke.eduasmith@neuro.duke.edujarvis@neuro.duke.edu
DNA sequence and structure: direct and indirect recognition in
protein-DNA binding
Authors: Steffen N.R. , Murphy S.D. , Tolleri L. , Hatfield G.W. , Lathrop R.H. 
Published: May 2011

Abstract
Motivation: Direct recognition, or direct readout, of DNA bases by a DNA-binding protein involves amino acids that interact directly with features specific to each base. Experimental evidence also shows that in many cases the protein achieves partial sequence specificity by indirect recognition, i.e., by recognizing structural properties of the DNA. (1) Could threading a DNA sequence onto a crystal structure of bound DNA help explain the indirect recognition component of sequence specificity? (2) Might the resulting pure-structure computational motif manifest itself in familiar sequence-based computational motifs?
Results: The starting structure motif was a crystal structure of DNA bound to the integration host factor protein (IHF) of E. coli. IHF is known to exhibit both direct and indirect recognition of its binding sites. (1) Threading DNA sequences onto the crystal structure showed statistically significant partial separation of 60 IHF binding sites from random and intragenic sequences and was positively correlated with binding affinity. (2) The crystal structure was shown to be equivalent to a linear Markov network, and so, to a joint probability distribution over sequences, computable in linear time. It was transformed algorithmically into several common pure-sequence representations, including (a) small sets of short exact strings, (b) weight matrices, (c) consensus regular patterns, (d) multiple sequence alignments, and (e) phylogenetic trees. In all cases the pure-sequence motifs retained statistically significant partial separation of the IHF binding sites from random and intragenic sequences. Most exhibited positive correlation with binding affinity. The multiple alignment showed some conserved columns, and the phylogenetic tree partially mixed low-energy sequences with IHF binding sites but separated high-energy sequences. The conclusion is that deformation energy explains part of indirect recognition, which explains part of IHF sequence-specific binding.
Availability: Code and data on request.
Contact:Nick Steffen for code and Lorenzo Tolleri for data. nsteffen@uci.eduLorenzo_Tolleri@chiron.it
Keywords: protein-DNA binding sites; sequence motifs or patterns; indirect recognition or readout; integration host factor; IHF.
The Pathway Tools software
Authors: Karp Peter D. , Paley Suzanne , Romero Pedro 
Published: May 2011

Abstract
Motivation: Bioinformatics requires reusable software tools for creating model-organism databases (MODs).
Results: The Pathway Tools is a reusable, production-quality software environment for creating a type of MOD called a Pathway/Genome Database (PGDB). A PGDB such as EcoCyc (see http://ecocyc.org) integrates our evolving understanding of the genes, proteins, metabolic network, and genetic network of an organism. This paper provides an overview of the four main components of the Pathway Tools: The PathoLogic component supports creation of new PGDBs from the annotated genome of an organism. The Pathway/Genome Navigator provides query, visualization, and Web-publishing services for PGDBs. The Pathway/Genome Editors support interactive updating of PGDBs. The Pathway Tools ontology defines the schema of PGDBs. The Pathway Tools makes use of the Ocelot object database system for data management services for PGDBs. The Pathway Tools has been used to build PGDBs for 13 organisms within SRI and by external users.
Availability: The software is freely available to academics and is available for a fee to commercial institutions. Contact ptools-support@ai.sri.com for information on obtaining the software.
Contact: pkarp@ai.sri.com
Keywords: Bioinformatics; model organism database; genome analyses; metabolic pathways.
*To whom correspondence should be addressed.
Modelling regulatory pathways in E. coli from time
series expression profiles
Authors: Ong Irene M. , Glasner Jeremy D. , Page David 
Published: May 2011

Abstract
Motivation: Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data.
Results: We describe an approach that naturally handles time series data with the capabilities of modelling causality, feedback loops, and environmental or hidden variables using a Dynamic Bayesian network. We also present a novel way of combining prior biological knowledge and current observations to improve the quality of analysis and to model interactions between sets of genes rather than individual genes. Our approach is evaluated on time series expression data measured in response to physiological changes that affect tryptophan metabolism in E. coli. Results indicate that this approach is capable of finding correlations between sets of related genes.
Contact: ong@cs.wisc.edu
Keywords: Dynamic Bayesian networks; regulatory pathways; time series gene expression; operon model.
A tree kernel to analyse phylogenetic profiles
Authors: Vert Jean-Philippe 
Published: May 2011

Abstract
Motivation: The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every fully sequenced genome. Because proteins that participate in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion, the phylogenetic profiles of such proteins are often ‘similar’ or at least ‘related’ to each other. The question we address in this paper is the following: how to measure the ‘similarity’ between two profiles, in an evolutionarily relevant way, in order to develop efficient function prediction methods?
Results: We show how the profiles can be mapped to a high-dimensional vector space which incorporates evolutionarily relevant information, and we provide an algorithm to compute efficiently the inner product in that space, which we call the tree kernel. The tree kernel can be used by any kernel-based analysis method for classification or data mining of phylogenetic profiles.
As an application a Support Vector Machine (SVM) trained to predict the functional class of a gene from its phylogenetic profile is shown to perform better with the tree kernel than with a naive kernel that does not include any information about the phylogenetic relationships among species. Moreover a kernel principal component analysis (KPCA) of the phylogenetic profiles illustrates the sensitivity of the tree kernel to evolutionarily relevant variations.
Availability: All data and software used are freely and publicly available upon request.
Contact: Jean-Philippe.Vert@mines.org
Keywords: phylogenetic profile; tree; kernel; support vector machine; gene function prediction.
Beyond tandem repeats: complex pattern structures and
distant regions of similarity
Authors: Hauth Amy M. , Joseph Deborah A. 
Published: May 2011

Abstract
Motivation: Tandem repeats (TRs) are associated with human disease, play a role in evolution and are important in regulatory processes. Despite their importance, locating and characterizing these patterns within anonymous DNA sequences remains a challenge. In part, the difficulty is due to imperfect conservation of patterns and complex pattern structures. We study recognition algorithms for two complex pattern structures: variable length tandem repeats (VLTRs) and multi-period tandem repeats (MPTRs).
Results: We extend previous algorithmic research to a class of regular tandem repeats (RegTRs). We formally define RegTRs, as well as two important subclasses: VLTRs and MPTRs. We present algorithms for identification of TRs in these classes. Furthermore, our algorithms identify degenerate VLTRs and MPTRs: repeats containing substitutions, insertions and deletions. To illustrate our work, we present results of our analysis for two difficult regions in cattle and human data which reflect practical occurrences of these subclasses in GenBank sequence data.
In addition, we show the applicability of our algorithmic techniques for identifying Alu sequences, gene clusters and other distant regions of similarity. We illustrate this with an example from yeast chromosome I.
Availability: Algorithms can be accessed at http://www.cs.wisc.edu/areas/theory
Contact: kryder@cs.wisc.edujoseph@cs.wisc.edu
*To whom correspondence should be addressed.
Fully automated ab initio protein
structure prediction using I-SITES, HMMSTR and ROSETTA
Authors: Bystroff Christopher , Shao Yu 
Published: May 2011

Abstract
Motivation: The Monte Carlo fragment insertion method for protein tertiary structure prediction (ROSETTA) of Baker and others, has been merged with the I-SITES library of sequence structure motifs and the HMMSTR model for local structure in proteins, to form a new public server for the ab initio prediction of protein structure. The server performs several tasks in addition to tertiary structure prediction, including a database search, amino acid profile generation, fragment structure prediction, and backbone angle and secondary structure prediction. Meeting reasonable service goals required improvements in the efficiency, in particular for the ROSETTA algorithm.
Results: The new server was used for blind predictions of 40 protein sequences as part of the CASP4 blind structure prediction experiment. The results for 31 of those predictions are presented here. 61% of the residues overall were found in topologically correct predictions, which are defined as fragments of 30 residues or more with a root-mean-square deviation in superimposed alpha carbons of less than 6Å. HMMSTR 3-state secondary structure predictions were 73% correct overall. Tertiary structure predictions did not improve the accuracy of secondary structure prediction.
Availability:The server is accessible through the web at http://isites.bio.rpi.edu/hmmstr/index.html Programs are available upon requests for academics. Licensing agreements are available for commercial interests.
Supplementary information: http://isites.bio.rpi.eduhttp://predictioncenter.llnl.gov/casp4/
Contact: bystrc@rpi.edushaoy@rpi.edu
Keywords: CASP; CAFASP; protein folding; motifs; hidden Markov model; knowledge-based.
*To whom correspondence should be addressed.
Inferring sub-cellular localization through
automated lexical analysis
Authors: Nair Rajesh , Rost Burkhard 
Published: May 2011

Abstract
Motivation: The SWISS-PROT sequence database contains keywords of functional annotations for many proteins. In contrast, information about the sub-cellular localization is available for only a few proteins. Experts can often infer localization from keywords describing protein function. We developed LOCkey, a fully automated method for lexical analysis of SWISS-PROT keywords that assigns sub-cellular localization. With the rapid growth in sequence data, the biochemical characterisation of sequences has been falling behind. Our method may be a useful tool for supplementing functional information already automatically available.
Results: The method reached a level of more than 82% accuracy in a full cross-validation test. Due to a lack of functional annotations, we could infer localization for fewer than half of all proteins in SWISS-PROT. We applied LOCkey to annotate five entirely sequenced proteomes, namely Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), Drosophila melanogaster (fly), Arabidopsis thaliana (plant) and a subset of all human proteins. LOCkey found about 8000 new annotations of sub-cellular localization for these eukaryotes.
Availability: Annotations of localization for eukaryotes at: http://cubic.bioc.columbia.edu/services/LOCkey
Contact: nair@cubic.bioc.coplumbia.edurost@columbia.edu
Keywords: genome sequence analysis; predicting sub-cellular localization; protein function; lexical analysis.
*To whom correspondence should be addressed.
Identifying transcription factor binding sites through
Markov chain optimization
Authors: Ellrott Kyle , Yang Chuhu , Sladek Frances M. , Jiang Tao 
Published: May 2011

Abstract
Even though every cell in an organism contains the same genetic material, each cell does not express the same cohort of genes. Therefore, one of the major problems facing genomic research today is to determine not only which genes are differentially expressed and under what conditions, but also how the expression of those genes is regulated. The first step in determining differential gene expression is the binding of sequence-specific DNA binding proteins (i.e. transcription factors) to regulatory regions of the genes (i.e. promoters and enhancers). An important aspect to understanding how a given transcription factor functions is to know the entire gamut of binding sites and subsequently potential target genes that the factor may bind/regulate. In this study, we have developed a computer algorithm to scan genomic databases for transcription factor binding sites, based on a novel Markov chain optimization method, and used it to scan the human genome for sites that bind to hepatocyte nuclear factor 4 α (HNF4α). A list of 71 known HNF4α binding sites from the literature were used to train our Markov chain model. By looking at the window of 600 nucleotides around the transcription start site of each confirmed gene on the human genome, we identified 849 sites with varying binding potential and experimentally tested 109 of those sites for binding to HNF4α. Our results show that the program was very successful in identifying 77 new HNF4α binding sites with varying binding affinities (i.e. a 71% success rate). Therefore, this computational method for searching genomic databases for potential transcription factor binding sites is a powerful tool for investigating mechanisms of differential gene regulation.
Contact: jiang@cs.ucr.edu
Genome segmentation using piecewise constant intensity models and
reversible jump MCMC
Authors: Salmenkivi Marko , Kere Juha , Mannila Heikki 
Published: May 2011

Abstract
The existence of whole genome sequences makes it possible to search for global structure in the genome. We consider modeling the occurrence frequencies of discrete patterns (such as starting points of ORFs or other interesting phenomena) along the genome. We use piecewise constant intensity models with varying number of pieces, and show how a reversible jump Markov Chain Monte Carlo (RJMCMC) method can be used to obtain a posteriori distribution on the intensity of the patterns along the genome. We apply the method to modeling the occurrence of ORFs in the human genome. The results show that the chromosomes consist of 5–35 clearly distinct segments, and that the posteriori number and length of the segments shows significant variation. On the other hand, for the yeast genome the intensity of ORFs is nearly constant.
Contact: Marko.Salmenkivi@cs.helsinki.fiJuha.Kere@biosci.ki.seHeikki.Mannila@cs.helsinki.fi
The mutual information: Detecting and evaluating dependencies between
variables
Authors: Steuer R. , Kurths J. , Daub C. O. , Weise J. , Selbig J. 
Published: May 2011

Abstract
Motivation: Clustering co-expressed genes usually requires the definition of `distance' or `similarity' between measured datasets, the most common choices being Pearson correlation or Euclidean distance. With the size of available datasets steadily increasing, it has become feasible to consider other, more general, definitions as well. One alternative, based on information theory, is the mutual information, providing a general measure of dependencies between variables. While the use of mutual information in cluster analysis and visualization of large-scale gene expression data has been suggested previously, the earlier studies did not focus on comparing different algorithms to estimate the mutual information from finite data.
Results: Here we describe and review several approaches to estimate the mutual information from finite datasets. Our findings show that the algorithms used so far may be quite substantially improved upon. In particular when dealing with small datasets, finite sample effects and other sources of potentially misleading results have to be taken into account.
Contact: steuer@agnld.uni-potsdam.de
A new distance measure for comparing sequence profiles
based on path lengths along an entropy surface
Authors: Benson Gary 
Published: May 2011

Abstract
We describe a new distance measure for comparing DNA sequence profiles. For this measure, columns in a multiple alignment are treated as character frequency vectors (sum of the frequencies equal to one). The distance between two vectors is based on minimum path length along an entropy surface. Path length is estimated using a random graph generated on the entropy surface and Dijkstra's algorithm for all shortest paths to a source. We use the new distance measure to analyze similarities within familes of tandem repeats in the C. elegans genome and show that this new measure gives more accurate refinement of family relationships than a method based on comparing consensus sequences.
Comparative analysis of protein interaction networks
Authors: Bork Peer 
Published: May 2011

Abstract
Recent advances in proteomics and computational biology have lead to a flood of protein interaction data and resulting interaction networks (e.g. (Gavin et al., 2002)). Here I first analyse the status and quality of parts lists (genes and proteins), then comparatively assess large-scale protein interaction data (von Mering et al., 2002) and finally try to identify biological meaningful units (e.g. pathways, cellular processes) within interaction networks that are derived from the conservation of gene neighborhood (Snel et al., 2002). Possible extensions of gene neighborhood analysis to eukaryotes (von Mering and Bork, 2002) will be discussed.
Theoretical analysis of alternative splice forms using computational
methods
Authors: Boué Stéphanie , Vingron Martin , Kriventseva Evgenia , Koch Ina 
Published: May 2011

Abstract
Nowadays understanding alternative splicing is one of the greatest challenges in biology, because it is a genetic process much more important than thought at the time of its discovery. In this paper, we explain the approach of using the different available databases and software tools to start a large scale investigation of alternative splice forms. To collect information about alternative splicing we investigated known data in the databases using different computational methods. The investigations proceeded from the genomic sequence data to structural protein data. Then, we interpreted those data to find the relationship between alternative splice forms and protein function and structure. We found some interesting features of alternative splicing which are presented here. We discuss the results of one chosen example. They concern the coverage quality of the protein sequence of a known structure, an EST analysis, the validation of splice variants, the determination of the alternative splice type, and finally the link between alternative splicing and disease.
Contact: ina.koch@molgen.mpg.de