bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of a compartmentalized shotgun assembler for the human

genome
Authors: Huson Daniel H. , Reinert Knut , Kravitz Saul A. , Remington Karin A. , Delcher Art L. , Dew Ian M. , Flanigan Mike , Halpern Aaron L. , Lai Zhongwu , Mobarry Clark M. , Sutton Granger G. , Myers Eugene W. 
Published: May 2011

Abstract
Two different strategies for determining the human genome are currently being pursued: one is the “clone-by-clone” approach, employed by the publicly funded project, and the other is the “whole genome shotgun assembler” approach, favored by researchers at Celera Genomics. An interim strategy employed at Celera, called compartmentalized shotgun assembly, makes use of preliminary data produced by both approaches. In this paper we describe the design, implementation and operation of the “compartmentalized shotgun assembler”.
Contact: Knut.Reinert@celera.com
Designing fast converging phylogenetic methods
Authors: Nakhleh Luay , Roshan Usman , St. John Katherine , Sun Jerry , Warnow Tandy 
Published: May 2011

Abstract
Absolute fast converging phylogenetic reconstruction methods are provably guaranteed to recover the true tree with high probability from sequences that grow only polynomially in the number of leaves, once the edge lengths are bounded arbitrarily from above and below. Only a few methods have been determined to be absolute fast converging; these have all been developed in just the last few years, and most are polynomial time. In this paper, we compare pre-existing fast converging methods as well as some new polynomial time methods that we have developed. Our study, based upon simulating evolution under a wide range of model conditions, establishes that our new methods outperform both neighbor joining and the previous fast converging methods, returning very accurate large trees, when these other methods do poorly.
Contact: usman@cs.utexas.edu
An algorithm for finding signals of unknown length in DNA
sequences
Authors: Pavesi Giulio , Mauri Giancarlo , Pesole Graziano 
Published: May 2011

Abstract
Pattern discovery in unaligned DNA sequences is a challenging problem in both computer science and molecular biology. Several different methods and techniques have been proposed so far, but in most of the cases signals in DNA sequences are very complicated and avoid detection. Exact exhaustive methods can solve the problem only for short signals with a limited number of mutations. In this work, we extend exhaustive enumeration also to longer patterns. More in detail, the basic version of algorithm presented in this paper, given as input a set of sequences and an error ratio ε &lt; 1, finds all patterns that occur in at least q sequences of the set with at most εm mutations, where m is the length of the pattern. The only restriction is imposed on the location of mutations along the signal. That is, a valid occurrence of a pattern can present at most ⌈εi⌉ mismatches in the first i nucleotides, and so on. However, we show how the algorithm can be used also when no assumption can be made on the position of mutations. In this case, it is also possible to have an estimate of the probability of finding a signal according to the signal length, the error ratio, and the input parameters. Finally, we discuss some significance measures that can be used to sort the patterns output by the algorithm.
Contact: pavesi@disco.unimib.it
Inferring subnetworks from perturbed expression profiles
Authors: Pe’er Dana , Regev Aviv , Elidan Gal , Friedman Nir 
Published: May 2011

Abstract
Genome-wide expression profiles of genetic mutants provide a wide variety of measurements of cellular responses to perturbations. Typical analysis of such data identifies genes affected by perturbation and uses clustering to group genes of similar function. In this paper we discover a finer structure of interactions between genes, such as causality, mediation, activation, and inhibition by using a Bayesian network framework. We extend this framework to correctly handle perturbations, and to identify significant subnetworks of interacting genes. We apply this method to expression data of S. cerevisiae mutants and uncover a variety of structured metabolic, signaling and regulatory pathways.
Contact: danab@cs.huji.ac.il
An insight into domain combinations
Authors: Apic Gordana , Gough Julian , Teichmann Sarah A. 
Published: May 2011

Abstract
Domains are the building blocks of all globular proteins, and are units of compact three-dimensional structure as well as evolutionary units. There is a limited repertoire of domain families, so that these domain families are duplicated and combined in different ways to form the set of proteins in a genome. Proteins are gene products. The processes that produce new genes are duplication and recombination as well as gene fusion and fission. We attempt to gain an overview of these processes by studying the structural domains in the proteins of seven genomes from the three kingdoms of life: Eubacteria, Archaea and Eukaryota. We use here the domain and superfamily definitions in Structural Classification of Proteins Database (SCOP) in order to map pairs of adjacent domains in genome sequences in terms of their superfamily combinations. We find 624 out of the 764 superfamilies in SCOP in these genomes, and the 624 families occur in 585 pairwise combinations. Most families are observed in combination with one or two other families, while a few families are very versatile in their combinatorial behaviour. This type of pattern can be described by a scale-free network. Finally, we study domain repeats and we compare the set of the domain combinations in the genomes to those in PDB, and discuss the implications for structural genomics.
Contact: apic@mrc-lmb.cam.ac.uk
The metric space of proteins—comparative study of clustering algorithms
Authors: Sasson Ori , Linial Nathan , Linial Michal 
Published: May 2011

Abstract
Motivation: A large fraction of biological research concentrates on individual proteins and on small families of proteins. One of the current major challenges in bioinformatics is to extend our knowledge to very large sets of proteins. Several major projects have tackled this problem. Such undertakings usually start with a process that clusters all known proteins or large subsets of this space. Some work in this area is carried out automatically, while other attempts incorporate expert advice and annotation.
Results: We propose a novel technique that automatically clusters protein sequences. We consider all proteins in SWISSPROT, and carry out an all-against-all BLAST similarity test among them. With this similarity measure in hand we proceed to perform a continuous bottom-up clustering process by applying alternative rules for merging clusters. The outcome of this clustering process is a classification of the input proteins into a hierarchy of clusters of varying degrees of granularity. Here we compare the clusters that result from alternative merging rules, and validate the results against InterPro.
Our preliminary results show that clusters that are consistent with several rather than a single merging rule tend to comply with InterPro annotation. This is an affirmation of the view that the protein space consists of families that differ markedly in their evolutionary conservation.
Availability: The outcome of these investigations can be viewed in an interactive Web site at http://www.protonet.cs.huji.ac.il
Supplementary information: Biological examples for comparing the performance of the different algorithms used for classification are presented in http://www.protonet.cs.huji.ac.il/examples.html
Contact: ori@cs.huji.ac.il
Keywords: protein families; protein classification; sequence alignment; clustering.
Evaluating machine learning approaches for aiding probe
selection for gene-expression arrays
Authors: Tobler J. B. , Molla M.N. , Nuwaysir E.F. , Green R.D. , Shavlik J.W. 
Published: May 2011

Abstract
Motivation: Microarrays are a fast and cost-effective method of performing thousands of DNA hybridization experiments simultaneously. DNA probes are typically used to measure the expression level of specific genes. Because probes greatly vary in the quality of their hybridizations, choosing good probes is a difficult task. If one could accurately choose probes that are likely to hybridize well, then fewer probes would be needed to represent each gene in a gene-expression microarray, and, hence, more genes could be placed on an array of a given physical size. Our goal is to empirically evaluate how successfully three standard machine-learning algorithms—naïve Bayes, decision trees, and artificial neural networks—can be applied to the task of predicting good probes. Fortunately it is relatively easy to get training examples for such a learning task: place various probes on a gene chip, add a sample where the corresponding genes are highly expressed, and then record how well each probe measures the presence of its corresponding gene. With such training examples, it is possible that an accurate predictor of probe quality can be learned.
Results: Two of the learning algorithms we investigate—naïve Bayes and neural networks—learn to predict probe quality surprisingly well. For example, in the top ten predicted probes for a given gene not used for training, on average about five rank in the top 2.5% of that gene's hundreds of possible probes. Decision-tree induction and the simple approach of using predicted melting temperature to rank probes perform significantly worse than these two algorithms. The features we use to represent probes are very easily computed and the time taken to score each candidate probe after training is minor. Training the naïve Bayes algorithm takes very little time, and while it takes over 10 times as long to train a neural network, that time is still not very substantial (on the order of a few hours on a desktop workstation). We also report the information contained in the features we use to describe the probes. We find the fraction of cytosine in the probe to be the most informative feature. We also find, not surprisingly, that the nucleotides in the middle of the probes sequence are more informative than those at the ends of the sequence.
Contact: molla@cs.wisc.edu
Keywords: microarrays; probe selection; artificial neural networks; decision trees; naïve Bayes.
*To whom correspondence should be addressed.
Multiple genome rearrangement: a general approach via the evolutionary genome graph
Authors: Korkin Dmitry , Goldfarb Lev 
Published: May 2011

Abstract
Motivation: In spite of a well-known fact that genome rearrangements are supposed to be viewed in the light of the evolutionary relationships within and between the species involved, no formal underlying framework based on the evolutionary considerations for treating the questions arising in the area has been proposed. If such an underlying framework is provided, all the basic questions in the area can be posed in a biologically more appropriate and useful form: e.g., the similarity between two genomes can then be computed via the nearest ancestor, rather than ‘directly’, ignoring the evolutionary connections.
Results: We outline an evolution-based general framework for answering questions related to the multiple genome rearrangement. In the proposed model, the evolutionary genome graph (EG-graph) encapsulates an evolutionary history of a genome family. For a set of all EG-graphs, we introduce a family of similarity measures, each defined via a fixed set of genome transformations. Given a set of genomes and restricting ourselves to the transpositions, an algorithm for constructing an EG-graph is presented. We also present the experimental results in the form of an EG-graph for a set of concrete genomes (for several species). This EG-graph turns out to be very close to the corresponding known phylogenetic tree.
Contact: dkorkin@unb.ca
Keywords: genome rearrangement; evolutionary model; gene transformations; longest common subsequence; ETS framework.
*To whom correspondence should be addressed.
Mining viral protease data to extract cleavage knowledge
Authors: Narayanan Ajit , Wu Xikun , Yang Z. Rong 
Published: May 2011

Abstract
Motivation: The motivation is to identify, through machine learning techniques, specific patterns in HIV and HCV viral polyprotein amino acid residues where viral protease cleaves the polyprotein as it leaves the ribosome. An understanding of viral protease specificity may help the development of future anti-viral drugs involving protease inhibitors by identifying specific features of protease activity for further experimental investigation. While viral sequence information is growing at a fast rate, there is still comparatively little understanding of how viral polyproteins are cut into their functional unit lengths. The aim of the work reported here is to investigate whether it is possible to generalise from known cleavage sites to unknown cleavage sites for two specific viruses—HIV and HCV. An understanding of proteolytic activity for specific viruses will contribute to our understanding of viral protease function in general, thereby leading to a greater understanding of protease families and their substrate characteristics.
Results: Our results show that artificial neural networks and symbolic learning techniques (See5) capture some fundamental and new substrate attributes, but neural networks outperform their symbolic counterpart.
Availability: Publicly available software was used (Stuttgart Neural Network Simulator—http://www-ra.informatik.uni-tuebingen.de/SNNS/ and See5—http://www.rulequest.com. The datasets used (HIV, HCV) for See5 are available at: http://www.dcs.ex.ac.uk/~anarayan/bioinf/ismbdatasets/
Keywords: protase cleavage; protease inhibitors; machine learning; neural networks; decision trees.
Contact: a.narayanan@ex.ac.uk; wuxikun@yahoo.com; z.r.yang@ex.ac.uk
Support vector regression applied to the determination of
the developmental age of a Drosophila embryo from its
segmentation gene expression patterns
Authors: Myasnikova E. , Samsonova A. , Samsonova M. , Reinitz J. 
Published: May 2011

Abstract
Motivation: In this paper we address the problem of the determination of developmental age of an embryo from its segmentation gene expression patterns in Drosophila.
Results: By applying support vector regression we have developed a fast method for automated staging of an embryo on the basis of its gene expression pattern. Support vector regression is a statistical method for creating regression functions of arbitrary type from a set of training data. The training set is composed of embryos for which the precise developmental age was determined by measuring the degree of membrane invagination. Testing the quality of regression on the training set showed good prediction accuracy. The optimal regression function was then used for the prediction of the gene expression based age of embryos in which the precise age has not been measured by membrane morphology. Moreover, we show that the same accuracy of prediction can be achieved when the dimensionality of the feature vector was reduced by applying factor analysis. The data reduction allowed us to avoid over-fitting and to increase the efficiency of the algorithm.
Availability: This software may be obtained from the authors.
Contact: samson@fn.csa.ru
Keywords: gene expression patterns; development; embryo staging; support vector regression; segmentation genes; Drosophila.
*To whom correspondence should be addressed.
Estonian Genome Project—before the take-off and
take-off
Authors: Metspalu Andres 
Published: May 2011

Abstract
I will give an overview of the preparations of the EGP including all aspects of the project and current satus of the pilot phase, describe the informatics part of the project together with other aspects (legal, ethical, logistics, financial etc.) and where we stand in terms of population genetics and genotyping technology in order to start using the sample collection for case—control studies for revealing the genetic component of the common diseases.
Proteome analysis based on motif statistics
Authors: Nicodème P. , Doerks T. , Vingron M. 
Published: May 2011

Abstract
Motivation: Even for the amino acid motifs collected in the Prosite database there may be chance occurences as opposed to those occurences where the motif is involved in fold or function of a protein. With recent mathematical advances in assessing the significance of observing such a motif a particular number of times, we can now study the over- or under-representation of particular motifs in a complete genome and attempt to make functional deductions.
Results: We demonstrate that statistical over- or under-representation of motifs in complete proteomes may be an indicator of whether, in that organism, we are looking at chance occurrences of the motif or whether the occurrences are sufficiently numerous to suggest a systematic, and thus functionally important occurrence. This has important implications on databank annotations.
Availability: The complete dataset comprising the plotted statistics of 266 Prosite motifs on 42 proteomes is available at http://algo.inria.fr/nicodeme/proteomes/proteocomp.html. The software used to compute this data has been described by Nicodème (2000, 2001). They are available either by web access as mentioned in these articles or by direct request from Pierre Nicodème.
Contact: nicodeme@genopole.cnrs.fr
Correlating gene promoters and expression in gene disruption experiments
Authors: Palin Kimmo , Ukkonen Esko , Brazma Alvis , Vilo Jaak 
Published: May 2011

Abstract
Motivation: Finding putative transcription factor binding sites in the upstream sequences of similarly expressed genes has recently become a subject of intensive studies. In this paper we investigate how much gene expression regulation can be attributed to the presence of various binding sites in the gene promoters by correlating the binding sites and the changes in gene expression resulting from gene disruptions (e.g. knockouts).
Results: We have developed a data analysis method for comparing mRNA measurements of gene disruption experiments with information about gene promoters. The method was applied to a well–known dataset to uncover correlations between known transcription factor binding site motifs in the upstream regions of all S. cerevisiae genes and the gene expression changes in various gene disruption experiments. The possible explanations of the correlations were categorized and analyzed using e.g. expression cascades. Several correlations turned out to be consistent with existing biological knowledge while some new ones suggest themselves for further study.
Availability: The resulting tables are available at http://www.cs.helsinki.fi/u/kpalin/CorrDisrupt/.
Contact: kimmo.palin@cs.helsinki.fi
Building and analysing genome-wide gene disruption networks
Authors: Rung J. , Schlitt T. , Brazma A. , Freivalds K. , Vilo J. 
Published: May 2011

Abstract
Motivation: Microarray experiments comparing expression levels of all genes in yeast for hundreds of mutants allow us to examine properties of gene regulatory networks on a genomic scale. We can investigate questions such as network modularity, connectivity, and look for genes with particular roles in the network structure.
Results: We have built genome-wide disruption networks for yeast, using a representation of gene expression data as directed labelled graphs. Nodes represent genes and arcs connect nodes if the disruption of the source gene significantly alters the expression of the target gene. We are interested in features of the resulting disruption networks that are robust over a range of significance cutoffs. The networks show a significant overlap with analogous networks constructed from scientific literature. In disruption networks the number of arcs adjacent to different nodes are distributed roughly according to a power-law, like in many complex systems where the robustness against perturbations is important. The networks are dominated by a single large component and do not have an obvious modular structure. Genes with the highest outdegrees often encode proteins with regulatory functions, whereas genes with the highest indegrees are predominantly involved in metabolism. The local structure of the networks is meaningful, genes involved in the same cellular processes are close together in the network.
Keywords: gene networks, microarrays, yeast, graph visualisation
Availability: http://www.ebi.ac.uk/microarray/networks
Contact: schlitt@ebi.ac.ukjohan@ebi.ac.uk
These authors contributed equally to this paper.
BioMiner—modeling, analyzing, and visualizing biochemical pathways and networks
Authors: Šırava M. , Schäfer T. , Eiglsperger M. , Kaufmann M. , Kohlbacher O. , Bornberg-Bauer E. , Lenhof H. P. 
Published: May 2011

Abstract
Motivation: Understanding the biochemistry of a newly sequenced organism is an essential task for post-genomic analysis. Since, however, genome and array data grow much faster than biochemical information, it is necessary to infer reactions by comparative analysis. No integrated and easy to use software tool for this purpose exists as yet.
Results: We present a new software system—BioMiner—for analyzing and visualizing biochemical pathways and networks. BioMiner is based on a new comprehensive, extensible and reusable data model—BioCore—which can be used to model biochemical pathways and networks. As a first application we present PathFinder, a new tool predicting biochemical pathways by comparing groups of related organisms based on sequence similarity. We successfully tested PathFinder with a number of experiments, e.g. the well studied glycolysis in bacteria. Additionally, an application called PathViewer for the visualization of metabolic networks is presented. PathViewer is the first application we are aware of which supports the graphical comparison of metabolic networks of different organisms.
Availability: http://www.zbi.uni-saarland.de/chair/projects/BioMiner
Contact: schaefer@bioinf.uni-sb.de
Supplementary Information: Additional information on experimental results can be found on our web site.
Keywords: Biochemical data model, metabolic and regulatory pathways, visualization, Java, XML.
Principles of protein-protein interactions
Authors: Teichmann Sarah A. 
Published: May 2011

Abstract
In the postgenomic era, one of the most interesting and important challenges is to understand protein interactions on a large scale. The physical interactions between protein domains are fundamental to the workings of a cell: in multi-domain polypeptide chains, in multi-subunit proteins and in transient complexes between proteins that also exist independently. Thus experimental investigation of protein-protein interactions has been extensive, including recent large-scale screens using mass spectrometry. The role of computational research on protein-protein interactions encompasses not only prediction, but also understanding the nature of the interactions and their three-dimensional structures. I will discuss properties such as sequence conservation and co-regulation of genes and proteins involved in different types of physical interactions. Given that all proteins consist of their evolutionary units, the domains, all interactions occur between these domains. The interactions between domains belonging to different protein families will be the second topic of my talk.